Characterization and recognition of particles for improving cleanability in automotive production

INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING Vol. 14, No. 6, pp. 977-984 JUNE 2013 / 977

© KSPE and Springer 2013

Characterization and Recognition of Particles forImproving Cleanability in Automotive Production

Phan Quoc Bao1 and Sung Lim Ko2,#

1 Department of Advanced Fusion Technology, Konkuk University, 120, Neungdong-ro, Gwangjin-gu, Seoul, South Korea, 143-701 2 Department of Mechanical Design and Production Eng., Konkuk University, 120, Neungdong-ro, Gwangjin-gu, Seoul, South Korea, 143-701

# Corresponding Author / E-mail: [email protected], TEL: +82-2-450-3465, FAX: +82-2-447-5886

KEYWORDS: Burr, Deburr, Cast, Chip, Cleanability

The complicated systems for casting, anodizing, machining (drilling, milling, turning), high-pressure water jet (HPWJ) deburring, and

brushing processes create many different kinds of particles: burr, cast and chip. These particles lodge inside the transmission, engine

and crankshaft, and then damage the functions of these components, posing risks to drivers. Many researchers have endeavored to

minimize these negative effects without clear understanding of the main sources. This investigation aims at clear recognition of these

problems and suggests solutions to minimize or remove each kind of particle with high reliability based on experimental databases.

By understanding the formation mechanism of each particle, a particle classification algorithm is developed and verified by

comparison between the results from simulation and experiment. This research contributes to building a classification algorithm for

specific parts like transmissions and engines by suggesting the source of each particle which is very important in cleanability.

Manuscript received: September 27, 2012 / Accepted: March 24, 2013

1. Introduction

Recently, automotive recalls have appeared more and more often.

Most people recognize problems from what they can see, but other

more serious problems may come from inside the engine, transmission

or crankshaft. These parts have common design elements such as oil

lines that are formed from drilling intersection holes or inclined holes,

cast surfaces, drill holes with milled surfaces, and milling surfaces with

cast surfaces. All of these are also affected by casting technology which

produces very small casting particles.

Inside an engine or transmission, there are many different kinds of

sensors to control oil pressure or motion signals. There is also relative

motion within these components between shafts and drilled holes. Small

particles in the lubricant induce direct contact with metal surfaces,

which produces a negative effect on the surface or connection between

parts. As the speed of the car increases, more serious particle damage

to the working surface and functions of main parts can be incurred.

Fig. 1. shows particles extracted from transmission by grid filter.

Cleanability is defined as the sum of the particles that circulate inside

the parts. Particles are mainly composed of burrs, cast particles and

chips, which require expensive inspection, deburring, cleaning by high

pressure water jets and other specific deburring methods to be removed.

Unfortunately, up to now these problems have not been reviewed in

terms of their real effects on cleanability and function of equipment.

Therefore, the objective of this work is to build a basic method of

recognition and classification of particles for supervision of

cleanability. The results of classification may suggest very useful

information on current status of cleanability. Each particle includes

information on its source. For example, the specific geometry of a burr

may show the location of its formation, which necessitates an

appropriate deburring method.

2. Mechanism of particles generation

The key points of characterization and recognition of the particles

DOI: 10.1007/s12541-013-0129-4

Fig. 1 Particles extracted from transmission by grid filter

978 / JUNE 2013 INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING Vol. 14, No. 6

come from the understanding on the formation mechanism of each kind

of particles.

The work in this research is concerned with the burrs and cast

particles formed at windows and cast surfaces. In Fig. 2, we define four

main areas inside a transmission part to investigate (Fig. 2(a)).

- Cast surface (Fig. 2(c))

- Machined surface in drilling and milling

- Window area between machined and cast surface (Fig. 2(b))

- Edge in drilling at point 7 and milling at point 8

Burrs are formed from plastic deformation during machining. The

formation of burrs happens at the end of a cut, especially in milling and

drilling. Burrs are formed as chips remaining along a workpiece.

Gillespie1 classified four burr types according to their formation

mechanisms: Poisson burr, rollover burr, tear burr and cut-off burr.

Chu, C.H. and Dornfeld2 named exit burrs as likely to form along edges

where the tool leaves the work part.

The burr particles collected by filters are the ones detached from the

workpiece during operation in a transmission or engine. The detached

burrs were unstable burrs that remained after machining or deburring.

Stable burrs on a workpiece are acceptable if they do not cause any

problems in function as in Fig. 3, at the window. To characterize the

detached burrs, it is necessary to investigate the mechanism of

formation of unstable burrs.

Fig. 3 shows stable burrs at the intersection edges between a cast

surface and drilled hole. These burrs will not be detached during

operation. If we increase the pressure of the high-pressure water jet

with which the surfaces are treated, or slow down the feedrate of the

jet nozzles or increase brushing time, it may damage the cast surface

near edges, and even the drilled surfaces as well. As a result, more

particles can be generated. Many experiments were carried out to

minimize the size of these kinds of burr and the damage to the cast

surface at the same time.

In contrast, the burrs in Fig. 4 seem to be unstable. These will be

detached during operation and become particles in the circulating oil

flow. Fig. 4(a) shows a window burr at the entrance of a window in the

drill feed direction, at location 5. This burr was bent to the side when

the drilling tool moved through the window. These types of burrs can

be stable burr or unstable. To remove these particles by brushing, the

type of brush material, the diameter of the brush, and the diameter of

filaments in the brush, are investigated.

Fig. 4(b) shows the feed direction burr produced along the slot in

the drilling process, at location 7. This burr is quite common in burr

and deburring researches. But this burr is formed at the edge of the

intersection between casting and drilling. The edge contains both stable

and unstable burrs. The stable burrs mostly have very small size. The

unstable burrs are removed by changing feedrate of the brush.

Fig. 2 Samples with 4 areas: casting surface, machined surfaces

(drilling and milling), window area between casting surface and

machined surface, and edges at point 7 and point 8

Fig. 3 Stable burrs formed along the edge of a workpiece even after

deburring process, location 2 and 3, in Fig. 2 Fig. 4 Unstable burr after machining or deburring process

INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING Vol. 14, No. 6 JUNE 2013 / 979

Fig. 4(c) shows a window burr at the exit of a window along the

drilling direction, at location 1. From experiments, two kinds of burrs

are made, which are stable and unstable. Similar to Fig. 4(a), it

becomes possible to reduce these burrs by changing brush deburring

conditions. Removing burrs at the location entrance and exit of the

window as in locations 1 and 5 is important. They can be detached by

oil pressure supplied to the system. In both of the two cases in Fig. 4(a)

and Fig. 4(c), the burrs are bent inside a narrow window, which makes

it difficult for brush filament to enter the window to remove the burrs.

By deploying a long and small-diameter nozzle for high-pressure water

jet deburring or by using proper filament and conditions in brushing,

burrs can be removed properly.

Fig. 4(d) hows the burr formed at the edge of a milled surface and

cast surface, at location 8. Stable burrs cannot be removed due to high

stiffness even with high pressure water jets and face brushing, which is

acceptable. The unstable burrs in Fig. 4(d) can be removed.

Another very dangerous area is the intersection of a machined

surface and a cast surface. Unstable edges are formed along these

intersections. These edges are damaged easily by high pressure water

jets as shown in Fig. 4(e). This broken edge is very weak and expected

to be damaged more during operation, which produces the particle as

shown. We defined them as burr, for one side was deformed in

machining and another side is a cast surface, at location 4. It was

clearly found that the edges made by the machined surface and cast

surface contains very weak burrs and cast particles.

Cast particles: A casting defect is an irregularity in the metal

casting process that is undesired. Some defects can be tolerated,

otherwise they must be eliminated. They are classified into five main

categories: gas porosity, shrinkage defects, material defects, pouring

metal defects, and metallurgical defects.3 After a cleaning process with

a high pressure water jet, some particles still remain on the surface.

They have very small size and are called as cast debris shown in Fig.

5(a). When a pressurized water jet hits the small casting defects, it may

damage the defect, which produces small particles. Most of them are

collected during cleaning, but some particles remain among the defects

or on the surface.

Other invisible casting particles are made from the large surface

defects in casting as in Fig. 5(b), which has very high potential to be

detached partially during the high pressure water jet process in cleaning

and deburring. When a pressurized water jet hits the damaged cast

surface, very thin and wide cast particles are produced as in Fig. 5(b),

which shows as cast surface. Usually these particles are irregular in shape

and black in color. This means it becomes very important in the casting

process not to include cast defects from the viewpoint of cleanability.

Chips are formed from machining (drilling or milling), which

produces many different shapes: discontinuous, wavy, sawtooth,

continuous, segmented, serrated or sheared. Corinne L. Reich-Weiser4

optimized the type of chip produced by varying process parameters

such as feed, speed, depth of cut, and lubrication. Some insight is

gained as to how these parameters affect chip geometry and size. Chips

are removed during machining and deburring. Due to their geometry,

some chips cannot be removed.

Fig. 6 shows some typical chip shapes, and some have serration

marks on the surface and others have smooth, curled or flat surfaces.

Due to the large size of the chip, it is lodged inside at trap positions.

It may create serious damage to the function inside. S. Garg, D.

Dornfeld and K. Berger5 did research on the interaction between the

chip morphology and workpiece landscape to build a chip optimization

model to improve cleanability. It was very useful for design for

cleanability could solve the industrial cleaning problems for

transmissions and automotive cylinder heads. Corinne Lee Reich-

Weiser6 investigated how chip-related contamination could be

controlled by varying milling and drilling cutting parameters such as

feed, speed, depth of cut, and lubrication.

3. Standard for particle classification

Many authors have paid much attention to how to classify different

shapes of particles, texture, shape and color. Particle characteristics and

their development, features of surface damage, wear particle shape and

other some characteristics have been described. N. K. Myshkin and A.

Ya. Grigoriev7,8 developed parametrical models and methods of

classification for morphological texture, shape and color. Kun Xu and

A. R. Luxmore9 used neural networks for classification of shape and

texture to carry out automatic analysis of single particles. Z. Peng and

T. B. Kirk10-14 built an automatic wear-particle classification using

neural networks by characterizing boundary morphology and surface

topology of the wear particles. Surapol Raadnui15 built an expert

system for wear particle analysis for particle morphological

Fig. 5 Generation of cast debris and cast surface

Fig. 6 Variety of chip shape


characterization regarding size, shape, surface texture, edge detail and

thickness. Among one of the key manufacturing technologies16 clean

production system may require the particle classification to control the

cleanliness of oil.

Surface texture is outlined qualitatively by several characterizations

of shape and positions of irregularities, which can be spherical, linear,

facet random. Shape is the simplest and clearest way to define all

morphological features like object boundary (serrated, roundness,

straight line, curvatures). Color is another very important point to

define particles, but there is much noise due to the density of lightning

systems, and reflected, unstable energy sources. Often, these systems to

set standard classifications with color are very expensive. In this

research, we mostly concentrate on classification by shape. Sometimes

it is not possible to confirm exactly which group a particle may belong

to, especially in case of burrs. SEM and microscopy help to specify

features of each particle type and to define particle types clearly, as in

Fig. 7. There is machined part on surface of burr as in Fig. 7(a), but

burr is not shiny as in the chip in Fig. 7(b). A shiny chip surface reveals

information about the friction behavior on the tool rake face which is

not in burr. In the case of cast particles as in Fig. 7(c), there is no

machined surface, only cast surface.

The standards of particles can be summarized as followes based on

the formation mechanism:

Burr-1: Long particles are generated along an edge with radius R of a

drilled hole and a straight particle along edge in milling as

shown in Fig. 7(a).

Burr-2: Short particles are generated along an edge in drilling and

milling, as part of burr-1, which is broken partly in the

deburring process as in Figs. 4(b), (d) and (e).

Burr-3: Small particles are generated from unstable burrs as in Figs.

4(a), and (c).

Cast-1 (cast debris): Small particles are generated from the defect of a

cast surface during the deburring process as in

Fig. 5(a).

Cast-2 (cast surface):Wide and thin particles are generated from the

defect of a surface during the deburring process as

in Fig. 5(b).

Chip: Wide and large particles, larger than burrs and cast particles.

Filament of brush: Broken off during the brushing process. In the

deburring process, filaments contact sharp burrs,

rough edges or surfaces, as in Fig. 8.

To measure the parameters of each particle, we remove the noise in

an image of a particle through image processing and obtain boundary

characteristics to set up standard classification as in Fig. 9.

Fig. 10 shows a definition of shape parameters for the particle

classification algorithm. L and W are defined as length and width. L is

obtained as longest line within particle and W is determined in

perpendicular direction to L.

This research concentrates 3 independent parameters: A, L, and W,

plus aspect ratio and area ratio to make the classification simple. They

are: length (L), width (W), aspect ratio (R, aspect ratio of width/length

defined as R = W/L), area (A, the real area which has white color) and

area ratio (AR, is defined as AR = A/(L.W)).

The absolute number for classification can be determined

efficiently. Using automatic measurement system, five parameters of

Fig. 7 (a) Burr, (b) Chip and (c) Cast, observation of surface by SEM

Fig. 8 Filament of brush broken during deburring, observed by SEM

Fig. 9 Standard of particle classification after image processing to get

clear shape/boundary of particles.

Fig. 10 Definition of shape parameters in (a) burr, (b) cast, (c) chip and

(d) filament of brush


each particle can be measured and databased using Excel with manual

particle classification. The boundary values between different types of

particle can be decided as a critical value of parameter.

The result of image processing and measurement of each particle

after removing noise inside a boundary is shown in Fig 10 and calculated

in Table 1. After the analysis of shape, it is found that five geometric

parameters are useful to classify burrs, cast particles and chips.

4. Algorithm of Classification

With more than 1500 particles measured and evaluated from the

filter by experts, we built a classification algorithm, shown in Fig. 11.

(1) In case of W ≤ W1 (straight) or AR ≤ AR0 (curled or spiral),

particles are classified as brush filaments as in Fig. 8. W1 is determined

as the thickness of the filament. If the number of these particles is

remarkable, we should pay attention to the brush deburring process.

Different brush deburring conditions or other brush materials can be

applied to increase the deburring or cleaning performance.

(2) In case of W1 < W ≤ W2, particles are classified as cast debris as

in Fig. 5(a). These particles are the result of the casting process and

cleaning. We should pay attention to material preparation or surface

treatment processes like anodizing or cleaning with ultrasonic cleaning

or manual cleaning.

(3) In case of W > W2:

• If the length L ≥ L1, particles are classified as chips. This means

the size of all chips is larger than burrs and cast particles as in Fig. 6.

We should concentrate on the washing with high pressure water jet,

brushing process, assembly or moving transmission to remove chips.

Furthermore, it is also required that cutting conditions are controlled to

minimize the size of chips so they can be taken out easily, while

avoiding chip lodging inside some traps.

• If the length L2 ≤ L < L1, particles are classified as cast surface

particles as in Fig. 1(b). They are generated from the defect area of the

cast surface by high pressure water jets or brushing processes. The

particle is wide and thin with black color. The size of particle depends

on the defect of the casting surface and the parameters from high

pressure water jet process (nozzle, pressure, stand-off distance, feed).

• If the length L3 ≤ L < L2, particles are classified as burrs and cast

surface particles. Most particles will be within this range, which is the

result of a combination of many processes such as machining, HPWJ,

surface treatment, and brushing. If we increase the pressure to

minimize burrs, it may damage the surface of material or enlarge the

defected areas on a cast surface. Based on the observation of burr and

cast particles, an algorithm is suggested as follows:

■ If the aspect ratio R ≥ R1, particles are cast surface particles as in

Fig. 7(c) and classified as cast-2 (small) in Fig. 9.

■ If the aspect ratio R2 ≤ R < R1:

○ If the area ratio AR ≥ AR1, particles will be burrs in Fig. 7(a),

classified as (burr-1) Fig. 9.

○ If area ratio AR0 ≤ AR ≤ AR1, particles will be cast surface

particles as in Fig. 5(b) and classified as cast-2 (small) in Fig. 9.

This cast surface particle is larger than cast debris.

■ If the aspect ratio R < R2, particles will be the burrs in Fig. 4(b)

(burr-2)

Table 1 Automatically measured shape parameters (L, W, A) of sample

particles

ClassLength

(L)

Width

(W)

Area

(A)

Aspect ratio

(R)

Area ratio

(AR)

Burr 435 331 129586.5 0.761 0.9

Burr 437 267 34609 0.611 0.296617215

Burr 437 341 86584 0.780 0.581034379

Cast surface 416 363 113256 0.873 0.75

Cast surface 431 290 83743.3 0.673 0.67

Cast surface 440 331 51334 0.752 0.352471848

Cast debris 54 45 1864 0.833 0.767078189

Cast debris 82 70 3067 0.854 0.534320557

Chip 849 112 53133 0.132 0.558777133

Chip 1605 489 251363 0.305 0.320270882

Chip 1005 820 376448.8 0.816 0.4568

Brush 1300 600 42930 0.046 0.055038

Brush 3000 800 93120 0.267 0.0388

Brush 1272 30 23072 0.024 0.866667

Fig. 11 particle classification algorithm by geometry Output: % of burr, cast, chip; solution to minimize, success rate; graph trend


• If the length L < L3, particles will be burrs (burr-3). These burrs

are very small as in Fig. 4(a). The small size particles are not

necessarily better regarding cleanability. If the size decreases, particles

can enter critical clearance areas, which cause failure in body valves.

To determine the parameters W1, W2, L1, L2, L3, R1, R2 and AR1,

it is required to measure the particles correctly.

5. Testing and simulation

First of all, the parameter design of the Taguchi method is used to

determine the significant parameters and set the proper level for each

parameter based on the results of analysis of variance (ANOVA) and

the main effect percentage. Then, we search for the proper values and

finalize the best parameters for our algorithm. The flow diagram of

searching parameters for the particle classification algorithm is shown

in Fig. 12.

Experimental procedure for collecting particles: After deburring and

cleaning process by high pressure water jet and brushing, the samples

(transmission, engine) were operated for a while. And then the

particles, which were generated during operation, are collected. By

filtering the transmission oil or engine oil, the particles are collected

through filter following standardized method at automotive factory.

The grid size of filter will be determined according to the requirements

of cleanability.

To perform the test, we selected 2 different filters as a result of 2

different deburring processes in transmission production line, and

called them group 1, and group 2. The working conditions of these 2

groups are shown in Table 2.

Seven independent parameters were chosen: W2, L1, L2, L3, R1, R2

and AR1. For 7 two-level factors, we need an array that has at least 7

two-level columns. Table 3 shows the level of each variable used in

testing and simulation. The tests were conducted to identify the factors

causing the best success rate (SR) and also to determine the effects of

each parameter on the classification of particles.

The final success rate of the classification was retained as the

response parameter. We did simulations with 2 different groups, shown

in Table 2. Number of tests was reduced using the Taguchi analysis of

variance (ANOVA) technique to designate the best success rate. Table

4 shows the planned L-8 experiments with 2 groups of samples.

From the ANOVA results, a proper value for each variable is

selected. The contribution effect of each of the parameters on the

process output is shown in Table 5. R1 contributed 38%, and has the

main effect on the result of classification, and the other variables

contributed as follows: L2 (20.96%), L3 (12.87%), AR1 (11.32%), R2

(8.02%), L1 (4.79%), W2 (3.23%).

With these proper values, we tested each group and obtained a

success rate of 95% for group 1 and 92% for group 2. The difference

in values of success rate between group 1 and group 2 are from errors

in human expert recognition to set up standards for classification. From

Fig. 12 The flow diagram of searching optimum parameters for

Particle Classification Algorithm

Table 2 The working conditions of group 1 and 2

Particle

number

High pressure water jet Brushing

Pressure

(bar)

Feed

(mm/s)

Brush

Dia. (mm)

Filament

Dia. (mm)

Group 1 1000 550 13.3 18 0.4

Group 2 1500 500 26 17.5 0.3

Table 3 Level of each variable used in the testing and simulation

Variable Level 1 Level 2

W2 (um) 80 89

L1 (um) 800 770

L2 (um) 570 594

L3 (um) 400 415

R1 0.68 0.61

R2 0.53 0.48

AR1 0.3 0.2

Table 4 L-8 experiments, with 2 trial test results from group 1, 2

Test W L R AR SR (%)

W1 W2 L1 L2 L3 R1 R1 AR1 Group 1 Group 2

1 30 80 800 570 400 0.68 0.53 0.3 93.00% 89.60%

2 30 80 800 570 415 0.61 0.48 0.2 95.00% 92.00%

3 30 80 770 594 400 0.68 0.48 0.2 91.00% 91.20%

4 30 80 770 594 415 0.61 0.53 0.3 93.00% 91.20%

5 30 80 800 594 400 0.61 0.53 0.2 93.00% 91.20%

6 30 80 800 594 415 0.68 0.48 0.3 92.00% 90.40%

7 30 80 770 570 400 0.61 0.48 0.3 93.00% 92.00%

8 30 80 770 570 415 0.68 0.53 0.2 94.00% 89.60%

Table 5 Proper value from ANOVA

No FactorsLevel

Description Level Contribution

%

Effect

1 W2 80 um 1 0.050 3.23

2 L1 800 um 1 0.074 4.79

3 L2 570 um 1 0.324 20.96

4 L3 415 um 2 0.199 12.87

5 R1 0.61 2 0.600 38.81

6 R2 0.48 2 0.124 8.02

7 AR1 0.2 2 0.175 11.32


the test, we found that there are some difficulties in the classification,

especially at the boundaries of each particle between the cast surface

and the burr, and the chip and the cast surface.

With the different values for the effect of parameters in Table 4, we

place the priority of variables from the highest effect percentage to the

lowest in our searching as R1, L2, L3, AR1, R2, L1, W2. The final values

are in Table 6.

Finally, we can obtain the classification at the final values shown in

Table 7, with a success rate up to 98% for group 1 and 94.40% for group 2.

6. Result and Discussion

At the final values, we tested 2 groups and obtained the graphs

shown Fig. 13.

Burr (58.5%, 51%): We can reduce the amount of burrs by HPWJ,

brushing, changing machining conditions, or deburring tool. The

number of burr particles is largest among all particles. Therefore,

solution of burr minimization must be seriously considered for the

whole production line.

Cast surface (14.15%, 19.33%): The casting process for parts must

be well prepared in order to not include surface defects. It is especially

important to control the pressure of water jet to reduce the impact on

defects on the surface from casting.

Cast debris (6.60%, 7.56%): It is necessary to practice much care in

casting technology, especially in cleaning process, where cast debris

must be removed or not generated during HPWJ.

Chip (2.83%, 10.92%): The maximum size of the chips found was

1605 um. Most chips are large and are evacuated from parts after

machining by HPWJ and air jetting.

Brush filaments (17.92%, 10.93%): A proper brush type must be

selected to replace old brushes at the right time. Brushing can reduce

the size of burrs and clean surface after HPWJ. If a brush remains

inside a transmission, it must be removed in cleaning.

7. Conclusion

All databases for this research are taken from experiments with auto

transmission. With other materials, we would collect and evaluate the

information in the same way to classify the particles.

In this research, we recognized and characterized each type of

particle, to evaluate the percentage of each type found in a system. We

found the sources of 3 kinds of burrs, with 3 clear cases of cast

particles, chips and brush filament. Edges at the intersection between

casting and machining are very weak, so many particles detached. The

classification shows the importance of not including defects in casting

surfaces during HPWJ.

The most important contribution is the development of the particle

classification algorithm for 9 cases. This classification is based on the

source of particle generation mechanism. Finally, if particles from

filters are well classified, we can diagnose the production process to

reduce particles thanks to the understanding of their origin.

ACKNOWLEDGEMENT

This research was supported by Leading Foreign Research Institute

Recruitment Program through the National Research Foundation of

Korea (NRF) funded by the Ministry of Education, Science and

Technology (MEST) (2011-00260).

REFERENCES

1. Gillespie, L. K., “The Formation and Properties of Machining

Burrs,” Journal of Manufacturing Science and Engineering, Vol. 98,

No. 1, pp. 64-74, 1976.

2. Chu, C. H. and Dornfeld, D. A., “An Experimental Study on Edge

Table 6 Final values of variables R1, L2, L3

R1 L2 L3

SR (%)

Group 1 Group 2

0.53 555 390 98.00% 94.40%

0.49 560 499 96.20% 94.00%

0.61 570 415 95.00% 92.00%

Table 7 Determination of all parameters with success rates 98%

(group 1) and 94.4% (group 2)

Ranking value of classification Result

Success rate

(SR%)

Group

1

Group

2

W ≤ 30 or

AR ≤ 0.07Brush

98% 94.40%

30 < W ≤ 80Cast

debris

W > 80

L ≥ 800 Chip

555 ≤ L < 800Cast

surface

390 ≤ L < 555

R ≥ 0.53Cast

surface

0.48 ≤ R < 0.53

AR ≥ 0.2 Burr

0.07 < AR< 0.2Cast

surface

R < 0.48 Burr

L < 390 Burr

Fig. 13 Graph of classification for particles inside a transmission at the

final values of parameters


Defects in End Milling of Al-6061,” CODEF/NSF, 1999.

3. Wilby, A. J. and Neale, D. P., “Defects introduced into Metals

During Fabrication and Service,” Materials Science and Engineering,

Vol. III.

4. Corinne, L. R. W. and Dornfeld, D. A., “Experimental Investigation

of the Influence of Machining Parameters on Chip Geometry for

Enhanced Cleanability,” Laboratory for Manufacturing and

Sustainability, UC Berkeley, 2006.

5. Garg, S., Dornfeld, D. A., and Berger, K., “Formulation of the chip

cleanability mechanics from fluid transport,” International Conference

on Burrs - Analysis, Control & Removal, 2009.

6. Corinne, L. R. W. and Dornfeld, D. A., “An Experimental

Investigation on the Inuence of Process Parameters During Chip

Formation,” Laboratory for Manufacturing and Sustainability, UC

Berkeley, 2006.

7. Myshkin, N. K. and Grigoriev, A. Y., “Morphology: Texture, Shape

and Color of Friction Surfaces and Wear Debris in Tribodiagnostics

Problems,” Journal of Friction and Wear, Vol. 29, No. 3, pp. 192-199,

2008.

8. Myshkin, N. K., Kong, H., Grigoriev, A. Y., and Yoon, E. S., “The

use of color in wear debris analysis,” Wear, Vol. 251, No. 4, pp. 1218-

1226, 2001.

9. Xu, K. and Luxmoore, A. R., “An integrated system for automatic

wear particle analysis,” Wear, Vol. 208, No. 1-2, pp. 184-193, 1997.

10. Peng, Z. and Kirk, T. B., “Automatic wear-particle classification

using neural networks,” Tribology Letters, Vol. 5, No. 4, pp. 249-

257, 1998.

11. Peng, Z., Kirk, T. B., and Xu, Z. L., “The development of three-

dimensional imaging techniques of wear particle analysis,” Wear,

Vol. 203-204, pp. 418-424, 1997.

12. Kirk, T. B., Panzera, D., Anamalay, R. V., and Xu, Z. L., “Computer

image analysis of wear debris for machine condition monitoring and

fault diagnosis,” Wear, Vol. 181-183, No. 2, pp. 717-722, 1995.

13. Peng, Z. and Kirk, T. B., “Wear particle classification in a fuzzy

grey system,” Wear, Vol. 225-229, No. 2, pp. 1238-1247, 1999.

14. Peng, Z. and Goodwin, S., “Wear-debris analysis in expert systems,”

Tribology Letters, Vol. 11, No. 3-4, pp. 177-184, 2001.

15. Surapol, R., “Wear particle analysis - utilization of quantitative

computer image analysis: A review,” Tribology International, Vol.

38, No. 10, pp. 871-878, 2005.

16. Shan, Z., Qin, S., Liu, Q., and Liu, F., “Key manufacturing

technology & equipment for energy saving and emissions reduction

in mechanical equipment industry,” Int. J. Precis. Eng. Manuf., Vol.

13, No. 7, pp. 1095-1100, 2012.

Documents

Characterization and recognition of particles for improving cleanability in automotive production