Tecnomatix Plant Simulation in the context of research and ... · 2017-10-24 · 2. Research with Tecnomatix Plant Simulation –An overview 3. Practical example 1 –Plant Simulation

Berlin, 10/24/2017, 14:30 – 15:30

Tecnomatix Plant Simulation in the context of

research and development

B. Denkena, S. Wilmsmeier

Room: Estrel Hall B

© Leibniz Universität Hannover, IFW, Prof. Dr.-Ing. Berend Denkena

Seite 2 | PLM Europe User Conference | 10/24/2017 | S. Wilmsmeier

1. The IFW

2. Research with Tecnomatix Plant Simulation – An overview

3. Practical example 1 – Plant Simulation as part of the digital factory

4. Practical example 2 – Employee competency based simulation

5. Summary

Content



1. The IFW




5. Summary

Content



Associated Centers

Laser Zentrum

Hannover

Institut für Integrierte

Produktion Hannover

Leibniz Research

Center Energy 2050

Production Engineering at the Leibniz Universität Hannover

Photo: sliwonik.com



Employees (PZH wide / IFW only)

Researchers: ca. 260 / 88

Technicians and administrations: ca. 100 / 20

Student assistants: ca. 500 / 211

Students: ca. 800

Machines and equipment

High-quality machine tools and installations

Latest measuring equipment, SEM, laboratories

Cleanroom (350 m2, class 100)

Building

Approx. 22,000 m2 effective surface for office buildings, proving

grounds, lecture and seminar rooms, library, cafeteria etc.

Infrastructure of the Center for Production Technology



Institute of Production Engineering and Machine Tools

Manufacturing processes(Dr.-Ing. Thilo Grove)

Grinding technology

Cutting

Tailored surfaces

Machines and controls(Benjamin Bergmann)

Machine components

Machines and Monitoring

Production systems(Dr.-Ing. Marc-André Dittrich)

Production planning and control

Process planning and simulation



Process planning and simulation

CAD / CAM process chain

NC programming

NC optimization

Development of CAM modules

Virtual control

Analysis of cutting conditions

Visualization of machine kinematics

Production planning and control

Integrated work planning and production control

Process chain optimization

Availability and maintenance

Skill-oriented planning

Technological simulation of the process chain

Interface solutions for coupled simulations

Sustainable production

Department production systems

5-axis simultaneous machining

Work planning and production control



1. The IFW




5. Summary

Content



Planning Control Employees Modelling

Development of a

method for integrated

production and

maintenance

planning

Exploration of

interdisciplinary

planning approaches

Development of

algorithms for optimal

production control by

means of simulation

Exploring measures

of sequencing and

pooling

Development of a

method for

simulation-based

cost-benefit analysis

of training measures

Exploration of further

training potentials

Development of a

method for the fully

automated adaptation

of simulation models

by means of machine

data

Exploration of

adaptive simulation

models

Trainings

Selection of projects with Tecnomatix Plant Simulation

Checklist



1. The IFW




5. Summary

Content




Development of a


production and

maintenance

planning

Exploration of

interdisciplinary

planning approaches

Development of



means of simulation

Exploring measures

of sequencing and

pooling

Development of a

method for

simulation-based




training potentials

Development of a




by means of machine

data

Exploration of

adaptive simulation

models

Practical example 1

Checklist



Problems in production and maintenance planning

Separate consideration of production and maintenance leads to inefficient use of

resources

Digitization offers the possibility to plan maintenance measures at an early stage

Impact of individual maintenance measures on production difficult to quantify

Static methods for production and maintenance planning do not show the

interactions

?



Solution – Integrated production & maintenance planning

Time t

M1

M2

M3

M4

M5

A1

A3

A2

Setup changeoverReserved capacity for

production order Ai

Production-free period

(e. g. weekend)

A4

A3 A4

A1 A2

A2

Maintenance planning (variable)

Starting point of

maintenance measures tSj

Constant parameters

Batch sizes

Sequences of production orders

Machine assignments

Planning period T

t1

Maintenance measure MAj

Maintenance time tjStarting point of maintenance measures tSj

tS1

MA11

1

A1

Buffer Pl

Machine Mp

PS1

Process step

PSk

PS2

A1,

A2

A3,

A4

Machine

assignment

for production

order Ai

PS

1P

S2

Krö/68786 © IFW



Solution – Integrated production & maintenance planning

Time interval ∆t

M3 A1 A2

tS1,mtS1,1 tS1,2 …

m simulation experiments,

n views per experiment

Time t

Planning period T

Observation time tB

MA1

M1 M2

M3 M4 M5

P1

P2 P3

P4

Krö/68788 © IFW



RF: Order variants

LG: Lot sizes

B: Machine utilisation variants

z: Maintenance start time

p: possible planning scenarios

Decision algorithm

Wil/91104 © IFW

Choose machine

utilisation variant B

Choose order

variant RF

Choose lot size

LG

Choose mainte-

nance start time tSj,z

Combine RF, LG,

B and tSj,z

Save planning

scenario p

Maintenance start

times processed?

Machine utilisation

variants processed?

Lot size vatriants

processed?

Order variants

processed?Reduction of

experimental plan

eliminate invalid

planning scenarios

eliminate not

sufficient planning

scenarios

eliminate scenarios

by company

specific „know-

how“

Simulate scenarios

yes

yes

yes

yes

no

no

no

no

p = p+1

B = 1

LG = 1

RF = 1

tSj,z = 1

B = B+1

LG = LG+1

RF = RF+1

z = z+1



0

2000

4000

6000

8000

10000

12000

14000

16000

0 8 16 24 32 40 48 56 64 72 80 88 96 104 112 120

Starting point of maintenance measure tsj

Weekend

Dyn

am

ic M

ain

ten

an

ce

co

sts

D

ire

ct m

ain

ten

an

ce

co

sts

Ind

ire

ct M

ain

ten

an

ce

co

sts

[€*]I II III IV

DynamicMaintenance costs

DirectMaintenance costs

Indirect Maintenance costs

[h]

*Values have been removed due toconfidentiality

ΔK

I. Constant

Maintenance time tj : 12 h

Observation period tB : 120 h

Time interval ∆t : 2 h

Confidence interval : 90 %

Legend

Production-free period

(weekend)

Setup changeover

Maintenance measure

II. Variable

Starting point of

maintenance measure tsj

A4A2 A3

Observation period tB

Time t

A1

Krö/68791 © IFW

Simulation results first research phase



Summary first research phase

Dynamic input

Order variant

Lot size

Machine utilisation variant

Maintenance start time

Static input

Layout related information

Machine behaviour

Output

Optimal production

and maintenance plan

Reduction of unit costs

by up to 7 %

Development of a method for the automated adaptation of simulation models by means of

machine data

Further need for research

Continious manual

adaption needed for

application during

operation time

Dynamic

planning method

Simulation model



Simulation model adaption with machine data acquisition

Decreasing accuracy of the simulation models in use with increasing deployment time

due to changed plan data base

Adaptation of the simulation models time-, personnel- and cost-intensive

Plan data base is updated at the beginning of the simulation

Data base improves with increasing deployment time (edited historical data)

Manual adaptation only necessary for structural changes

Time

Model

validitySelf-parameterizing and

learning simulation model

Traditional

simulation model

Model

creation

Model

usage

Adaption

„Target“

validity

Wil/86787 © IFW

Traditional simulation models

Self-parameterizing and learning simulation models



Integration of machine data in material flow simulation

MDE/BDEMDE/BDEMDE/BDE

MDE/BDE

Evaluation

algorithms

time period

related data

Adaptive simulation model

Wil/86761 © IFW

Interfaces (selection)

OPC-DA/UA,

DDE, MCIS, DNC,

FOCAS 1&2,

NIO Interface,

Open Core Interface,

Modbus

Protocols (selection)

FTP, TCP

SQL

Web Applikation

SQL query

Create

XML files

time

related data

MDE/BDE



Data preparation and automated model adaption

Simulation model

Tests of goodness

of fit

DataFit

Calculate

percentage

distribution

Process

data

Wil/86798 © IFW

Transfer of

results

Machine 1

95 %2 %3 %

Machine 1

Dura

tio

n

Dis

tan

ce

Data logger

ok

wa

ste

rew

ork

ing

Fre

qu

en

cy

Stored

data

Machine 1

Failure

duration

1000

80

5680

...

...

9500

7644

Failure

distance



Adaptive and integrated

maintenance and production planning

Simulation-based decision supportWeb-based acquisition and visualisation

Simulation model

Production

planningMaintenance

planning

Real production system

1 Plan data, real data from MDC and PDC systems

2

2

Selected real data

3 Prepared database (e.g. cycle and setup time,

machine failure behaviour)

5

4

Simulation results (e.g. failure follow-up costs)

Decision for real system

4

5

Data preparation

1

3

Wn/72107 © IFW

0

1000

2000

3000

4000

5000

6000

7000

8000

2 10 18 26 34 42 50 58 66 74 82 90 98 106[h]

Maschine 2

Startzeitpunkt IH-Maßnahme

Maschine 1

Maschine 3

[€]

Au

sfa

llfo

lge

ko

ste

n Wochenende

Startzeitpunkt Instandhaltungsmaßnahme

Ausfa

llfo

lge

ko

ste

n

Data acquisition and selection

Visualisation of results

Legend

Time of maintenance measure

Failu

re f

ollo

w-u

p c

osts

Machine 1 Machine 2

Machine 3

Weekend

0

7

14

21

28

5 10 15 20 25 30 35 40 45 50 55 60

Fre

qu

en

cy

Failure distance

Histogram short-term failure distance



Simulation model

Summary second research phase

Simulation model

Dynamic Input

Order variant

Lot size

Machine utilisation variant

Maintenance start time

Machine behaviour &

system status

Static Input

Layout related information

Output

Optimal production

and maintenance plan

Valid simulation model without cost

and time consuming manual efforts

Shortening of the settling phase

due to initialized buffers

Development of a method for the (fully) automated creation of simulation models based on

layout scans

Requested research project

Temporarily manual

adaption needed if

layout changes

Dynamic

planning method



1. The IFW




5. Summary

Content




Development of a


production and

maintenance

planning

Exploration of

interdisciplinary

planning approaches

Development of



means of simulation

Exploring measures

of sequencing and

pooling

Development of a

method for

simulation-based




training potentials

Development of a




by means of machine

data

Exploration of

adaptive simulation

models

Practical example 1

Checklist



Stable connection between the competence development of employees and

innovative ability of companies

Benefit of trainings in companies can only be estimated

Mathematical models allow a quantitative description of the entire workforce

performance of a company

Individual employees or their characteristics and abilities are not explicitly taken

into account

If companies are able to flexibly vary key decision-making variables, companies can

then develop their business and situation-specific optimal training strategy.

Initial considerations for the SAPA project

Initial hypothysis



System behavior of a production system is often too complex for it to be fully

captured and evaluated

Material flow simulations represent the system behavior of a production in a

model (using appropriate software)

Employee competencies are elements of a production system and can be

represented in the material flow simulation

If employee competencies are represented, a change in competencies (eg. by

trainings) leads to a change in the production system

Thus planning processes can be supported or optimized, by using simulation

approaches

Competence-based simulation



Example: Material flow simulation



Example: Production program

0

2

4

6

8

10

12

14

Quantity

of pro

ducts

Simulation time

Product 1

-

Product 2 Product 3



0

2

4

6

8

10

12

14

16

Quantity

of pro

ducts

Simulation time

Product 1

Output

-

Example: Reaction of the production system



Example: Reaction of the production system

0

2

4

6

8

10

12

14

16

18

Quantity

of pro

ducts

Simulation time

Stock

-



0

2

4

6

8

10

12

14

16

Quantity

of pro

ducts

Simulation time

-

Product 1

Stock

Output

Example: Further training of employee 1



Example: Further training of employee 2

0

2

4

6

8

10

12

14

16

18

Quantity

of pro

ducts

Simulation time

-

Product 1

Stock Output



1. The IFW




5. Summary

Content



The IFW uses Tecnomatix Plant Simulation for research and improvement of

production systems

Production planning and control, the employees and simulation techniques can

be investigated with Plant Simulation

Digitization allows the integration of Tecnomatix Plant Simulation as part of the

Digital Factory into the daily planning processes

Practical implementations of developed methods show the reproducibility of

simulated results

Summary

Planning Control Employee Technique

Tecnomatix Plant Simulation

Checklist



Thank you for your attention!

Documents

Tecnomatix Plant Simulation in the context of research and ... · 2017-10-24 · 2. Research with Tecnomatix Plant Simulation –An overview 3. Practical example 1 –Plant Simulation