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Process planning for precision manufacturing
An approach based on methodological
studies
Mats Bagge
Doctoral Thesis 2014
KTH Royal Institute of Technology
Engineering Sciences
Department of Production Engineering
SE-100 44 Stockholm, Sweden
TRITA IIP-14-04
ISSN 1650-1888
ISBN 978-91-7595-172-0
Akademisk avhandling som med tillstånd av KTH i Stockholm framlägges till offentlig
granskning för avläggande av teknisk doktorsexamen tisdagen den 10 juni 2014 kl. 13:00 i
sal M311, KTH Industriell Produktion, Brinellvägen 68, Stockholm
Copyright © Mats Bagge 2014
Tryck: Universitetsservice US AB
III
Abstract Process planning is a task comprising a broad range of activities to
design and develop an appropriate manufacturing process for producing
a part. Interpretation of the part design, selection of manufacturing
processes, definition of operations, operation sequences, machining
datums, geometrical dimensions and tolerances are some common
activities associated with the task. Process planning is also “the link
between product design and manufacturing” with the supplementary
commission to support design of competitive products.
Process planning is of a complex and dynamic nature, often managed
by a skilled person with few, or no, explicit methods to solve the task. The
work is heuristic and the result is depending on personal experiences and
decisions. Since decades, there have been plenty of attempts to develop
systems for computer-aided process planning (CAPP). CAPP is still
awaiting its breakthrough and one reason is the gap between the
functionality of the CAPP systems and the industrial process planning
practice.
This thesis has an all-embracing aim of finding methods that cover
essential activities for process planning, including abilities to predict the
outcome of a proposed manufacturing process. This is realised by
gathering supporting methods suitable to manage both qualitative and
quantitative characterisation and analyses of a manufacturing process.
The production research community has requested systematisation
and deeper understanding of industrial process planning. This thesis
contributes with a flow chart describing the process planning process
(PPP), in consequence of the methodological studies. The flow chart
includes process planning activities and information flows between these
activities.
The research has been performed in an industrial environment for
high volume manufacturing of gear parts. Though gear manufacturing
has many distinctive features, the methods and results presented in this
thesis are generally applicable to precision manufacturing of many kinds
of mechanical parts.
Keywords
Process planning, precision manufacturing, machining, tolerance
chain analysis, process behaviour, process performance, process
capability, in-process workpiece.
V
Acknowledgements
This thesis is a result of my work as an industrial PhD student at the
Department of Production Engineering IIP, KTH Royal Institute of
Technology, employed by Scania CV AB, Södertälje. Since the start in
2006, I have been involved in several research projects where
collaborations between Swedish industry, academia and research
institutes have been a common denominator. These projects have
provided many interesting research topics related to my main field of
research – process planning – and given many fruitful opportunities to
get to know nice and proficient people.
Among these people, I would first like to express my special
appreciation and thanks to my supervisors Professor Bengt Lindberg and
Professor Sören Andersson at KTH and Ulf Bjarre at Scania CV AB for
inspiring cooperation over the years of studies. The appended
publications in this thesis have also been written in an invigorating
manner together with Professor Cornel Mihai Nicolescu, Mats Werke and
Mikael Hedlind. Thank you!
I would also like to thank my colleague and participant in the KUGG
project, Julia Lundberg Gerth for taking time and giving well advises
when writing this thesis. My appreciations to the other Ph.D. students in
the KUGG project (where it all begun); Mathias Werner, Ellen Bergseth,
Sören Sjöberg and Karin Björkeborn.
One important source for inspiration and discussions about cross-
disciplinary considerations related to production research has been
participating in the seminars lead by Associate Professor Peter Gröndahl.
Many persons have contributed to the engaging enthusiasm in the
seminar group, especially Robert Gerth, Tord Johansson, Joakim Storck,
Jens von Axelson, Richard Lindqvist and Magnus Lundgren.
I have in addition had the favour to get to know and spend time
together with many people at IIP, in front of others; Anders Berglund,
Andreas Archenti and Lorenzo Daghini. It has been a pleasure to cadge
your offices!
- Marie, Johan, Maj-Britt and Mona, let me express my sincere
gratitude for all your assistance when colloquial duties have not fit into
my business schedule!
Finally, all my love goes to Carolin, Hanna and Ellen. You are great!
Mats Bagge, Södertälje, May 2014
VII
List of publications
This thesis is based on five publications of which one is a licentiate
thesis and four are papers. The four papers are appended at the end of
this doctoral thesis.
Licentiate thesis (not appended)
Bagge, M., 2009. An approach for systematic process planning of gear
transmission parts. Licentiate thesis, Stockholm, Sweden: Royal Institute
of Technology.
Paper A
Werke, M., Bagge, M., Nicolescu, M. & Lindberg, B., 2014. Process
modelling using upstream analysis of manufacturing sequences.
Submitted to: The International Journal of Advanced Manufacturing
Technology (Under review April 2014).
Paper B
Bagge, M. & Lindberg, B., 2012. Analysis of process parameters during
press quenching of bevel gear parts. In: M. Björkman, ed. Proceedings of
The 5th International Swedish Production Symposium. Linköping:
Produktionsakademien, pp. 251-259.
Paper C
Bagge, M., Hedlind, M. & Lindberg, B., 2013. Tolerance chain design and
analysis of in-process workpiece. In: A. Archenti & A. Maffei, eds.
Proceedings of the International Conference on Advanced
Manufacturing Engineering and Technologies. Stockholm: KTH Royal
Institute of Technology, pp. 305-315.
Paper D
Bagge, M., Hedlind, M. & Lindberg, B., 2014. Process chain based
workpiece variation simulation for performance utilisation analysis.
Submitted to: Swedish Production Symposium 2014, Göteborg, Sweden.
IX
Abbreviations
AI: Artificial Intelligence
AIAG: Automotive Industry Action Group
APQP: Advanced Product Quality
CAD: Computer Aided Design
CAM: Computer Aided Manufacturing
CAPP: Computer Aided Process Planning
CoPQ: Cost of Poor Quality
DDC: Dimension Dependency Chart
DDC2: Dimension Dependency Chart 2
DOE: Design of Experiments
DPMO: Defects per Million Opportunities
FD: Final Dimension
FFI: Fordonsstrategisk Forskning och Innovation
Icam: Integrated Computer Aided Manufacturing
IDEF: Integration DEFfinition
IPW: In Process Workpiece
KPI: Key Performance Index
NC: Numerical Control
OE: Operation Element
OEM: Original Equipment Manufacturer
PCI: Process Capability Index
PLM: Product Lifecycle Management
PPAP: Production Part Approval Process
P-FMEA: Process Failure Mode and Effects Analysis
PPM: Parts Per Million
PPP: Process Planning Process
PUR: Performance Utilisation Ratio
R&D: Research and Development
RE: Random Error
ReVer: Realistic Verification
SE: Systematic Error
T1, T2... : Tool number 1, 2 et cetera
TE: Total Error
VW: Virtual Workshop
XI
Contents
List of publications...................................................................................VII
Abbreviations............................................................................................IX
1 Introduction ..................................................................... 1
1.1 Process planning for manufacturing ............................................... 1
1.2 Structure of the thesis ..................................................................... 4
2 Frame of reference .......................................................... 5
2.1 PPP - the process planning process ............................................... 5
2.2 Process capability – a boundary condition for process planning .... 7
2.3 Conceptions of process characterisation ........................................ 8
2.4 The beam balance .......................................................................... 8
2.5 The beam balance versus process capability ................................. 9
2.6 Transmission part manufacturing .................................................. 12
2.6.1 The transmission parts .......................................................... 12
2.6.2 The workshop ........................................................................ 12
2.6.3 The process planning ............................................................ 13
3 Problems to solve and research objectives ............... 15
3.1 Industrial problem to solve ............................................................ 15
3.2 Related process planning research .............................................. 16
3.3 Research objectives ...................................................................... 20
3.4 Delimitations .................................................................................. 20
4 Research approach ....................................................... 21
4.1 R&D of systematic process planning methods ............................. 21
4.2 Positioning of appended publications ........................................... 24
XII
5 Performed research and synthesis of results ............ 25
5.1 Schematic process planning ......................................................... 26
5.1.1 An approach for systematic process planning of gear
transmission parts .................................................................. 27
5.2 Capture of process behaviour ....................................................... 28
5.2.1 Paper A: Process modelling using upstream analysis of
manufacturing sequences ...................................................... 30
5.2.2 Paper B: Analysis of process parameters during press
quenching of bevel gear parts ............................................... 33
5.2.3 Multivariate data analysis ...................................................... 38
5.3 Tolerance synthesis and analysis ................................................. 39
5.3.1 Paper C: Tolerance chain design and analysis of in-process
workpiece ............................................................................... 41
5.3.2 Paper D: Process chain based workpiece variation simulation
for performance utilisation analysis ....................................... 44
5.4 Synthesis of results ....................................................................... 49
5.4.1 Information flows .................................................................... 49
5.4.2 Refining the PPP with information flows ................................ 52
6 Discussion and conclusions ........................................ 55
6.1 Coverage of the research results .................................................. 55
6.2 Follow-up of research objective 1 .................................................. 56
6.3 Follow-up of research objective 2 .................................................. 58
6.4 The relation between the PPP and PPAP ..................................... 58
6.5 Process capability index ................................................................ 59
6.6 Relevance of the chosen problems to solve ................................. 61
6.7 Scientific contribution ..................................................................... 62
XIII
6.8 General application of the research results .................................. 62
6.9 Conclusions ................................................................................... 63
7 Future work.................................................................... 65
7.1 Economic aspects ......................................................................... 65
7.2 Representation of the PPP and information flows ........................ 65
7.3 The model driven approach .......................................................... 66
7.4 A new approach for P-FMEA ........................................................ 66
8 References ..................................................................... 67
1
1 Introduction
1.1 Process planning for manufacturing
Process planning for manufacturing covers a wide range of activities
needed to specify the manufacturing process for a part. Unfortunately,
the process planning terminology “is somewhat fuzzy” (Anderberg, 2012)
and there is no commonly adopted or standardised definition of what is
included or not, nor for the activities whatever they may be.
Marri, et al. (1998) gives a definition that seems to be applicable in
most cases where process planning is considered:
“Process planning is defined as the activity of deciding which
manufacturing processes and machines should be used to perform the
various operations necessary to produce a component, and the sequence
that the processes should follow.”
A more comprehensive definition of the aim of process planning for
manufacturing is that by ElMaraghy and Nassehi (2013):
“Process planning, in the manufacturing context, is the determination
of processes and resources needed for completing any of the
manufacturing processes required for converting raw materials into a
final product to satisfy the design requirements and intent and respect
the geometric and technological constraints.”
An important addition to these constraints is that the manufacturing
must be cost-effective for a business to be successful. This is realised by a
well-running manufacturing process in addition to efficient process
planning to meet agreed deadlines (Scallan, 2003). The integration of
manufacturing cost is essential and has been discussed by many authors
(Gupta, et al., 2011; Xu, et al., 2009; Mayer & Nusswald, 2001).
Process planning is also described to be the interface between product
design and manufacturing (Scallan, 2003) and the work often includes
coordination of product design intentions and constraints imposed by the
workshop (Ham, 1988). The process planner therefore plays a desirable
role not only to define a process plan but to contribute with
manufacturing knowledge to a competitive product design (Bagge, 2009;
Groche, et al., 2012; Inman, et al., 2013).
To bring some clarity in different focuses for process planning,
ElMaraghy and Nassehi (2013) have defined four process planning levels
2 | INTRODUCTION
according to Table 1. These levels are placed in order from a very low level
of detail to a very detailed level. In addition, the focus and output from
each level are identified.
Table 1: Process planning levels (ElMaraghy & Nassehi, 2013).
Process planning
level
Main focus of planning at
this level
Level of detail Planning output at this level
Generic planning
Selecting technology and rapid process planning
Very low Manufacturing technologies and processes, conceptual plans, and DFx analysis results
Macro planning Multi-domain Low Routings, nonlinear plans, alternate resources
Detailed planning
Single domain, single process
Detailed Detailed process plans (sequence, tools, resources, fixtures, etc.)
Micro planning Optimal conditions and machine instructions
Very detailed Process/Operation parameters, time, cost, etc., NC codes
This definition of process planning levels is established in the
academic research community but is not so well known by process
planners in industry. The probable reason is that in practice these four
levels overlap each other and are difficult to distinguish. More easily
recognised are the main activities related to process planning. These
activities have been listed by Alting and Zhang (1989) in ten steps:
1. Interpretation of product design data (CAD data, material properties,
batch size, tolerances, surface definition, heat treatment and hardness,
special requirements).
2. Selection of machining processes (e.g. turning milling, drilling and
grinding), usually based on a company specific strategy.
3. Selection of machine tools considering for example availability,
process capability, machining range and production rate.
4. Determination of fixtures and datum surfaces.
5. Sequencing the operations.
6. Selection of inspection devices.
7. Determination of production tolerances.
INTRODUCTION | 3
8. Determination of the proper cutting tools and conditions (e.g. depth of
cut, feed rate and cutting speed).
9. Calculation of the overall times (machining, non-machining and set-
up times).
10. Generation of process sheets, operation sheets and NC data.
There is no established definition of what a process plan is, and what
process planning work shall include, furthermore not all process planners
in all companies may perform all of the activities.
Process planning has been, and still is, mainly a knowledge intensive
manual task performed by a skilled person because of its multi-
perspective problem nature including “problem-solving, constraint-
reasoning, goal-achieving, resource utilisation and conflict-resolution”
(Ham, 1988). Although computer aided process planning (CAPP) has
been on the production research agenda for decades it has not been
commonly assimilated into industry (Xu, et al., 2011; Anderberg, 2012).
In the end, the goal for the process planner is to design a
manufacturing process that is capable of producing a part that fulfils all
design requirements, without incurring high economic costs. Xu, et al.,
(2009) emphasise the major impact that process planning has on factory
activities and the resulting manufacturing costs.
Process planning is, and will be, an important issue for all
manufacturing companies, no matter whether manual, computer-aided
or, most probably, a combination.
4 | INTRODUCTION
1.2 Structure of the thesis
Figure 1: Structure of the thesis.
5. Performed research and synthesis of results
1. Introduction-A common description of process planning for manufacturing
2. Frame of reference-Process planning as considered in this thesis
4. Research approach
6. Discussion and conclusions
Refined PPP
Schematic
process
plan
Schematic process planning
Tolerancing
Process
control
strategy
Defining process control strategy
Predicted
outcome
of process
Tolerance
analysis
Process
plan
concept
Initial
process
planning
YES
NO
Good
enough?D
Simulations
Design of
Experiments
Multivariate
data
analysis
Work instructions
Tool layout
NC-programs
Process
plan!
Establish
process
plan
Examples of supporting
processes to create:
Measuring programs
Assignment
directive
Control documents
F G
...
Part design
Workshop
Collaboration parties:
Manufacturing technology
BE
I
H
H
H
K
J
I
DC
A
In-process
tolerances
In-process
dimensions
Process behaviour
In-process tolerances
Product characteristics
Process settings
G
F
Part design
K
Framework of process
planning methodsLicentiate thesis
Paper A
Paper B
Paper C and D
Objective 1
Objective 2
3. Problems to solve and research objectives
3.3 Research objectives
3.2 Related process planning research
3.1 Industrial problem to solve
7. Future work
PPP
5
2 Frame of reference
The overall scope of this thesis comprises all levels of process planning
described in Table 1 but does not deal in detail with all potential topics
and activities. The following will be an introduction to the frame of
reference in which the research has been performed and to the origin of
and ideas for resolving the subsequently identified problems.
2.1 PPP - the process planning process
To clarify the general conception of process planning it is appropriate
to make some more statements and explain the desired goal for the
process planner.
Process planning is work that includes all needed activities to define a
process plan. A process plan is a specification that contains information
aimed to facilitate production of a certain part in a definite
manufacturing system. In practice, two tacit boundary conditions narrow
this definition; the process plan must be defined in a way that ensures
that the produced part fulfils all requirements stated in the design
specification as well as minimising the production cost.
As process planning in general is a very broad topic it is here delimited
to fit the purpose of this thesis. My approach to giving an overview of the
process planning domain is shown in Figure 2 as a flow chart of the
“process planning process” (PPP). The PPP flow chart includes activities
derived from my own experiences and ideas; many of them, especially
sub-activities, are also found among the ten activities listed by Alting and
Zhang (1989).
6 | FRAME OF REFERENCE
Figure 2: The PPP flow chart – an approach to describe the process planning
process (PPP).
According to Figure 2, the main flow starts with the activity “initial
process planning” on receiving an assignment directive. The result is a
process plan concept where the main directions of the following work
have been set. This is used together with knowledge about the behaviour
of relevant manufacturing processes as input to the three following
process planning activities.
These three activities are interdependent and are performed in an
iterative way starting with schematic process planning. Schematic
process planning includes, for example, interpretation of design
requirements, definition of production and operation sequence, machine
tools, cutting and work holding tools.
Schematic
process
plan
Schematic process planning
In-process
work piece
tolerancesTolerancing
Process
control
strategy
Defining process control strategy
ResultTolerance
analysis
Process
plan
concept
Initial
process
planning
YES
NO
Good
enough?
Process
behaviourSimulations
Process
behaviour
Design of
Experiments
Process
behaviour
Multivariate
data
analysis
Known
process
behaviour
Work instructions
Tool layout
NC-programs
Process
plan!
Establish
process
plan
Examples of supporting
processes to create:
Measuring programs
Control documents
...
Assignment
directive
Part designPart design
FRAME OF REFERENCE | 7
Tolerancing is to define in-process workpiece (IPW) tolerances in
conjunction with the schematic process plan, process behaviour and
design specification of the part.
The process control strategy is based on both the schematic process
plan and the required tolerances, but also on the behaviour of all included
processes.
The defined process plan is then analysed regarding tolerances and the
expected outcome of the process. The results are tested against
acceptance criteria to decide whether the defined manufacturing process
can be approved or not. If the manufacturing process has all the
necessary qualities to succeed, the process plan can be established and all
needed documents, programs, working instructions, etc. created.
2.2 Process capability – a boundary condition for process planning
Because a manufacturing process that produces virtually no products
out of specification tends to be very expensive, there is often an
acceptance criterion allowing a small amount of deviation from
specification. This criterion is commonly defined as the lowest acceptable
level of a process capability index 1 (PCI), the highest number of defect
parts per million (PPM) or sometimes as maximum defects per million
opportunities (DPMO) (Wu, et al., 2009). The aim of using this kind of
criteria is to prohibit poor production process performance in relation to
the requirement, preventing adherent high quality deficiency costs.
PCIs is often used to set a limit for the lowest acceptable process
capability, but has not been used for limiting the highest acceptable
process capability. The aim of a higher limit should be to prevent
excessive manufacturing cost due to overqualified manufacturing
resources; this is in contrast to the commonly used lower limit which
aims to save the customer from getting a product out of specification.
Even though a useful higher limit of capability index is not defined in the
literature, the assumption here is that the process planner must try to
obtain a balance between required product performance and the
economic efforts put into the manufacturing process.
1 Capability index (typically Cp, Cpk, Cm, or Cmk) represents the relation between the requirement
(tolerance range) and the statistically defined process behaviour (e.g. 6*standard deviation).
(Montgomery, 2009)
8 | FRAME OF REFERENCE
2.3 Conceptions of process characterisation
Until now, three conceptions of a similar kind have been used to
characterise a process; process performance, process behaviour and
process capability. Some clarifications are in order to avoid confusions.
“Process performance” refers to the accuracy of the process when
creating a final part dimension or an IPW dimension.
“Process behaviour” is a representation of the manner, or action, of
the manufacturing process.
“Process capability” is dedicated to the established conception of PCI
where a tolerance range is related to a statistical representation of the
process behaviour.
2.4 The beam balance
My conceptual approach to show the effect of the relation between
product performance and production performance is illustrated by the
beam balance in Figure 3
The leftmost scale pan contains a load that represents the product
performance, stated as requirements in the design specification. The
rightmost pan contains the load of the manufacturing process
performance as a consequence of the process plan.
Figure 3: A balance beam balancing product performance and manufacturing
performance.
High cost!
High cost!Competitive
cost?
c)
a)
b)
FRAME OF REFERENCE | 9
If the decided manufacturing process cannot balance the product
design requirements, as shown in Figure 3 a, there will be a situation with
more out-of-specification parts. If the effect of this poor quality is
managed within the production system, for example by putting extra
efforts in measuring, sorting procedures, re-work and scrapping, the
production cost is assumed to be high (Fischer, 2011). If the poor quality
is not given enough attention and the parts reach a down-stream
customer, like the assembly line, the costs tend to be very high and even
worse, if the final customer recognises parts out-of-specification, there is
a risk of extremely high expenses.
The inverse situation (Figure 3 b) occurs when the manufacturing
process is specified to contain resources where the capabilities exceed the
requirements. The production cost is then assumed to be higher than
necessary because of unutilised performance of the manufacturing
resources. In addition, there is a risk of increased processing lead times
when using manufacturing equipment with unnecessarily high quality
capabilities (Mayer & Nusswald, 2001). This is a contributory cause for
high overall production cost.
2.5 The beam balance versus process capability
To elaborate the rather intuitive approach with the balance beam, I
have created a simplified causal graph model in Vensim2, shown in
Figure 4. This model includes the necessary underlying factors and the
relations to understand the balance beam and show why process
capability can be regarded as the other side of the same coin.
2 Vensim is software for “system dynamics” modelling. System dynamics is a methodology for
modelling complex, dynamic casual systems (Sterman, 2001). Vensim is also used later on to
simulate manufacturing processes (Bagge, et al., 2014).
10 | FRAME OF REFERENCE
Figure 4: Relations between product performance and manufacturing process
performance.
According to the right part of Figure 4, earnings when selling a
product are defined as the difference between the manufacturing cost and
the sales price. Sales price is highly dependent on the product
performance aimed to satisfy the customer3. Manufacturing cost is a
result of both manufacturing resource cost and, if any, an internal cost of
poor quality (CoPQ).
Manufacturing resource cost depends on the manufacturing process
performance. This relation is most frequently illustrated as a chart
showing possible tolerance width versus the manufacturing cost (left part
of Figure 5) (Manoharan, et al., 2012; Hamou, et al., 2006; Wu, et al.,
1998). An increasing tolerance width results in lower manufacturing cost
thanks to a decreased need for manufacturing process performance. This
can correspondingly be expressed as shown in Figure 5 b, where
increased manufacturing process performance drives higher
manufacturing cost.
3 The used curve in Figure 4 is just an attempt to illustrate a conceivable relation between sales price
and product performance.
Manufacturing
resource cost
Capability
Tolerance
width
Manufacturingcost
Sales price
Earnings
-
+
Manufacturing
process performance
Product
performance
Manufacturingprocessvariation
Lookup
CoPQ
+
Cost of
poor quality
+
Lookupmanuf cost
Balance
Lookupprice
1/x
1/x
-
External
Internal
Co
PQ
Process capability
Ma
nu
f. r
eso
urc
e c
ost
Manuf. process performance
=
Sale
s p
rice
Product performance
FRAME OF REFERENCE | 11
Figure 5: Manufacturing cost versus tolerance width (left) and versus process
performance (right).
The cost of poor quality in Figure 4 (Anderberg, 2012) is a result of a
low-capability manufacturing process that (too often) produces parts out
of tolerance. If the CoPQ appears internally, before delivery to the final
customer, it is here regarded as an undesirable contributor to the
manufacturing cost. If the customer is subjected to poor quality, the
CoPQ (external) will have a negative impact on earnings, no matter
whether it is put on the manufacturing account or elsewhere.
Capability originates from the relation between the tolerance width
and the manufacturing process variation and drives CoPQ if it is too low.
Tolerance width and manufacturing process variation are in turn the
inverses of product performance and manufacturing process performance
respectively. The quotient is a measure of the balance between these two
performances. This implies that the beam balance approach can be
supported by the use of PCI as an indicator for what is a well-balanced
manufacturing process design.
In the short term, imbalances between the product design and the
production facilities can be accepted. If the manufacturing process
capability is low, there may be a temporary solution to filter off bad
quality parts by additional measuring and checking (Anderberg, 2012). If
there are high performing resources available in the workshop, they may
be used in favour of investments in new, less sophisticated equipment.
As the production efficiency, final cost and product quality are affected
by the process plan (Chang, 2005), the desirable balance between product
performance and manufacturing process performance (Figure 5 c) is
definitely considered to be dependent on the work of the process planner.
Man
ufa
ctu
rin
g co
st
Tolerance width
Man
ufa
ctu
rin
g co
st
Manufacturing process
performance
12 | FRAME OF REFERENCE
2.6 Transmission part manufacturing
As already explained, the work of a process planner cannot be
unambiguously defined because it will contain different activities,
strategies and methods depending on situation. Typically, the type of
part, production rate, annual volumes, company strategies, etc. gives
different possibilities and constraints for process planning. The following
will give an insight into the area of industrial transmission part
manufacturing from which process planning experiences and the research
presented in this thesis are derived.
2.6.1 The transmission parts
The studied company both develops and manufactures transmission
parts in-house. Precision manufacturing is performed in workshop
facilities aimed at series production of gear wheels, pinions, gear box
shafts, etc. for heavy trucks, busses and coaches. Annual production
volumes for each category can be counted in terms of ten thousand to a
few hundred thousand parts. Each part is usually in production for at
least a couple of years. Figure 6 shows cylindrical gears for a gearbox and
bevel gears for rear axles.
Figure 6: Examples of transmission parts:
Bevel gear pinion (a), bevel gear ring gear (b),
counter shaft (c) and gear wheel (d).
2.6.2 The workshop
The manufacturing process for these kinds of parts can be divided into
three sub-processes; 1) soft machining, 2) heat treatment and 3) hard
machining, as shown in Figure 7.
a)
c)
b)
d)
FRAME OF REFERENCE | 13
Figure 7: A typical manufacturing process chain for gears.
These three sub-processes contains various multi-step machining or
heat treatment operations organised on different kinds of manufacturing
lines. A common line for soft machining contains one or two multi-
spindle lathes, machine tools for gear and spline cutting and some
equipment for chamfering/deburring. Heat treatment is normally case
hardening followed by tempering. Hard machining lines usually include
machine tools for hard turning, grinding, honing and gear grinding.
Depending on transmission part category, a complete manufacturing
process includes 6 to 12 set-ups, measuring excluded, for the part to
undergo before it is finished.
Each line is designed and equipped to produce one kind of
transmission part such as gear box gears, gear box main shafts, gear box
counter shafts, bevel gear pinions, etc. Depending on part design, the
fixtures, cutting tools and NC programs are changed to facilitate
manufacturing of variants within the desired operational range of the
line.
2.6.3 The process planning
As a consequence of the product design, most transmission parts are
able to be grouped or categorised, and the process plans are more or less
systemized in accordance with these part categories. For example, bevel
gear sets for the rear axle (one pinion and one ring gear) may have
different gear ratios depending on customer needs, but the gear housing
axle casing is the same. This is realized by using standardised interfaces
between gears and housing and then defining a range of gear sets that will
Raw
material
/blanksSoft machining Heat treatment Hard machining
Inspection and measuring
Carburiz-
ing
Quench-
ing
Temper-
ingTurning Drilling
Gear
cutting
Turning/
grindingHoning
Gear
grinding
14 | FRAME OF REFERENCE
fit. These standardised interfaces are utilised also in the manufacturing
process. All the pinions are manufactured in the same way, on
standardised machining lines with the same kind of machine tools. Most
of the cutting tools and fixtures are also the same, but with some specific
NC programming and gear cutting tools suited to the individual, gear
ratio dependent dimensions.
This structure is emphasised by the company strategy, advocating
both systematic part design and manufacturing process design, which in
turn characterises the process planning work.
The process planning methods can be regarded as mostly retrieval
because of this strategy. This implies that the process planning has more
of a continuous improvement and long-term perspective than handling
completely new product designs in a workshop with frequent
changeovers. Much effort can be put into getting proper designs for both
the manufacturing process and the part.
This industrial production environment is the cause and trigger of
many thoughts and ideas of methods for systematic planning of precision
manufacturing processes.
15
3 Problems to solve and research objectives
Two main process planning problems are discussed in this thesis. The
first has its background in my own industrial experiences and relates to
the need for different kinds of process planning methods. The other is
identified as a result of studies of related academic research about process
planning. The research objectives are a consequence of these two problem
definitions as illustrated in Figure 8.
Figure 8: The research objectives are derived from both an industrial and an
academic problem identification.
3.1 Industrial problem to solve
Process planning is a challenge because of the diversity of activities
required to design and evaluate a manufacturing process. Human
knowledge has to be involved, yet it is not possible to efficiently transform
this into CAPP systems because of the multi-perspective problem nature.
Because many people are usually involved in product and manufacturing
process development much process planning information has to be both
easily accessible and comprehensible for others than the process planner.
Tacit process planning knowledge and reasons for decisions have to be
explicitly described. The first issue when developing methods for process
planning is to facilitate systematic representation of qualitative
information. The other issue is to integrate methods for quantitative
process planning information.
Product design specifications are with only few exceptions based on
quantitative measures. Whatever the kind of acceptance criterion for
good quality is and what is regarded as an acceptable production cost,
there is a need to estimate the outcome of a proposed manufacturing
3. Problems to solve and research objectives
3.3 Research objectives
3.2 Related process planning research
3.1 Industrial problem to solve
16 | PROBLEMS TO SOLVE AND RESEARCH OBJECTIVES
process. The most decisive question before finalizing a process plan is
whether the outcome of the manufacturing process will be as expected.
Will the parts comply with the specification? How often will an out-of-
tolerance part appear? The process planner must quantify and ascertain
the “truth” about the production system capabilities to be able to answer
these questions and define a process plan that ensures well-running
production.
Even if the produced parts fulfil the specifications, the manufacturing
process is not automatically well-utilised in terms of production system
capabilities. The mission for the process planner is to design the process
in a way that gives a good yield in terms of required quality but also low
cost thanks to a smooth running production with well utilised workshop
resources that are suited to the purpose.
In multi-step manufacturing processes, which are used for gear
transmission parts, there are many concurrent parameters that must be
considered and the process planner has many choices to make while
developing the process plan. The process plan must be more or less
detailed to avoid deviations due to variations in machine tool set-up,
cutting tool change, process control strategy, etc. There will often be a
broad range of possible solutions for bringing different process steps
together and define for example a control strategy for a complete
manufacturing process.
There is a need to analyse the effect of potential process plan
solutions, parameter settings, IPW tolerances, control strategies, etc. and
there has to be one or more methods available where these aspects can be
defined and tested. Predicting manufacturing process outcome and
making comparisons to the design part specification have to be integrated
parts of a framework of methods to support process planning.
There is a need for a framework of process planning methods
integrating both descriptive/qualitative and analytic/quantitative
parameters and able to reveal whether the proposed process plan is good
enough.
3.2 Related process planning research
An introduction to the nature of process planning and how it is
defined in literature has been given in chapter 1. The following gives
further details of previous research efforts on topics related to the
identified problem.
PROBLEMS TO SOLVE AND RESEARCH OBJECTIVES | 17
The greater part of modern process planning research has been
devoted to CAPP. CAPP has challenged the manufacturing research
community for the last four decades and a huge volume of literature has
been published (Xu, et al., 2011). Though there are extensive efforts put
into CAPP research, and many different CAPP systems have been
developed, there is still a long way to go before process planners can
commonly use the technique. This was the overall conclusion drawn 20-
25 years ago about the state of CAPP research and implementation
(Alting & Zhang, 1989; ElMaraghy, 1993) and the situation is hardly
changed since (Xu, et al., 2011; Anderberg, 2012).
The problem with CAPP is characterised by many interdependent
parameters and variables (Denkena, et al., 2007), not only technical but
economical, organisational and environmental. However, this problem is
not peculiar to CAPP but characterises the complex and dynamic nature
of process planning in general (Ham, 1988), whether it is performed by
computers or humans.
As the process planner spends a great deal of time on creating and
administrating operation sheets, drawings, text documents, etc.
(Lundgren, et al., 2008; Halevi & Weill, 1995) there is an incentive to
rationalise and automatise such work. This is one of the topics that CAPP
research has been focusing on and what can be associated with research
areas such as product lifecycle management (PLM), computer-aided
design (CAD) and computer-aided manufacturing (CAM). As indicated in
the PPP (Figure 2) these kinds of activities are done when a process plan
is established and most of the process planning decision-making is
already done; for example, there are available definitions of operation
sequence, suitable machine-tools, tolerances and control strategies.
This thesis has its focus on the activities leading to establishing the
process plan according to the PPP, i.e. process planning decision-making,
roughly described as manufacturing process design. Here, the CAPP
research has been devoted to different approaches to reflect the heuristic
character of human-based process planning and develop computer
systems showing some traits of human intelligence. Artificial intelligence
(AI) has the goal of modelling human intelligence on computers, and has
been one of the main approaches for automated process planning.
Rzevski (1995) has listed five paradigms of AI in engineering:
knowledge-based systems, neural networks, genetic algorithms, fuzzy
logic and intelligent agents. Most of the AI-related CAPP research can be
18 | PROBLEMS TO SOLVE AND RESEARCH OBJECTIVES
classified according to these paradigms with examples given in Table 2.
Some of them are hybrids covering more than one paradigm.
Table 2: Examples of research in different paradigms of artificial intelligence (AI).
Paradigm of AI
Examples of research
Knowledge-based systems
(Kusiak, 1991; Xu, et al., 2009; Zhang, et al., 2012), including definition, storage and retrieval of relevant process planning information, often incorporated in a PLM system (Denkena, et al., 2007)
Neural networks
(Teti, et al., 1999) (Hua & Xiaoliang, 2010) (Amaitik & Kiliç, 2007)
Genetic algorithms
(Qiao, et al., 2000) - operation sequencing (Cai, et al., 2009) integrates process planning and scheduling (Li, et al., 2002) (Azab & ElMaraghy, 2007) Reconfigurable process planning (Nejad, et al., 2012) Model based tolerance analysis
Fuzzy logic (Amaitik & Kiliç, 2007) (Chen, et al., 1995) (Li & Gao, 2010) Integrates process planning and scheduling
Intelligent agents
(Li, et al., 2011) (Bose, 1999) Operation sequencing
Much of the AI–based CAPP research has an ambition to develop
systems that automatically optimise the process plan, whether it is about
operation sequencing, scheduling, tool selection, tolerancing or some
other task. Many of these systems are shown to be suitable and well
working for a highly delimited environment with well-defined
constraints, but are not up to the wide-ranging characteristics of a
complete process planning scope. Existing CAPP solutions do not have
the possibility to include for example business and strategic
considerations, which often have an impact on the choice of technology
and machining resources (Denkena, et al., 2007).
Finding research with the emphasis on comprehensive and heuristic
process planning methods and understanding is difficult. Although,
Timm et al. (2004 a, 2004 b) are good examples of where a heuristic and
graphical approach is used for developing a mathematical model to
support and link design dimensioning and process planning. Some
general design rules can then be derived and explained, based on the
model and the results.
PROBLEMS TO SOLVE AND RESEARCH OBJECTIVES | 19
Even more focus on the working processes, without the ambition of
AI, self-learning abilities or automatic optimisation, is a qualitative and
knowledge based approach to manufacturing process planning presented
as a part of “Produktionslotsen” (DMMS, 2009) (in English; the
production pilot) (Chen, et al., 2011). Produktionslotsen contains
systemised, model-based guides to support not only process planning but
other production-related activities such as factory planning, production
investments and continuous improvements.
A platform for model driven process planning for machining is
presented by Hedlind (2013). With this platform, not only the human
perspectives of visualisation and interaction of models can be satisfied
but also the technological aspects of standardised information
representation and exchange in a product realisation environment.
Probably, it is this kind of comprehensible approach, with the
potential to gather process planning intelligence, knowledge and
information to support efficient decision-making, that has to be
developed to bring CAPP one step towards industrial implementation. In
the CAPP review (25 years ago) Ham (1988) pointed out the importance
of detailed understanding of the task to be automated:
“In that sense, before pursuing any computer-based effort toward
process planning, it is necessary for us to move one step back and ask
ourselves: do we really understand the process planning task well enough
for computer automation? In other words, the fundamental questions
such as what is process planning, how is it being done presently in actual
practice, are there more logical ways to doing it must be answered before
any programming efforts should take place. Given even the current state
of computer technology, including artificial intelligence, it is still not
possible to develop computer programs to intelligently carry out tasks
unless we, as intelligent human beings, fully understand those tasks.”
In spite of the fact that computer technology has experienced huge
developments since 1988, lack of process planning understanding and
systematisation still seems to be a reason that restricts the relevance of
CAPP research and the successful industrial implementation.
20 | PROBLEMS TO SOLVE AND RESEARCH OBJECTIVES
3.3 Research objectives
Two research objectives are defined as a consequence of the identified
problems in sections 3.1 and 3.2.
Research objective 1
The first objective has an all-embracing aim of finding methods that
cover essential activities for process plan synthesis and analysis,
including the possibility to predict the outcome of a proposed process
plan. These essential activities are all found in the PPP flow chart in
section 2.1. Much attention is paid to understanding and analysis.
Research objective 2
The second objective is a result of the request for systematisation and
a deeper understanding of process planning, addressed by the academic
production research community. The aim here is to develop the PPP flow
chart on the basis of experiences and findings from the methodology
studies addressed in objective 1.
3.4 Delimitations
The last activity in the PPP flow chart “Establish process plan” is not
covered by the performed research; i.e. creating documentation required
for production, as operation sheets, control plans and NC programs.
Focus is on the underlying process planning efforts needed for its
creation.
Even though the manufacturing cost is an important factor it is not
included in this thesis other than in general terms as described in
section 2.
“Process performance” is just a matter of geometric dimensions, it
does not include any aspect concerning productivity, production rate or
volume capacity, for example.
Time related aspects such as machining cycle time or lead time
through a complete manufacturing process are not considered.
This thesis does not go into automatic optimisation procedures to find
for example appropriate quality or balanced tolerances.
Production scheduling and planning is not treated, nor process
planning for job-shops.
21
4 Research approach
The origin of the work presented in this thesis is years of production
engineering practice for the manufacturing of transmission parts such as
gears, shafts and synchromesh. Experiences and ideas are mainly derived
from my own process planning work, performed in an industrial
environment where the tasks have been both managing and improving
mature processes and products in addition to development of new ones.
The research has been characterised by adopting a methodology based
approach to meet the objectives stated in section 3.3. The approach is
comprehensive and aims to gather essential methods that should cover
the process planning domain as shown in Figure 2. The level of details is
not restricted and can include all four levels defined by ElMaraghy and
Nassehi (2013) in Table 1.
Strategies for defining tolerances and evaluation of manufacturing
process chains are examined with most of the focus on the detailed and
micro-planning levels. The purpose of examining these strategies is to
facilitate a well-substantiated answer to the final revealing question:
-Is the process plan good enough?
The work is also related to the request for more process planning
knowledge addressed by Ham (1988) in chapter 3.2. The approach to
meet this need is to refine the PPP by using results derived from the
methodology studies.
The research is not aimed at completing any existing CAPP approach
but to independently contribute to better process planning
understanding, without limitations in available computer-based process
planning aids.
4.1 R&D of systematic process planning methods
The presented research extends the qualitative approach for
systematic process planning (Bagge, 2009) to include quantitative and
analytical methods for design and evaluation of a process plan. A
conceptual illustration of the research and development (R&D) process
for this work is shown in Figure 9.
22 | RESEARCH APPROACH
Figure 9: Research and development of systematic process planning methods.
The licentiate thesis “An approach for systematic process planning of
gear transmission parts” (Bagge, 2009) covers descriptive and qualitative
methods for process planning, e.g. how to deal with product design
requirements, part and manufacturing datums, manufacturing operations
and manufacturing sequence, all with an explanatory approach. The left
part of Figure 9 represents this schematic process planning and the tacit,
but crucial, knowledge about process behaviour.
However, making analyses and predictions of the process plan
requires quantification both of the requirements, typically manufacturing
tolerances, and the behaviour of the included manufacturing processes.
The purpose of the new research is to reach the complete process
planning state , “To be”, on the right of the arrow in Figure 9, where not
only descriptive and qualitative methods are defined but also analytic and
quantitative methods. This part of the work corresponds to research
objective 1.
Research objective 2 is realised by using results derived from the work
for objective 1 and has not been published separately. Research objectives
1 & 2 and their relation to the performed research are showed in
Figure 10.
Schematic
process planning
Descriptive, qualitative
Capture of
process behaviour
Perceptual, qualitative
Schematic
process planning
Descriptive, qualitative
Capture of
process behaviour
Quantitative
(Perceptual, qualitative)
Complete
process planning
Descriptive, qualitative
Analytic, quantitative
Revealing
New research
As is To be
RESEARCH APPROACH | 23
Figure 10: Research objectives 1 & 2 related to the performed research.
5. Performed research and synthesis of results
4. Research approach
Refined PPP
Schematic
process
plan
Schematic process planning
Tolerancing
Process
control
strategy
Defining process control strategy
Predicted
outcome
of process
Tolerance
analysis
Process
plan
concept
Initial
process
planning
YES
NO
Good
enough?D
Simulations
Design of
Experiments
Multivariate
data
analysis
Work instructions
Tool layout
NC-programs
Process
plan!
Establish
process
plan
Examples of supporting
processes to create:
Measuring programs
Assignment
directive
Control documents
F G
...
Part design
Workshop
Collaboration parties:
Manufacturing technology
BE
I
H
H
H
K
J
I
DC
A
In-process
tolerances
In-process
dimensions
Process behaviour
In-process tolerances
Product characteristics
Process settings
G
F
Part design
K
Framework of process
planning methodsLicentiate thesis
Paper A
Paper B
Paper C and D
Objective 1
Objective 2
3. Problems to solve and research objectives
3.3 Research objectives1. Find suitable methods for process planningi
2. Increase knowledge about process planning
3.2 Related process planning research
3.1 Industrial problem to solve
PPP
24 | RESEARCH APPROACH
4.2 Positioning of appended publications
The appended publications in this thesis and their coverage are
mapped onto the PPP flow chart as shown in Figure 11. Experiences from
the FFI ReVer project have not yet been published but are partially
described in this thesis as an example of multivariate data analysis.
Figure 11: Coverage of appended publications upon the PPP flow chart.
Paper B
(Bagge
Lindberg
2012)
Paper A
(Werke
et al.
2014)
FFI
ReVer
project
Paper D
(Bagge et al. 2014)
Licentiate thesis
(Bagge 2009)Schematic
process
plan
Schematic process planning
In-process
work piece
tolerancesTolerancing
Process
control
strategy
Defining process control strategy
ResultTolerance
analysis
Process
plan
concept
Initial
process
planning
YES
NO
Good
enough?
Process
behaviourSimulations
Process
behaviour
Design of
Experiments
Process
behaviour
Multivariate
data
analysis
Known
process
behaviour
Work instructions
Tool layout
NC-programs
Process
plan!
Establish
process
plan
Examples of supporting
processes to create:
Measuring programs
Control documents
...
Assignment
directive
Part designPart design
Paper C (Bagge et al. 2013)
25
5 Performed research and synthesis of results
The five publications shown in Figure 11 are turned to account in this
thesis, and all discuss supporting methods for process planning. These
methods have been gathered into a framework for process plan
development shown in Figure 12 as a result of the research undertaken.
This framework is introduced here to provide an overview and better
understanding while reading the following summaries of the publications.
Figure 12: Framework of methods for process plan development.
The framework contains three vital process planning subjects
indicated in Figure 12:
1. Schematic process planning
2. Capture of process behaviour
3. Tolerance synthesis and analysis
Finished part
CAB ab
Turning
Retractable jawsFace
driver
a’b’A’B’End-machining and centre drilling
Parting line and
residual flash
a’b’
1 2
3
OE
Representation of IPWdimensions
Illustration of part geometry
Productdimensions
& tolerances
PSIPW
dimensions& tolerances
Mappingmatrix
FDProcess chain
dim. & tol.
OEIPW
dimensions
PSview of
IPW variation
OEview of
IPWvariation
Mappingmatrix
Representation ofproduct dimensions
PS
Representation of IPWdimensions
Comparison
26 | PERFORMED RESEARCH AND SYNTHESIS OF RESULTS
Each subject has one or more supporting methods to realise the
subject intentions, all outlined in this thesis.
Subject 1: Schematic process planning
The methodology for systematic process planning covers
interpretation and transfer of design requirements into the schematic
process plan. The schematic process plan facilitates explanatory
representation of machining or heat treatment operation elements,
process sequence and important decisions such as the choice of datum,
clamping surfaces and tools. The representation of measuring operations
and measuring equipment is done by the same principles. It is desirable
to highlight key features derived from both design specification and
manufacturing process development.
Subject 2: Capture of process behaviour
Subject 2 contains the supporting methods; simulation, design of
experiments (DOE) and multivariate analysis. The aim is to capture
knowledge and information about the behaviour and abilities of potential
manufacturing processes to be included in the process plan design.
Subject 3: Tolerance synthesis and analysis
The DDC2 technique is used to support the development of a process
plan regarding synthesis and analysis of tolerances. Different process
plan solutions, tolerancing strategies and control strategies can be tested
and the outcome of a contemplated or existing manufacturing process
predicted.
In the following sections, summaries of the appended publications are
presented after a short introduction to each subject.
5.1 Schematic process planning
The schematic process planning has been characterised as a
descriptive and qualitative part of the process planning domain. This has
mainly been dealt with in the licentiate thesis “An approach for
systematic process planning of gear transmission parts” (Bagge, 2009).
PERFORMED RESEARCH AND SYNTHESIS OF RESULTS | 27
5.1.1 An approach for systematic process planning of gear transmission
parts
The licentiate thesis (Bagge, 2009) introduces the field of process
planning of transmission parts, based on experiential research. What role
does the process planner play in different situations, what is the area of
responsibility, what motivates the use of systematic and transparent
process planning methods? Figure 13 characterises the interaction
between the process planner and collaboration parties while developing a
process plan.
Figure 13: The role of the process planner.
There is always an inflow of information from part design to process
planning, and often a possibility to feed back to part design. This depends
on the situation and where the designed part is in the product life cycle.
In product development stages it is common to have collaboration
between part design and process planning and here are considerable
possibilities to make design adjustments. When the product is mature,
possibly becoming a spare part, and a sub-supplier is assigned to start
production, the threshold for changing the design is conversely high. In
addition, collaboration with external providers of manufacturing
technologies very much depends on the situation and on company
strategies.
Often process planning is based on experiences and knowledge of the
process planner, performed by intuition, and without any explicit
Process Plan:
Methods
Operation sequence
Machining data
Tolerances
Cutting tools
Clamping tools
Measuring strategy
Part design
Workshop
facilities
Manufacturing
technology
28 | PERFORMED RESEARCH AND SYNTHESIS OF RESULTS
procedures for working. The only documentation is the finally defined
process plan and the traceability of made decisions is poor.
The licentiate thesis proposes a method that facilitates visualisation of
the intentions of the process planner, the prerequisites and the
circumstances for made decisions. This makes the determining factors for
the resulting process plan transparent and comprehensible.
One important aspect of process planning is tolerancing, which must
be considered to complete the definition of how a part shall be
manufactured. Detailed tolerancing is not included in the licentiate thesis
but the principles of IPW tolerance chains (tolerance stack-ups) are
pointed out. Identification of relations between machining datums and
created features is described by using a datum hierarchy (rooted-tree)
diagram. Bagge ends up with the following conclusions about the
proposed method for systematic process planning (Bagge, 2009):
It describes a systematic approach to interpret new as well as mature
products.
It prescribes systematically how essential design characteristics should
be transferred to the process plan.
It points out how attention should be paid to datums
It guides the process planner through the evaluation of operations
It proposes a way to document the process planning work, aimed at
both immediate and future needs.
Further performed research and publications are along the same line
as the proposals expressed in the licentiate thesis; tolerancing,
quantifying performance of needed manufacturing processes and
capability testing of the developed process plan.
5.2 Capture of process behaviour
One precondition for creating a relevant process plan is to have
knowledge about the abilities and behaviour of the processes to be used.
This is essential when IPW tolerances and control strategy shall be
defined.
Depending on the situation and the exactitude of the process planning
work, different degrees of understanding of process behaviours are
required. It is possible, but not always advisable, to compensate for
deficient information about process abilities by defining (too) wide IPW
PERFORMED RESEARCH AND SYNTHESIS OF RESULTS | 29
tolerances. This will result in low utilisation of the process performance.
If the process behaviour does not correspond to what is assumed, and the
IPW tolerances are too tight, there is an obvious risk of difficulties to
control the process and, as a result, getting parts out of specification.
Knowledge of manufacturing process behaviour can be captured in
many ways, in different situations and for different purposes.
This thesis identifies and deals with three different methods according
to Figure 14 for investigating the behaviour of complex processes. One or
more of these methods can be used to shed light on process behaviour
and bring valuable input data to the development of the process plan.
Figure 14: Methods to capture process behaviour.
When developing a new process plan for a new part, with new and
unrealised manufacturing processes, there will at the start be little or no
available knowledge about process capabilities. This implies the use of
methods for simulation to get one step closer to what is required.
Section 5.2.1 refers to Werke, et al. (2014) who proposes a methodology
for process modelling of manufacturing sequences.
If there is a need for new, or more detailed, information about
especially complex processes, DOE has been proven to support the
process planner (Bagge & Lindberg, 2012). This is further referred to in
section 5.2.2.
For established processes in workshops specialised for production of
high volumes of the same kind of parts, there is often a lot of production
data available. Work package A of the Realistic Verification (ReVer)
project has the intention to use available historical process and material
Process
behaviourSimulations
Process
behaviour
Design of
Experiments
Process
behaviour
Multivariate
data
analysis
Known
process
behaviour
30 | PERFORMED RESEARCH AND SYNTHESIS OF RESULTS
data to predict geometrical distortion of press quenched gears. A tool
based on data analysis should then support the process planner. This kind
of multivariate data analysis approach is discussed in section 5.2.3.
5.2.1 Paper A: Process modelling using upstream analysis of
manufacturing sequences
Simulations enable the examination of processes not only in the initial
development stages, but also when they are well established. One reason
for making simulations of established processes is to facilitate efficient
process start-up and control. For example, when material properties have
a significant impact on a machining or heat treatment operation, proper
process settings can be foretold by simulations.
Werke, et al. (2014) takes a step closer to efficient modelling and
simulation of manufacturing processes by proposing an algorithm for
upstream process modelling.
The methodology defines a set of rules for system modelling of
manufacturing sequences to be used in process planning. It facilitates the
selection of critical process parameter and an evaluation of how
variations in these parameters influence the final component properties.
The virtual manufacturing chain acts as an important source for
extracting and quantifying the process behaviour that is needed when
developing and controlling a process.
In contrast to the more intuitive downstream approach, the upstream
approach is governed more by the purpose of the simulations; not to
model a complete manufacturing chain and then make fragmentary
simulations. This is illustrated in Figure 15 where a conceptual framework
of models for making simulations to evaluate one or more properties of
the final part is defined. In this example, it is obvious that only some of
the process steps in the physical chain are represented in the virtual
chain. The reason is that only process steps affecting the finally examined
properties are modelled.
The principles of the upstream process modelling are along the same
line as the method for upstream process planning (Bagge, 2009)
illustrated in Figure 16.
PERFORMED RESEARCH AND SYNTHESIS OF RESULTS | 31
Figure 15: Transformation of a physical manufacturing sequence into a framework
of models and simulations.
Figure 16: Process planning methodology compared to manufacturing sequence.
Process
step 4
Process
step 3
Process
step 2
Process
step 1
(First)
Process
step 5
Process
step 6
Sim.
4,1
Process
step 7
(Final)
Physical chain:
Framework of simulations:
Model data
Current
process
Process
param.
Sim.
4,2
Process
param.
Sim.
2,1
Model data
Current
process
Process
param.
Sim.
2,2
Model data
Current
process
Process
param.
Sim.
5,1
Process
param.
Sim.
5,2
Process
param.
Sim.
6,1
Process
param.
Sim.
7,1
Process
param.
Accumulated
results
Final
workpiece
Process
param.
Process
param.
Process
param.
Process
param.
Process
param.
Process
param.
Process
param.
First operation
Final operation
Manufacturing Process Chain
Process Planning
32 | PERFORMED RESEARCH AND SYNTHESIS OF RESULTS
The process planner starts with the specification of the finished part
and defines the final manufacturing operation first. The work then
proceeds by defining required upstream process steps to fulfil the final
part specification.
Two cases in the paper exemplify the upstream process modelling
methodology; one for a bevel gear pinion and one for a forged steering
arm. The purpose of the virtual chain for the pinion shown in Figure 17 is
to simulate and reduce the accumulated displacements after case
hardening and in the end eliminate a straightening operation.
Figure 17: Framework of simulation activities for the pinion case study.
In conclusion, the paper proposes an algorithm to establish simplified
metamodels of manufacturing sequences using breadth first search. The
algorithm is based on stepwise upstream selection of process steps and
facilitates extraction of a virtual simulation sequence from a physical
manufacturing sequence.
The virtual sequences can be used for optimisation of process
parameters and evaluation of the effects of replacing, removing or adding
process steps to a manufacturing sequence.
PERFORMED RESEARCH AND SYNTHESIS OF RESULTS | 33
Additional comments on the results in paper A
Even if each simulation case has its own focus and well-defined
demand for output data, there is a potential to derive more useful
information from simulations by using the same, or slightly modified,
models but ask for other characteristics. For example, the pinion case has
its focus on geometric displacements, but the simulation models used also
contain information about steel properties like carbon content, and
residual stress distributions (Werke, 2009). This information may be of
interest for the product designer as it has an impact on product
characteristics such as the strength of the pinion.
5.2.2 Paper B: Analysis of process parameters during press quenching
of bevel gear parts
In transmission part manufacturing, heat treatment processes (for
example case hardening) are commonly used to increase the strength and
wear resistance of the produced parts (Lingamanaik & Chen, 2012;
Arimoto, et al., 2011). A well known drawback of the case hardening
processes is the more or less predictable geometric distortion (Holm, et
al., 2010). These distortions have a broad range of possible causes with
not fully known physical properties and mechanisms which make process
control difficult (Funatani, 2011; Canale & Totten, 2005).
In order to minimise distortion and control the quality, press
quenching is a commonly used method in industry. Bagge and Lindberg
(2012) has shown the potential to use DOE as a method for getting more
knowledge about press quenching of bevel gears. The results enable
definition of IPW tolerances satisfying a pre-defined acceptance level of
defect parts.
It would have been desirable to have a deep understanding of how
material characteristics and phase transformations influence the result,
but the aim of the paper was to find possible statistical relations between
process control factors and their impact on dimensional properties.
Figure 18 show the bevel gear crown wheel and press quenching set-up
used for the DOE investigation.
34 | PERFORMED RESEARCH AND SYNTHESIS OF RESULTS
Figure 18: Investigated bevel gear crown wheel (top)
and set-up in a press quenching machine (bottom).
The investigated properties are the centre-hole diameter (Ø240) and
conical properties of the back face (dishing) according to Figure 19.
Figure 19: Crown wheel back-face dishing.
Despite the press quenching machine having a pressure setting for an
expanding mandrel, aimed to influence Ø240, the effect is not obvious by
studying historical production data. Data for Ø240 versus the
corresponding expander pressure setting shown in Figure 20 does not
correlate as expected.
65
Ø480
Ø240Back face
Fexpander
FinnerFouter
Quench oil flow
Cradle
+
-
PERFORMED RESEARCH AND SYNTHESIS OF RESULTS | 35
Figure 20: Historical production data for diameter 240 mm, compared to the
corresponding expander pressure.
This is an example when the outcome of a manufacturing process is
not simply deduced and understood by observing existing data.
The DOE matrix included not only the expander pressure setting, but
also six other factors supposed to influence the investigated responses:
Ø240 and back face dishing.
The multivariate method used for estimating regression models for
both responses simultaneously was the partial least square method. The
coefficients used in the models are shown in Figure 21.
Figure 21: Left: Coefficient plot and error bars (95%) for the response Ø240
Right: Coefficient plot and error bars (95%) for the response dishing.
0
10
20
30
40
50
60
239,5
239,6
239,7
239,8
239,9
240,0
240,1
240,2
240,3
240,4
Pre
ssure
[bar]
Dia
mete
r [m
m]
Ø240 Tolerance limits Expander pressure
-0,03
-0,02
-0,01
0,00
0,01
0,02
0,03
0,04
Inn
er
Ou
ter
Exp
an
Cra
_(C
1)
Cra
_(C
2)
Mtr
l_(M
1)
Mtr
l_(M
2)
Le
ve
l
[mm
]
N=55 R2=0,837 RSD=0,0236
DF=48 Q2=0,790 Conf. lev.=0,95
-0,04
-0,03
-0,02
-0,01
0,00
0,01
0,02
0,03
0,04
Inn
er
Ou
ter
Cra
_(C
1)
Cra
_(C
2)
Mtr
l_(M
1)
Mtr
l_(M
2)
Le
vel
Inn
er*
Inn
er
Inn
er*
Ou
ter
[mm
]
N=55 R2=0,891 RSD=0,01809
DF=47 Q2=0,789 Conf. lev.=0,95
36 | PERFORMED RESEARCH AND SYNTHESIS OF RESULTS
The regression models then facilitate estimations of the impact of
individual factors and interactions of factors on the responses. The
number of significant factors affecting each response (Ø240 and back face
dishing) indicates the complexity of the process.
The regression models have then been used to make predictions of the
press quenching process outcome. Making process outcome predictions is
a desirable feature from a process planning point of view.
The predictions were made using the Monte Carlo simulation
technique and facilitate the estimation of the number of parts out of
specification, quantified as defect parts per million opportunities
(DPMO).
Bagge and Lindberg (2012) defined four scenarios which were
examined regarding the estimated DPMO for both Ø240 and back face
dishing. The scenarios were defined as combinations of two possible
cradles (C1 and C2) and two different materials (M1 and M2). The
predicted distributions and DPMO for each scenario are shown in Figure
22 and Figure 23.
Figure 22: Simulated distributions and DPMO for Ø240.
0
5000
10000
239,95 240 240,05 240,1 240,15 240,2
Counts
Ø240 [mm]
C1 M1DPMO=0
C1 M2DPMO=0
C2 M1DPMO=0
C2 M2DPMO=0
L. Tol. U. Tol.
PERFORMED RESEARCH AND SYNTHESIS OF RESULTS | 37
Figure 23: Simulated distributions and DPMO for dishing.
The DOE investigation and the consequent DPMO predictions are
useful for the process planner because they give important information
about the behaviour of the process and provide a good decision base. In
this case, the possibility to tighten the tolerance for Ø240, block certain
scenarios (C1 M1) or widen the tolerance for dishing can be useful when
designing a well-balanced and competitive manufacturing process.
To conclude Bagge & Lindberg (2012) :
Design of experiments is a proper method capable of examining the
complex process of press quenching.
Regression models can in combination with Monte Carlo simulations
be used to estimate the outcome of different press quenching
scenarios.
The simulation results make it possible to perform an IPW tolerance
analysis by estimating DPMO for each scenario.
Design of experiments is a method suitable to support process
planning regarding process knowledge and tolerancing.
0
10000
20000
-0,06 -0,05 -0,04 -0,03 -0,02 -0,01 0 0,01 0,02 0,03 0,04 0,05 0,06
Counts
Dishing [mm]
C1 M1 0,04: DPMO=832 0,05: DPMO=26
C1 M2 0,04: DPMO=38 0,05: DPMO=0
C2 M1 0,04: DPMO=6 0,05: DPMO=0
C2 M2 0,04: DPMO=6 0,05: DPMO=0
L. Tol.U. Tol.
38 | PERFORMED RESEARCH AND SYNTHESIS OF RESULTS
5.2.3 Multivariate data analysis
In many cases data from for example machine tool capability tests,
cutting tool tests or measurements from running production are available
after years of production. This data should be a good input to the process
planner for improving an established process plan or bringing a new
variant of a part into production. Depending on the complexity of the
process to be evaluated, the diversity of parameters taken into account
and data quality, data has to be handled in a more or less sophisticated
way.
For example, the relation between cutting tool wear, resulting in a
gradually shifting dimension, and the produced number of parts may be
good enough to predict process behaviour. However, if the result depends
significantly on a diversity of factors such as the number of produced
parts, material batch characteristics, surface roughness from an earlier
operation, temperature in the workshop, etc., multivariate analysis is a
more appropriate method.
This approach, where production data is used as a base for finding the
behaviour of a complex process, is used within the FFI project Realistic
Verification (ReVer). The aim of the project is to arrange and analyse
available production data in a way that geometrical distortions of a bevel
gear crown wheel can be derived. The production data includes many
manufacturing process parameter settings, material properties and
information about the origin of the raw material. Unfortunately, it has
shown to be a challenge to make the required multivariate data analysis
and get reliable results.
The reason is the great number of parameters having a potential
impact on the geometrical distortion of the bevel gear crown wheel, and
finding out which of them, and how they influence distortion, has been a
topic for research for many years. Figure 24 shows a commonly
recognized diagram where a multiplicity of parameters is identified, all
with a potential to have an effect on the distortion.
PERFORMED RESEARCH AND SYNTHESIS OF RESULTS | 39
Figure 24: Potential reasons for distortions. Based on Funatani, IMST Institute
IDE2011, Bremen.
A large amount of production data has been sampled and organised
for analysis in the ReVer project. The great number of potentially
significant parameters in combination with uncertainty about the validity
of some data makes an evaluation difficult. Despite these circumstances,
some significant correlations between factors and responses have been
identified within the project.
The experiences show that it is not so easy to derive relevant
parameters from the available production data without having a good
hypothesis as a start. As in DOE, the chance of getting good results when
analysing complex processes much depends on the initial knowledge and
thoroughly reviewed assumptions. The better the hypothesis, the better
the chance to succeed with the multivariate analysis.
As much of the process planning work is knowledge intensive,
multivariate analysis has a potential to be a good long-term contributor
that knowledge. However, it requires a systematic approach to store
production data with enough quality to be analysed. It also requires good
skills in multivariate data analysis methods, as instantiated by SIMCA4.
5.3 Tolerance synthesis and analysis
Tolerance chains, or stack-ups, are well known within the product
design domain but not usually recognised in production engineering,
though they are undoubtedly present in many manufacturing processes.
The appearance of tolerance chains and streams of variation (Ceglarek, et
4 SIMCA is software for multivariate analysis developed by Umetrics.
www.umetrics.com
Composition
Hardenability
Grain size
Rolling
Refining
Melt lot
SteelMethods
Lot
Residual stress
Shape
Deform rate
Equipment
Cold formingMachinability
Residual stress
Cut volume
Tool sharpness
Cut condition
Machine
Machining
Furnace
Temperature
Quenchant
Agitation
Pressure
Tank design
Quenching Press
Force
Tool design
Temperature
Strategy
Time
Weight
Core hardness
Surface hardness
Case depth
Shape
Mass balance
Part design
Die set
Machine
Fiber flow
Forge rate
Post cooling
Heating
Temperature
Forging
Timing
Cooling
Temperature
Duration
Furnace
Thermal cycle
Annealing
Timing
C-potenial
Temperature
Furnace
Tray & Jig
Atmosphere
Carburizing
Furnace
Cooling
Duration
Temperature
Pre heat treatment
Distortion
40 | PERFORMED RESEARCH AND SYNTHESIS OF RESULTS
al., 2004) in machining processes are similar phenomena to those found
in assemblies.
Design of multi-step manufacturing processes can be compared to
product design of an assembly containing several parts. The assembly
must be suited to the intended application and satisfy the needs of the
customer. This requires that each constituent part of the assembly has an
appropriate design and that they all are assembled in the right way.
Putting the parts together often requires a specified sequence. An
important aspect of designing assemblies is tolerancing of the parts. The
part tolerances must be specified in a way that they, when combined,
assure the function of the assembly. This combination of part tolerances
forms one or several tolerance chains.
In contrast, designing a multi-step process, such as for machining,
does not aim to specify how to make an assembly, but how to combine
different manufacturing processes to produce one final part in a proper
way. This combination of manufacturing processes does often result in
tolerance chains that determine how well the design part specification can
be met.
One important difference between design part tolerances and process
plan tolerances must be emphasised. The product designer appends
requirements to each design part as e.g. dimensional tolerances, and
sometimes to the complete assembly. The design part tolerances are
mainly on functional requirements arising from the intended application
of the part. From the process planner’s point of view; the IPW tolerances
must guarantee satisfaction of these requirements, but also process
related needs in the different stages of manufacture. These process-
related functional needs are important to carry out for each
manufacturing step. For example, in multi-step manufacturing where the
workpiece is moved between fixtures in different machine tools, or just
re-positioned and clamped in the same fixture, one or more features act
as locating surfaces. These surfaces have the in-process function of
making accurate fixturing of the workpiece possible. Another example is
when a measuring operation requires special surface characteristics other
than those defined by the product designer.
The process planner has to ensure the quality of the final part, not by
copying design part tolerances, but by defining both prerequisites and
allowed outcome for each process step as IPW tolerances. These IPW
tolerances are sometimes, but not always, equal to the design part
PERFORMED RESEARCH AND SYNTHESIS OF RESULTS | 41
tolerances and must, when combined, satisfy the design part
requirements.
This subject has been dealt with in two of the appended publications.
The main development of a method for tolerance chain design and
analysis was done by Bagge, et al. (2013) when first introducing the
dimension dependency chart (DDC). The DDC was then further
developed to become the DDC2 and used as a workbench for evaluation
of different tolerancing strategies (Bagge, et al., 2014).
5.3.1 Paper C: Tolerance chain design and analysis of in-process
workpiece
Bagge, et al. (2013) highlights the relations between the process plan5
and the product specification, but also between the process plan and the
basic manufacturing operations of which the process plan is composed.
The relations between these basic manufacturing operations, called
operation elements (OE), the process plan and the designed product
specification are shown in Figure 25.
Figure 25: Relations between operation elements, process plan and designed
product specification.
5 “Process plan” represents in this case only the process steps, their sequence, nominal dimensions and
tolerances. To be compared to the general description of process plan given in chapter 2 at page 5.
Process plan
Operation element
Operation element
Operation element
Operation element
Operation element
Operation element
behaviour
Dimensions
and tolerances
Designed product
specificationComparison
42 | PERFORMED RESEARCH AND SYNTHESIS OF RESULTS
The following gives a more detailed explanation of what an OE is:
Each process step in the process plan defines a task to be performed, for
example turning of a surface, and how the result is to be evaluated. Even
if this process step is realised by just one turning operation, the method of
evaluation may entail that the shown result depends on more than one
operation. Typically, this situation appears when a distance between two
machined surfaces needs to be measured and evaluated. The distance is a
result of the position of two surfaces created in not only the last, but also
in a previous operation. The definition of the process step therefore
includes two single operations, which are the “operation elements“. The
process plan is developed by defining and combining suitable operation
elements capable of producing the final part.
A method for the development of a process plan in terms of
dimensions and tolerances is described and facilitated by the dimension
dependency chart (DDC). The DDC is a structure based on the tolerance
charting technique where the design part tolerances, the IPW tolerances
and the operation element variations are connected. A conceptual
description of the DDC is illustrated in Figure 26 where information flows
between both design specification and process plan (upper view), and
between OE and process plan (lower view) are shown.
Figure 26: A conceptual illustration of information flow in the dimension dependency
chart (DDC).
Upper
view
Process plan
Design specification
Process plan
Operation element behaviour
Lower
view
PERFORMED RESEARCH AND SYNTHESIS OF RESULTS | 43
The DDC shown in Figure 27 has the capability to include and gather
both operation element behaviours and process step definitions and make
a comparison between the allowed outcome of the proposed
manufacturing process and the design specification. Details of the
procedure to establish a DDC are given in the appended paper C.
Figure 27: DDC – Dimension dependency chart.
1 2 3 4 5 Tol TE SE RE
X F P. step 1 1 0,5 0,3 0,2
X P. step 2 1 0,4 0 0,4
X F P. step 3 0,5 0,4 0 0,4
X F P. step 4 1 1 0,6 0,4
F X P. step 5 1,2 0,5 0,3 0,2
X F P. step 6 0,1 0,2 0,1 0,1
SE RE
X F Element 0,3 0,2 1 1 1 1
X Element 0,3 0,2 -1
X F Element 0,3 0,2 -1
X F Element 0,3 0,2 1
F X Element 0,3 0,2 1
X F Element 0,1 0,1 1
Comparison
Symbols:
Design dimension
Machined feature and extent of dimension
F Finally machined feature
X Machining location and datum surface
1 2 3 4 5 Dim Tol Dim Tol
50 2 50 2,3
20 2 20 0,1
20 3 20 2,8
20 4 20 1
Dim Tol
X F P. step 1 71 1
X P. step 2 49 1 1 1
X F P. step 3 30 0,5 -1
X F P. step 4 20 1 1
F X P. step 5 21 1,2 1 1
X F P. step 6 20 0,1 -1 1 -1
Comparison
+-
Lowerview
Upperview
44 | PERFORMED RESEARCH AND SYNTHESIS OF RESULTS
The concluding remarks from Bagge, et al. (2013) are:
The paper introduces the DDC for tolerance chain design and analysis
of an IPW.
The methodology is a novel approach based on the tolerance chart
technique to define, include and connect operation element behaviour
to the process plan and evaluate the expected outcome of the process
chain.
The DDC facilitates quantification and integration of smart process
planning practice in the process plan and enables analytical
calculation and evaluation of the results.
5.3.2 Paper D: Process chain based workpiece variation simulation for
performance utilisation analysis
As mentioned, the DDC was first introduced by Bagge et al. (2013) as a
methodology for process planners to design and evaluate in-process
tolerance chains. Figure 28 shows the second edition of the dimension
dependency chart (DDC2) which is a result from elaborating the DDC
methodology to facilitate the use and analysis of more substantial and
detailed input data (Bagge, et al., 2014).
Figure 28: DDC2 – Dimension dependency chart 2.
OE
Representation of IPWdimensions
Illustration of part geometry
Productdimensions
& tolerances
PSIPW
dimensions& tolerances
Mappingmatrix
FDProcess chain
dim. & tol.
OEIPW
dimensions
PSview of
IPW variation
OEview of
IPWvariation
Mappingmatrix
FD = Final dimensionIPW = In-process workpiecePS = Process stepOE = Operation element
Representation ofproduct dimensions
PS
Representation of IPWdimensions
Comparison
PERFORMED RESEARCH AND SYNTHESIS OF RESULTS | 45
The IPW tolerances for each PS are set by the process planner to limit
the permissible IPW shape deviations. In addition to the IPW tolerances,
it must be possible to satisfy the specification of the final part with the
manufacturing process defined in the process plan. The process planner
must evaluate each PS and the complete process chain regarding
capabilities to fulfil all requirements.
As discussed in section 2.2, the calculation of PCI is a commonly used
method to indicate the abilities of a manufacturing process to produce
within tolerance. Despite the statistical condition of normally distributed
process outcome to estimate the standard deviation and calculate for
example Cp, judgements based on PCI appears to be applied even when
that condition is not met. Many manufacturing processes do show trends
caused by tool wear, temperature changes, etc. and are often dependent
with correlated outcomes implying non-normally distributed data.
The first objective in the paper was to evaluate PCI as an indicator in
analysing IPW tolerance chains, a task in planning of multi-step
manufacturing processes. The second objective was to examine how the
characterisation of causes of deviation in the process chain and its
behaviour, affects the performance utilisation of the defined process.
The research was performed by developing and using the DDC2 as a
workbench for analysis of the process plan and evaluating how various
strategies for tolerancing and use of process data result in different
utilisation of the manufacturing equipment performance.
As in paper C, the subject for the process plan was a shaft, here
referred to as the “Xshaft”, shown to the left in Figure 29. The Xshaft is a
fictitious part but it has distinctive features found in typical transmission
parts like the gear shaft to the right in Figure 29.
Figure 29: Xshaft (left) and gear shaft (right).
50± x
20±x20±x20 ± x
Xshaft1:1 02014
46 | PERFORMED RESEARCH AND SYNTHESIS OF RESULTS
The Xshaft is proposed to be machined in three set-ups. The
contribution from each OE to the PSs defined in the process plan
continues to the final part dimensions as illustrated in Figure 30.
Figure 30: Manufacturing process for the Xshaft.
The focus for the research was to evaluate what can be achieved from
the available manufacturing facilities when used as defined in the process
plan. The manufacturing facilities have been assigned with behaviour
characteristics, like tool wear and random variations, to represent
industrial conditions. Each operation is defined as an OE and the ability
to create a feature on the Xshaft depends on the assigned behaviour of
that OE.
The aim of the investigations is to find out the tightest tolerances on
the FDs that the proposed manufacturing process chain can provide. The
tighter the tolerance, the higher the performance of the process, and
better the possibility for the part designer to increase the performance of
the product. The evaluation criterion was the potential process
performance utilisation (PUR) when different strategies for tolerancing
and use of process behaviour definitions are applied to the process plan.
Five simulation scenarios were defined. Four of the scenarios
represent different IPW tolerancing strategies in combination with
different characterisation of process behaviour information. The fifth
scenario is the reference where the “pure process behaviour” and its
propagation through the process chain determines the process outcome,
Operation elements
OE1
OE2
OE3
OE4
OE5
OE6
Process steps
PS1
PS2
PS3
PS4
PS5
PS6
Final dimensions
FD1
FD2
FD3
FD4Set-up 2
Set-up 3
Set-up 1
PERFORMED RESEARCH AND SYNTHESIS OF RESULTS | 47
without any limitations such as IPW tolerances and simplified
representation of process behaviours.
The reference scenario was defined to have 100% process PUR. All
other scenarios showed to have lower or much lower PUR for one or more
of the evaluated FDs.
As this paper considers manufacturing processes showing not only
random but also systematic variations, the significance of these
systematic variations (expressed as SE) is of interest. What outcome will a
virtual workshop with more pronounced SE than RE show compared to a
workshop where SE and RE are similar?
To show the impact of different workshop characteristics two
examples were defined: virtual workshop 1 (VW1) and 2 (VW2).
VW1 has SEs and REs with the same proportions; SE/RE quotient is
approximately 1. VW2 has SEs double the REs and consequently the
quotient is 2.
The simulations were performed using Vensim DSS software (by
Ventana Systems, Inc.) together with the DDC2. In total, 100 000 Xshafts
were produced virtually for each scenario to avoid statistical uncertainties
related to random factors. Figure 31 shows input and output data
provided by Vensim for the simulation model based on the DDC2.
Figure 31: Vensim simulations applied on the DDC2.
Symbols:
Design dimension
Machined feature and extent of dimension
F Finally machined feature
X Machining location and datum surface
1 2 3 4 5 DimDP TolDP TolFD DimFD
1 Design dim. 50 50
2 Design dim. 20 20
3 Design dim. 20 20
4 Design dim. 20 20
SEPS REPS TEPS TolPS DimPS
X F P. step 1 71
X P. step 2 -49 -1 -1
X F P. step 3 -30 1
X F P. step 4 -20 -1
F X P. step 5 -21 -1 -1
X F P. step 6 20 -1 1 -1
SEOE REOE DimOE
Normal
direction
-1 -1 -1 1 T1 X F Element 71 1
1 T1 X Element 22 1
1 T1 X F Element 41 1
1 T2 X F Element 51 -1
1 T3 F X Element -21 -1
1 T4 X F Element 20 1
+-
T1
T3T1
T4
T2
T1
70,98
71,01
71,04
1 50Part instance no.
Dimension PS1
70,98
71,01
71,04
1 50Part instance no.
Dimension OE 1
49,98
50,01
50,04
1 50Part instance no.
Dimension FD1
-0,03
0
0,03
50
Part instance no.
RE OE 1
0
0,02
0,04
0,06
1 50Part instance no.
Tool wear T1-T4
48 | PERFORMED RESEARCH AND SYNTHESIS OF RESULTS
Figure 32: Simulation results - Performance utilisation for FD1-4.
Figure 32 shows results from the simulations, in total 10 for each FD.
It is important to make clear that VW1 and VW2 represent two different
conditions to demonstrate how the workshop characteristics affect the
result in general; they do not compete with each other. The aim is to do a
sensitivity analysis regarding the impact on PUR related to the relation
between SE and RE for the involved processes.
A complete interpretation of the results is given in the appended
paper. From the results the conclusions are:
Using Cp in the IPW tolerancing strategy decreases the PUR, especially
for high Cp values and long process chains. In the presented case study
the use of TE instead of Cp=1,33 gives a higher PUR.
Separating SE and RE is useful if there are coupled, or semi-coupled,
OEs. SE for the PS will in these cases be eliminated or reduced.
Simplified definitions of process behaviour result in an
underestimation of the process performance. The longer the process
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
PS: Cp PS: TE OE: TE OE: SE&RE Reference
Pe
rfo
rma
nce
utilis
atio
n r
atio
FD1
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
PS: Cp PS: TE OE: TE OE: SE&RE Reference
FD2
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
PS: Cp PS: TE OE: TE OE: SE&RE Reference
Perf
orm
ance u
tilis
ation r
atio
Scenario
FD3
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
PS: Cp PS: TE OE: TE OE: SE&RE Reference
Scenario
FD4
Performance utilisation ratio (PUR)
Virtual Workshop 1
Virtual Workshop 2
FD1
FD4FD3FD2
PERFORMED RESEARCH AND SYNTHESIS OF RESULTS | 49
chains, the bigger the underestimation. Detailed definitions and
knowledge about process behaviour are vital for high PUR.
PUR is a potential element of a key performance index for process
planning.
Additional comments on the DDC2
General methods for tolerance charting often include abilities to make
stock removal calculations for cutting operations. Blank dimensions and
tolerances together with stock removal calculations have also been a
subject for integration into the DDC2. The attempts have shown to be
useful in practice, but at the time of writing, no research results have been
published.
5.4 Synthesis of results
The triangular framework for process plan development (Figure 12) is
synthesised as a result of the examined process planning methods
described in sections 5.1-5.3.
5.4.1 Information flows
Even though the process planning methods have been introduced and
observed as separate issues, each one requires more or less information
from the neighbouring subjects inside the triangle.
There is also an important interaction and information exchange with
the external collaboration parties “Part design”, “Workshop facilities” and
“Manufacturing Technology”, adopted from Figure 13.
If the available workshop facilities do not fulfil the needs, a
development or investment in new manufacturing technologies must be
considered. The specification of requirements on such technologies
mainly derives from the proposed conceptual or schematic process plans.
Information flows both inside and outside the triangle have been
identified and are indicated in Figure 33 as arrows A-K.
50 | PERFORMED RESEARCH AND SYNTHESIS OF RESULTS
Figure 33: Triangular framework with information flows.
The information flows are provided by the capabilities of each subject
in the triangular framework and aims to satisfy both internal and external
needs during manufacturing process development.
Table 3 specifies the information flows corresponding to the arrows
A-K in Figure 33.
Part designManufacturing
technology
Workshop
facilities
Finished part
CAB ab
Turning
Retractable jawsFace
driver
a’b’A’B’End-machining and centre drilling
Parting line and
residual flash
a’b’
1 2
3
DC E
B
A
G
F J
K
H I
OE
Representation of IPWdimensions
Illustration of part geometry
Productdimensions
& tolerances
PSIPW
dimensions& tolerances
Mappingmatrix
FDProcess chain
dim. & tol.
OEIPW
dimensions
PSview of
IPW variation
OEview of
IPWvariation
Mappingmatrix
Representation ofproduct dimensions
PS
Representation of IPWdimensions
Comparison
PERFORMED RESEARCH AND SYNTHESIS OF RESULTS | 51
Table 3 Information flows according to arrows A-K in Figure 33.
Internal information exchange:
External information exchange:
A Process sequence Operation elements Key-features
F
Product specification: dimensions, tolerances and key-features
B Process behaviour IPW tolerances Product characteristics
G
Manufacturing prerequisites Part design suggestions Obtainable part tolerances Product characteristics Manufacturing knowledge (Manufacturing cost)
C
Process sequence Operation elements Measuring datums Machining datums IPW dimensions
H Manufacturing resources Process characteristics Production data
D IPW tolerances IPW dimensions Predicted outcome of process
I
Process plan Process settings
E Process behaviour IPW tolerances
J
Manufacturing technology characteristics
K
Request for manufacturing technology
52 | PERFORMED RESEARCH AND SYNTHESIS OF RESULTS
5.4.2 Refining the PPP with information flows
The PPP was first assigned in chapter 1 to describe the process
planning domain as a process flow chart. To take the knowledge about
process planning practice one step further, the content of the PPP can
now be refined by adopting the identified information flows. These
information flows indicated as A-K and the collaboration parties
“Workshop facilities” and “Manufacturing technology” are brought into
the PPP flow chart as shown in Figure 34 at page 53.
However, the information flows cannot be unambiguously transferred
to the PPP because it has a different structure to the triangular
framework. The triangular framework represents methods connected to
each other in terms of information flow interfaces. The PPP is a
description of the process planning process where different activities,
supported by methods, both require and are providers of information.
This leads to some flows appearing in more than one place in the PPP
and sometimes does not include all the elements of information defined
in Table 3 at page 51. This is the case for information flow “I” which is
found in two positions, but the information content for each position is
clearly shown in the flow chart to be “Process settings” and “Process plan”
respectively.
PERFORMED RESEARCH AND SYNTHESIS OF RESULTS | 53
Figure 34: Information flows applied to the PPP.
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54 | PERFORMED RESEARCH AND SYNTHESIS OF RESULTS
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55
6 Discussion and conclusions
6.1 Coverage of the research results
A number of methods to support process planning have been
presented in Chapter 5. The coverage of all these methods on the PPP is
summarised in Figure 35.
Figure 35: Final coverage of presented methods on the original PPP.
There are two areas within the PPP indicated by dotted lines;
multivariate data analysis and defining process control. These are
included in the work but have been the focus of less research attention.
Schematic
process
plan
Schematic process planning
In-process
work piece
tolerancesTolerancing
Process
control
strategy
Defining process control strategy
ResultTolerance
analysis
Process
plan
concept
Initial
process
planning
YES
NO
Good
enough?
Process
behaviourSimulations
Process
behaviour
Design of
Experiments
Process
behaviour
Multivariate
data
analysis
Known
process
behaviour
Work instructions
Tool layout
NC-programs
Process
plan!
Establish
process
plan
Examples of supporting
processes to create:
Measuring programs
Control documents
...
Assignment
directive
Part designPart design
56 | DISCUSSION AND CONCLUSIONS
However, both areas are seen as important because they contribute to
the design of competitive process plans.
Multivariate analysis has a potential to contribute because it facilitates
extraction of process behaviour from many different kinds of
unstructured production data. However, it is a challenge to derive useful
information if production circumstances have been changed and there is
no possibility to trace the changes.
A process control strategy is more or less mandatory and must be
defined in harmony with the rest of the process plan, especially tolerances
and measuring capabilities. The DDC2 methodology together with
simulation software, like Vensim, can be used for evaluation of different
control strategies. As shown in Paper B (Bagge, et al., 2014), using the
same cutting edge for two conjugate surfaces reduces the need for process
control without the risk of dimensional deviations. On the other hand,
when this is not possible, there is still a potential to increase the
utilisation of process performance by a well-defined process control
strategy. This can be shown by assigning for example imperative tool
change intervals and sequences for two or more cutting tools. It is also
possible to integrate the effect of measuring and control strategies like
statistical process control. The efforts put into process control can then be
compared to the PUR.
6.2 Follow-up of research objective 1
The main result of the methodology studies is the triangular
framework providing both qualitative and quantitative supporting
methods for process planning. Figure 36 revives the illustration from
Chapter 4 where the desirable characteristics of complete process
planning methods are indicated as; descriptive, qualitative, analytic,
quantitative and finally revealing.
DISCUSSION AND CONCLUSIONS | 57
Figure 36: Update of “Research and development of systematic process planning
methods“.
The attained characteristic of each subject in the triangle does
correspond to the conceptual representation of the research and
development process leading to methods for “complete process
planning”.
Descriptive and qualitative:
All methods in the framework have good potential to describe, explain
and visualise the mechanisms and decisions behind a process plan. Both
how single manufacturing process steps are defined and intended to work
and how they interact when combined into process chains. All examined
methods to capture process behaviour (simulation, DOE and multivariate
data analysis) require some kind of computation software whose user
interface considerably determines the descriptive qualities.
Analytic and quantitative:
The methods to capture process behaviour are all both quantitative
and analytic, likewise the DDC2 for tolerance synthesis and analysis.
Revealing:
As both single manufacturing process steps and process chains can be
analysed in terms of predicting the outcome, the methods have the
capability to reveal the requested qualities of a process plan.
As is To be
As is
Schematic
process planning
Descriptive, qualitative
Capture of
process behaviour
Perceptual, qualitative
Schematic
process planning
Descriptive, qualitative
Capture of
process behaviour
Quantitative
(Perceptual, qualitative)
Complete
process planning
Descriptive, qualitative
Analytic, quantitative
Revealing
New research
58 | DISCUSSION AND CONCLUSIONS
6.3 Follow-up of research objective 2
Interactions realised by the identified information flows must be
emphasised as vital for all process planning work; either the work is done
by systematic methods, automated or manual, or by an experienced
process planner using intuitive, informal and undefined procedures.
Many of the information flows can by intuition be defined by someone
with process planning experience, but not surely with distinct reasons for
why they exist and for what they are used. The information flows related
to the framework in Figure 33 have their origins in verified demands and
opportunities of the proposed process planning methods, which in turn
support important process planning activities.
These information flows, based on needs and opportunities, are used
to refine and give more substance to the PPP, gaining better
understanding of the process planning domain.
6.4 The relation between the PPP and PPAP
Many manufacturing companies are subcontractors to original
equipment manufacturers (OEM) that demand conformity in quality
assurance methods and harmonized documentation of the results. An
example of such a duty is the production part approval process (PPAP)
(AIAG, 2006) often used to satisfy the international standard ISO/TS
16949. PPAP is also used by OEMs to assure quality and smooth
deliveries from organisations within the company. The aim with PPAP is
that the supplier must provide the evidence that the part will correspond
to the design specification and possible to produce at a quoted production
rate.
Many of the required investigations to provide that information are
included in the process planning work. The reason is simply that the
process planning has the same goal as requested by the PPAP. How PPAP
is related to the PPP is shown in Figure 37.
DISCUSSION AND CONCLUSIONS | 59
Figure 37: The relation between PPP and PPAP.
Today, PPAP requires test-runs as “significant production run” or “full
run test” to check the real outcome of the manufacturing process. By
improving process planning methods to ensure reliable predictions of the
process outcome, test-runs will probably be of less interest since they will
show similar results to the predictions. This situation is also desirable for
efficient process planning because the goodness of a proposed process
plan can be estimated before it is finally established, without putting
efforts into the creation of work instructions, control documents,
NC-programs, etc. that must be changed.
Another reason is that a test-run is performed during a limited period
of time, without possibilities to test for example variations in material
properties, workshop environment and idling machine-tools. Such
sources for process variations are common in a workshop and may be
important factors to analyse before start of production.
6.5 Process capability index
As described in section 2.2, PCI is a commonly used measure of the
capacity of a manufacturing process and is related to the balance between
product and process performance. PCIs, including directives of required
Process
plan
concept
YES
NO
Good
enough?
Known
process
behaviour
Process
plan!
Establish
process
plan
Predicted
process
outcome
Result
Significant
production
run
Process Planning Process (PPP)
Production Part Approval Process (PPAP) Approved
PPAP
Process flow diagram
Control plan
Initial process studies
60 | DISCUSSION AND CONCLUSIONS
levels, are more or less defined by both corporate standards and trade
association standards like PPAP 4th ed. and APQP published by AIAG6.
It is clear that for example Cm acts as a contributor of knowledge about
the behaviour of one single machine tool and Cp of a complete process in
relation to the requirements defined by the process planner. But what is
the effect of using that kind of aggregated and statistically constrained
information as an evaluation criterion for manufacturing processes where
the output not only depends on probability but on dependencies and
predictable changes?
One result in the appended publications (Bagge, et al., 2014) is that
there is a potential to get better utilisation of the manufacturing process
performance by sidestepping the established use of PCI, without
increasing quality related deviations.
As trends and non-normal distributed outputs can be recognised, not
only for the IPW but for dimensions on the final part, the traditional use
of PCI for FDs can be questioned also. This is also put in question by Wu,
et al., (2009). The aim with PCI is to prevent poor quality by an indirect
limit on the number of defects. The better the information is about
process behaviour then better are the possibilities to predict the outcome
and evaluate the risk of defects. Instead of traditional PCIs the use of
estimated DPMO is more appropriate because it does not require
normally distributed data.
On the other hand, better the process knowledge, better the
possibilities to control the process. This will reduce the impact from
systematic errors and the random errors will dominate. This implies that
the process output tends to be more normally distributed and the use of
PCIs more appropriate.
As discussed in section 2.5, the desirable balance between product
performance and process performance can be expressed as some kind of
PCI. A target value (not lower limit) for PCI, or better DPMO, should be a
way for the process planner to design a well balanced manufacturing
process. It is very important to find out the effect of using PCI in every
process planning case, both regarding IPW tolerances and final part
tolerances, to avoid low PUR.
6 Production Part Approval Process (PPAP) and Advanced Product Quality Planning (APQP) are
published by the Automotive Industry Action Group (AIAG) at www.aiag.org.
DISCUSSION AND CONCLUSIONS | 61
6.6 Relevance of the chosen problems to solve
During the work there have arisen some questions and hesitations
related to the very broad range of activities that can be associated with
process planning. One question is about the relevance of the identified
problems to solve and the chosen objectives for the thesis.
The first objective treated in this thesis has its origin in needs
identified by myself but is not for what either process planners in
industry or researchers usually ask.
Process planners tend to be aware of time-consuming, regularised,
activities that have to be done but do not require much process planning
skill, like creation of work instructions and NC programming. This kind of
activity can often be easily described and explained, but the decision-
making behind it cannot. Probably this is why industry promotes
supporting methods for these quite simple, but time-consuming,
activities instead of the more abstract process planning core business.
Academic research has in turn mainly been focusing on computer
automated process planning, including optimisation, more than
computer aided process planning in general terms. Even if many of the
research results are promising, there is a problem to introduce them into
an industrial process planning environment where few formal methods
are defined, let alone standardised, and decision-making is a matter of
personal experience and tacit knowledge.
Process planners probably do not see how they should integrate the
developed tools into their own work because of many undefined
interfaces between activities and no possibility for plug-and-play. It is a
risk that the effort of implementation of such tools is too high in relation
to the perceived potential. The “perceived potential” is in turn a
consequence of the process planner’s view of the overall scope of process
planning and priorities, eventually underpinned by company key
performance indicators (KPI).
The full extent of a process planning scope depends on not only the
part design, workshop facilities and new manufacturing technology but
also the process planning work itself. Different decisions will result in
more or less need for changes of the part design, rearrangements in the
workshop or investments in new technology. As long as process planning
decision-making is mostly heuristic and dependent on humans the
decisions will be different in spite of the fact that the prerequisites were
exactly the same. To understand the intention of the process planner at a
62 | DISCUSSION AND CONCLUSIONS
certain moment and make it possible to call a decision into question,
there must be a proper representation of the basis for that decision. The
records must be comprehensible, not only for a process planner but to
provide further academic research.
Even though most industrial process planners and academic
researchers have not prioritised comprehensible process planning
methods, the topic is still desirable and much work has to do be done
within this area before CAPP can make its breakthrough. Ham (1988) did
ask for more research facilitating a deeper understanding of process
planning tasks 25 years ago and more currently, Xu et al. (2011) invites
industry to play a more significant role in CAPP research.
After all, the chosen problems to solve have their relevance both for
academia and process planners in industry.
6.7 Scientific contribution
As described in chapter 3.2, the unsuccessful implementation of CAPP
in industry seems to be a consequence of a gap between industrial process
planning practice and the academic research. Despite asking for both
deeper understanding of process planning tasks (Ham, 1988) and a
“scientifically rigorous foundation for planning methods” (ElMaraghy,
1993) for more than two decades, this gap is still un-bridged.
The research presented in this thesis provides both the framework of
process planning methods and the refined PPP as contributions to the
establishment of a scientific foundation for process planning methods,
aimed to fill the gap.
The proposed framework of methods and the systematic
representation of the process planning process, provided by the refined
PPP, will also be an indirect support for future efforts in the development
and implementation of CAPP systems.
6.8 General application of the research results
The platform for the research has been manufacturing processes for
gear transmission parts as discussed in section 2.6. Gear manufacturing
processes have some special characteristics associated to the gear, but in
general there are many similarities to manufacturing of other mechanical
elements for engines, pumps, bearings, etc.
Yet no manufacturing processes have been identified that would not
be possible to develop by using the proposed methodologies. The PPP and
DISCUSSION AND CONCLUSIONS | 63
the triangular framework are supposed to be applicable in most
mechanical precision manufacturing environments, especially where
complex process chains appear.
In parallel to the scientific contribution, the research results have a
good potential to bring industrial process planning practice one step
forward. Both a manufacturing process designed by more ingenious
methods and the process planning work itself will gain better resource
utilisation in terms of PUR and project lead time respectively. In addition,
the potential to provide the product designer with relevant manufacturing
information is of great interest.
6.9 Conclusions
As process planning has a decisive impact on product quality and a
considerably high influence on product cost, the possibilities to efficiently
design and evaluate a process plan is of great interest.
This thesis has treated the realisation of process plan design and
evaluation by presenting several key contributors in the form of
systematic methods.
By gathering these methods into the triangular framework, it is
possible to cover the process planning domain from initial process
planning to prediction of the process outcome and finally establishing a
process plan based on transparent and well scrutinised decisions. This
corresponds very well to the first research objective.
It has also shown that each subject contributes with necessary
information for the rest of the framework, including the three
collaborating parties; product design, workshop facilities and
manufacturing technology.
The second objective is satisfied by the refined PPP that provides a
systematic representation of the process planning process. The refined
PPP is a contribution to knowledge about the essence of manufacturing
process planning.
Three methods for capture of process behaviour have been discussed.
One or more of these methods can be used to investigate process
behaviour and bring valuable input data to the development of the
process plan. The characteristics of each method are summarised in
Table 4.
64 | DISCUSSION AND CONCLUSIONS
Table 4: Comparison of three methods to capture process behaviour.
Simulations Design of experiments
Multivariate analysis
When? From pre-development to mature process stages
From early development stages when test equipment is available
Established processes
How? Assign FEM and/or analytical process models
Structured experiments, regression analysis
Gather production data, regression analysis
Resources Software for appropriate modelling. Data base with for example material properties.
Process knowledge and test equipment
Process knowledge production data
Model Process model Product model
Process model Process model
Use Simulation of process behaviour and prediction of process outcome. Prediction of secondary product properties.
Simulation of process behaviour and prediction of process outcome.
Simulation of process behaviour and prediction of process outcome.
Comment Some processes are difficult to model. Requires deep knowledge about the mechanisms behind the process behaviour.
Requires some process knowledge and free time in test equipment
Requires large quantities of high quality production data
65
7 Future work
7.1 Economic aspects
Despite cost being an important aspect when developing a
manufacturing process it has only been discussed in general terms in this
thesis. There is a need to combine the technical aspects of process
planning with the economic aspect in a systematic way. The PPP and
triangular framework adopt the important approach of covering the
complete manufacturing process chain. They facilitate evaluation of how
the aggregated technical contributions from the process steps involved
affect the final part. This must be followed by an analysis of the
aggregated economic contributions to the final manufacturing cost.
One approach for this should be to combine the methods within the
triangular framework with the technical-economic model described by
Ståhl (2007), further developed and implemented by Jönsson, et al.,
(2008a; 2008b). If this integration can be done, it will be possible to
define procedures for optimisation with an objective function on cost.
Another approach is to use system dynamics cost simulations in a
similar way to that Storck and Lindberg (2008) described for industrial
production in a steel plant.
7.2 Representation of the PPP and information flows
The representation of the triangular framework, the PPP and the
information flows are made in a simple way in this thesis to enhance
comprehensibility. However, a representation of activities, information
flows, etc. following a standard such these in the family of IDEF7
modelling techniques, should facilitate easier transfer of the results into
new research and development projects, especially in the context of
computer modelling.
7 www.nist.gov
66 | FUTURE WORK
7.3 The model driven approach
With some exceptions, CAD, CAM and CAPP solutions have not been
used to support the proposed process planning methods. There are many
well-established CAD and CAM systems on the market but their abilities
to manage process planning related characteristics are limited. The
benefit of CAM systems is the aid for creation and visualisation of NC
programs for single machine tools, but not for linked multi-step
manufacturing processes discussed in this thesis.
A challenge for production research is to bridge the gap between CAD
and CAM with systems capable to realize what has been presented in this
thesis, most probably called CAPP. By adopting the research results in
this thesis, it seems to be possible to develop a model driven approach, to
establish a base for representation of process planning related
information. Hedlind (2013) discusses the application of models to
create, represent and use information of products, manufacturing
processes and resources to support process planning. Further model
based approaches with good potential are these for machining tolerance
analysis discussed by Najad, et al. (2012). These approaches in
combination with the proposed methods and structures for process
planning presented in this thesis is suggested to be a subject for future
research, heading for usable CAPP systems.
7.4 A new approach for P-FMEA
Process failure mode and effects analysis (P-FMEA) is a step-by-step
procedure to identify and evaluate risks of failure in a manufacturing
process. The process is analysed regarding potential failures, the severity
of the failures and the effect of the failures.
As process planning is to define a process with a predictable outcome,
the risk assessment should be a part of this work. The DDC2 facilitates
representation of the process chain and has been used to analyse linked
tolerances and process behaviour propagation. The DDC2 also has a
potential to form a basis for process failure analysis by using the same
structure as for tolerance analysis. The information needed for such
assessment must therefore be integrated into the PPP and the DDC2
extended to support this kind of analysis.
67
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