Process Capability

Materials

www.elsevier.com/locate/matdes

Materials and Design 27 (2006) 364–372

&Design

Statistical process control in machining, a case study for machinetool capability and process capability

Ali Rıza Motorcu, Abdulkadir Gullu *

Department of Mechanical Education, Technical Education Faculty, Gazi University, 06500 Teknikokullar/Ankara, Turkey

Received 12 July 2004; accepted 9 November 2004

Available online 10 December 2004

Abstract

In this experimental study some statistical calculations have been made to eliminate quality problems such as undesirable toler-

ance limits and out of circularity of spherodial cast iron parts during machining. X–R control charts have been constructed on the

data obtained from this manufacturing to discover and correct assignable causes, so that the machine capability (Cp) and the pro-

cess capability (Cpk) can be determined.

In order to compare design tolerance on working drawings and attained tolerances on workpieces after machining five mass pro-

duction lines were set up in a medium sized company. The results obtained from five X–R control charts and the data gathered from

all production lines were processed and evaluated. At this stage of the study, it was observed that some parts were oval and out of

tolerance limits, machines and processes were insufficient and production was instable. Through machining data and follow up stud-

ies some assignable causes for faulty workpieces were discovered, and ovalness and out of tolerance limits problems were eliminated.

In addition to these developments, surface roughness of machined workpieces was improved.

All these activities show that in small or medium sized companies statistical quality control can be useful component of produc-

tion provided that sufficient finance and qualified personal are utilized.

� 2004 Elsevier Ltd. All rights reserved.

Keywords: Statistical quality control; Machine tool capability and process capability; Machinability; Cutting parameters

1. Introduction

High quality production provides some advantages

such as reduced scrap or remachining and increased

market share. For this purpose there are some require-

ments to be met. First of all the organization shouldbe cooperative and the quality should come first. On

the other hand, in order to meet quality requirements

of final product, quality should be achieved at every

stage of production [1].

Another way of achieving good quality during pro-

duction is to use the statistical period techniques at

0261-3069/$ - see front matter � 2004 Elsevier Ltd. All rights reserved.

doi:10.1016/j.matdes.2004.11.003

* Corresponding author.

E-mail addresses: [email protected] (A.R. Motorcu), agullu@

gazi.edu.tr (A. Gullu).

every stage of production. If the production is statisti-

cally under control the process can continue and there

is no need for a change in the process. However, if it

is not statistically under control, the assignable causes

should be discovered and removed from the process.

Statistical quality control methods apply statistical prin-ciples and techniques at every stage of design, manufac-

turing, and servicing. Statistical quality control methods

are quite different from traditional methods and they

have made great contribution to improvements in com-

panies dealing with mass production. In traditional

methods, the product is manufactured first and then it

is checked to determine whether it meets the quality

requirements. The product that does not meet the qual-ity requirements is rejected and sent back to the ma-

chines for remachining or correction otherwise it is

mailto:[email protected]

mailto:agullu@

A.R. Motorcu, A. Gullu / Materials and Design 27 (2006) 364–372 365

thrown away as scrap. If faulty products are too much,

in order to eliminate the assignable causes or the prob-

lem necessary corrections are made by examining pro-

duction period (Fig. 1a). However, statistical quality

control is the vital part of production. Instead of check-

ing the finished product after production, it is applied at

every period of production. If this period is under con-

trol, the next period is considered, otherwise the assign-able causes are discovered and corrected (Fig. 1b).

Today, in order to preserve and improve quality com-

panies perform their work in three phases: firm organi-

zation phase, process phase and application/

performance phase [2]. The priority orders of informa-

tion and data in these phases are control, diagnosis,

and planning respectively. Determination of how this

information is distributed and equalized on organiza-tional level is of vital importance for the success of com-

pany [2].

Quality improvement processes and calculations are

generally carried out at design stage or at work stages.

At these stages the standards for the product are quality

oriented and customer requirements are taken into

account.

But at organizational level, the tasks and the proce-dures at process level should also be organized together.

The standards desired for the product may have some

criteria such as exact size, material composition, and

production time. All employees should understand the

necessity of quality and employ required techniques at

their daily works [2].

Even tough quality is a must in almost every com-

pany, it receives limited attention by managements.Some firms use traditional methods and some of them

prefer statistical quality control methods. There are

plenty of research work concerning quality improve-

ment. Control process and quality management philoso-

phy was defined according to quality requirements by

Jabnoun [3] and it was pointed out that not enough

No

Yes

Does it meet thespecifications?

Scrap or remachining

Adjust the period

Customer

Product

Production Period

(a) (b

Fig. 1. Quality control methods: (a) traditional quality co

attention for quality was paid by managements. For

accepting the manufactured parts as identical in mass

production, it is enough to try to manufacture parts

according to working drawings and tolerances. Because

sizes and tolerances obtained after manufacturing make

them identical rather than the dimensions and tolerances

on working drawings. For this reason, when unbiased

decisions about a production period is needed statisticaltechniques based on unbiased information obtained

from product or process are used. Control charts, pro-

cess capability definitions and design of experiments

have been used for years.

There is considerable theoretical and experimental re-

search work for improving product quality and pro-

cesses using statistical techniques. Xie and Goh [4]

discussed statistical techniques and their roles for pro-cess development considering recent research works

and they summarized design techniques by giving some

examples. They focused on statistical techniques used

for improving quality continuously. In another work,

the application of statistical process control in a firm

manufacturing chemical and plastic products and its

usage was discussed. Focusing on outer necessary fac-

tors, statistical process control application was realized.Optimization of processes which is one of the important

part of statistical process control was discussed in man-

ufacturing activities and the success of statistical process

control was evaluated [5].

2. Process control definitions: machine tool capability

(Cp) and process capability (Cpk)

Definitions of process control are used to establish

qualified measurements for potential and performance

of process in industry which are elements of capacity

[6]. Capacity analysis is made by using a data set in sta-

tistical calculations for defining the system�s capability.

Yes

Eliminate causes

No

Is it undercontrol?

Scrap or remachining

Discover causes

SPC applications

First period step

)

ntrol method; (b) statistical quality control method.

366 A.R. Motorcu, A. Gullu / Materials and Design 27 (2006) 364–372

In order to define the system capability, the values are

compared. If the product is approximately 100% in tol-

erance limits, it can be said that the system is ‘‘capable’’.

The tolerance limits are determined by customers, engi-

neers and management and they are classified as require-

ments, aims, specifications and standards. There should

be lower and upper limits of specification restrictions for

the definition of the system.It is accepted that the data should distribute normally

for making capacity analysis calculations. A histogram

is plotted to see if the data distribute normally or not.

Before making capacity analysis, control charts are plot-

ted on the data gathered from the system to see the sys-

tem stability. Traditional applications of control charts

are used to discover the points exceeding the tolerance

limits of the part. In modern production systems, sinceproducts are generally inspected automatically, data ob-

tained during the use of traditional charts is not suitable

for a specific dimension of sample parts. Alongside this

the sample size should be selected larger. To make a

clear decision about the capability of a production line,

enough number of sample parts should be manufactured

and inspected [7].

Capability analysis helps to determine the ability formanufacturing parts in the tolerance limits and engi-

neering values. Capability analysis can be applied not

only to a manufacturing period but also to a machine

tool [1]. Capability analysis gives the information about

the system development during the period.

Machine tool capability (Cp) and process capability

(Cpk) are used to determine the efficiency. Cp is used

to determine the system�s location in tolerance limits.The size of deviations from the average value of process

dimensions will indicate how well the production is. If

the system is not at the center of specification values,

the trend of Cp is progressing faultily.

Cpk is used to determine the average so that the sys-

tem will works better in the specification limits. If the

value of Cpk is 1 it shows that the manufacturing is

going on in the system specification limits staying at99.73% level (±3r limits). If the system centralized at

the target value, Cp and Cpk values will be aqual. When

the values of Cp and Cpk is 1, this is considered, as the

minimum requirement of the system for some compa-

nies. Alongside this, larger Cp and Cpk values, for in-

stance 2, are accepted by many companies. Cp and

Cpk are defined by the following equations [8]:

SD1 ¼Range

d2

ð1Þ

SD2 ¼

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiPX � X aveð Þ2

n� 1

sð2Þ

Cp ¼ UTL� LTL

6:SD1

ð3Þ

Cpk ¼ UTL� LTL

6:SD2

ð4Þ

where

SD1 guessed standard deviation

Range tolerance, the difference between upper and

lower tolerance limits

d2 center line factor used for calculation of 3r con-

trol limitsSD2 experimental standard deviation

X measured size

Xave average of measured sizes

n number of measurements

UTL upper tolerance limit

LTL lower tolerance limit

The value of d2 was taken as 3.472 for n = 15 [9].Capability helps to reach the target values which are

important for customers. If a product deviates from the

target values, it means the product specifications do not

meet the requirements. This causes increase in costs and

decrease in sales. An experienced team is required for

capability analysis and this team tries to improve it.

They collect necessary information and data, and they

develop theories and do necessary calculations [10].Small and medium sized companies must make cor-

rect decisions and develop more efficient processes in or-

der to survive in the competitive market. Thereby,

correct understanding of the components of variables,

definition of factors causing variations and keeping

them under control are all important for small sized

companies [10,11]. Develeyd et al., presented two papers

on application of statistical methods. First paper is onthe capability analysis in Swedish industry and the sec-

ond one is on statistical quality control in two small

companies of ceramics industry which do not have large

production capacities. These cases shown that efficien-

cies of some medium sized companies may be increased

by applying the statistical methods developed [11].

3. Statistical process control in machining the cast parts, a

case study on Cp and Cpk analysis

With this study, eliminating the quality problems of

work pieces during machining in a medium sized com-

pany comprising a casting and machining work shop

was aimed. The process was required to reevaluate be-

cause of some problems accrued during assembly andafter quality control of products in the company where

the assembly of the products was carried out. Obtaining

the permission and support of the management a team

was gathered, the data was collected and analyzed and

the reason for problems investigated [12].

The workpiece is the part of a construction machine.

It was cast as spheroidal cast iron and machined using

various chip removing operations namely rough and


finish turning and drilling operations. First turning

operations are applied on the sizes of 62.500–62.250,

56.410–56.490 and 63.512–63.550 must be machined

within the specification limits and must be under con-

trol. The size, tolerance values and surface roughness

values of the sample part are shown in Fig. 2.

After having examined the faulty parts, it was discov-

ered that there were some problems with the sizes of62.500–62.250, 56.410–56.490 and 63.512–63.550; some

problems with geometric tolerances and some problems

with the surface finish. In other words, there were some

parts out of tolerance limits, some parts out of circular-

ity (ovalness), some parts with poor surface finish.

First the process in the foundry where the parts were

cast was examined. Then, in the machining work shop

where the quality problem had not been able to solved,the machining problems were identified by doing statis-

tical process control during production.

Using the lot acceptance sampling plan, a single-sam-

pling plan in statistical study, it was accepted that 10%

of Plot size of 600–750 parts sent to the partner com-

pany which represents the whole lot. 75 sample parts

were taken from each lot and the accepted quality level

was determined as 5%. Samples were taken from five

5˚

A

A

0.05 D

0.08 FN8

N8

D

0.80x45

Fig. 2. Technical drawing

production lines where the same amount of products

were manufactured in every hour. In order to plot X–

R control graphics and to do process capability analysis,

statistical parameters were calculated using the measure-

ments values taken from the samples that represent the

whole process [9,11].

For one size, dimension distribution of the products

manufactured in each production line affects the averageof the whole process in a different way. Thereby, carry-

ing out statistical work for each production line, normal

distribution diagrams and histograms were prepared

and how these affect the whole process was investigated.

In Table 1, for the production line 1 statistical study

results of 62.500–62.250, 56.410–56.490 and 63.512–

63.550 sizes are given.

In Fig. 3, for the production line 1 histograms for62.500–62.250, 56.410–56.490 and 63.512–63.550 sizes

are presented. In the production line 1, 62.500–62.250

size the average was calculated as 62.270 and it was

determined that normal distribution curve was located

in the middle (Table 1, Fig. 3). Staying of the average

value within the tolerance limits indicates that no faulty

product is manufactured. However, for this production

line it can not be said that the manufacturing is

0.05 D

0.08 F

N7

N9

F

Ø59.5

Ø66.7

DE TA Y C

DETAIL J

DETAIL C

DETAIL J

DETAIL C

of the sample part.

Table 1

For the production line 1, statistical calculation results for 62.500–62.250, 56.410–56.490 and 63.512–63.550 sizes

Sample no. 62.500–62.250 56.410–56.490 63.512–63.550 62.500–62.250 56.410–56.490 63.512–63.550

S1 62.270 56.450 63.540 Average 62.270 56.444 63.538

S2 62.280 56.450 63.540 SD1 0.0072 0.0057 0.0028

S3 62.255 56.430 63.540 SD2 0.0085 0.0071 0.0036

S4 62.255 56.450 63.530 Range 0.0250 0.0200 0.0100

S5 62.275 56.440 63.540 Cp 5.7870 2.3148 2.1990

S6 62.265 56.445 63.530 Cpk 4.8735 1.8549 1.7191

S7 62.280 56.435 63.540

S8 62.280 56.450 63.535

S9 62.265 56.435 63.540

S10 62.265 56.450 63.540

S11 62.275 56.445 63.540

S12 62.265 56.450 63.535

S13 62.280 56.440 63.540

S14 62.265 56.450 63.540

S15 62.270 56.435 63.540

6

5

4

3

2

1

0

8

6

4

2

0

12

10

8

6

4

2

0

Freq

uenc

y

Freq

uenc

y

Freq

uenc

y

63.530 63.535 63.540 56.430 56.435 56.440 56.445 56.450 62.255 62.265 62.270 62.275 62.280

Fig. 3. For the production line 1, histograms of 62.500–62.250, 56.410–56.490 and 63.512–63.550 sizes.


continuing normally. The averages of 56.410–56.490 and

63.512–63.550 sizes stays within the tolerance limits andit can be said that there is no faulty production for these

sizes.

The results of statistical calculations of the process

with data obtained from all production lines and process

capability values are listed in Table 2. Since the gap be-

Table 2

Statistical results of the process

Statistical

parameters

62.500–62.250 56.410–56.490 63.512–63.550

Average 62.2470 56.4367 63.5390

SD1 0.00576 0.00748 0.00403

SD2 0.01998 0.00924 0.00569

Range 0.0200 0.0260 0.0140

LSL 62.500 56.490 63.550

USL 62.250 56.410 63.512

Cp 7.2337 1.7825 1.5710

Cpk 2.0851 1.4422 1.1124

LCL (average) 62.2517 56.4425 63.5421

UCL (average) 62.2427 56.4309 63.5359

LCL (range) 0.0330 0.0430 0.0231

UCL (range) 0.0070 0.0090 0.0049

tween the upper tolerance values and lower tolerance

values in three sizes is large, the values of machine tooland process capability were desired to be larger than 2

(Cp, Cpk > 2) [12].

Since in the production line 1, for 62.500–62.250

Cp = 5.7870 > 2 and Cpk = 4.8732 > 2 the machine tool

and process is sufficient (Table 1). In the same produc-

tion line during machining of 56.410–56.490 and

63.512–63.550 sizes while the machine capability was

accomplished since Cp was greater than 2, it was deter-mined that the process capability was not achieved be-

cause Cpk was less than 2.

The process stability was observed in this production

line after evaluated of capability data obtained from

statistical calculations of production line 1. These ob-

tained values showed that having reevaluated the pro-

cess, process parameters were required to be

redetermined. When examining of Table 2 for othertwo sizes, except for 62.500–62.250 size, both machine

tool and process capability were accomplished. On

the other hand for two sizes of 62.500–62.250, process

capability value Cpk approached to the limit value.

When compared the Cp and Cpk values of production


line 1 in Table 1 and the Cp and Cpk values of the pro-

cess in Table 2 while the machine tool capability of all

sized in production line 1 was achieved, in the whole

process, accept for 62.500–62.250 size, any machine

tool and process capability was not achieved. It can

be seen that at least one or more production lines affect

the machine and process capability badly. It was ob-

served that the production deviated from the aimed val-ues and the production was not continued stably after

doing machine and process capability work.

In Fig. 4, the histograms and normal distribution

curves of 62.500–62.250, 56.410–56.490 and 63.512–

Fig. 5. The X–R graphics and normal distrib

12

10

8

6

4

2

0

20

18

16

14

20

18

16

14

12

10

8

6

4

2

0

56.4

10

56.4

20

56.4

15

56.4

25

62.2

20

62.2

25

62.2

30

62.2

35

62.2

40

62.2

45

62.2

50

62.2

55

62.2

60

62.2

65

62.2

70

62.2

75

62.2

80

62.2

85

62.2

90

62.2

95

62.3

00

62.3

05

62.3

10

62.3

15

62.3

20

62.3

25

62.3

30

Freq

uenc

y

Freq

uenc

y

Fig. 4. The histograms of 62.500–62. 250, 56.410–56.49

63.550 for the whole process were presented. In Fig. 3

for 62.500–62.250 sizes, while size dimensions were accu-

mulated near to the lower tolerance value and no pro-

duction was made out of tolerance in production line

1, Fig. 4 for the same size the normal distribution curve

was concentrated near to the lower tolerance value and

it can be seen that the production was made in high fre-

quency out of lower tolerance.Being high of size frequency manufactured out of tol-

erance shows that there is a problem at least in one or

more production lines. When examining Fig. 4, it can

be seen that no production was made out of tolerance

ution curve for the 62.500–62.250 size.

40

35

30

25

20

15

10

5

0

56.4

30

56.4

35

56.4

40

56.4

45

56.4

50

63.5

20

Freq

uenc

y

63.5

25

63.5

30

63.5

35

63.5

40

63.5

50

0 and 63.512–63.550 sizes for the whole process.


for 56.410–56.490 and 63.512–63.550 sizes. The normal

distribution curves and the frequency were concentrated

near average values for both sizes.

Using the data obtained from the statistical calcula-

tions in Table 2, X–R graphics charts were prepared

for each size and normal distribution curves were plot-

ted. In the X–R control graphic shown in Fig. 5 while

the averages study near the control limits in the produc-tion line 2 and 3 of the sample groups, the averages stay

out of the control limits in the production lines 1, 4 and

5. Due to making production out of the control limits it

was necessary to stop the production and rearrange the

manufacturing and machine tool parameters by evaluat-

ing the process completely. As it can be seen from the

normal distribution curve in Fig. 5 the average of aver-

ages Xave is smaller than the lower value (Xave =62.247 < 62.500). Thereby it can be said that most of

the products manufactured are faulty.

When examining the X–R charts of 56.410–56.490

size in Fig. 6, it is clearly seen that the averages of the

production line 1 and 3 are out of the control limits

and the average of the production line 2 approached

to the control limits. Whereas in the production line 4

and 5 since the averages came close to the average ofaverages, the production continuous normally. The pro-

duction line 1, 2 and 3 must be stopped, examined and

the necessary arrangements must be made.

In Fig. 7 for 63.512–63.550 size while the production

is made out of the control limits in the production line 1,

2 and 3, the production is carried out normally in the

Fig. 6. The X–R graphics and normal distrib

production line 4 and 5 according to the X control gra-

phic. Since the production is made out of the control

limits, the production lines must be stopped, machine

tool and machining parameters reexamined and the nec-

essary arrangements must be made.

Since the values of the average of averages for

56.410–56.490 and 63.512–63.550 sizes accumulated be-

tween the tolerance values of the two sizes faulty prod-ucts were not manufactured however, it can be said

that there is a tendency that these sizes can be manufac-

tured faulty in some production lines.

4. Machinability parameters and rearranging the process

As a result of the statistical studies, it was necessaryto stop the process, reexamine the process parameters

and rearrange the process in order to reassure machine

tool and process stability. Workpiece material, machine

tool and cutting parameters which are three important

components of machinability were reevaluated.

� First of all the structure of the workpiece material

were examined. Since the casting and machining ofthe part are carried out in different work shops in

the firm, having done arrangements in the casting

work shop, it was determined that there existed shell

hardening of the outer surface of the part because of

dismantling of casting dies before the necessary wait-

ing time. Casting die sets were dismantled after the

ution curve for the 56.410–56.490 size.

Fig. 7. The X–R graphics and normal distribution curve for the 63.512–63.550 size.


required time in order not to reduce the tool life and

therefore not to increase the tool costs because of this

excessive shell hardening.

� After the statistical studies carried out during manu-

facturing in the production workshop, the parameters

of two main components of machinability were inves-

tigated in order to prevent manufacturing out of tol-

erances and ovality and to obtain the ideal surfaceroughness. In order to prevent the ovality, the set-

tings of the center line of the chuck and tail stock

of the CNC machine tool and chuck jaws were exam-

ined. Having redetermined the work piece reference

points on the CNC machine tool, the center setting

of the machine tool were readjusted.

� It was determined that inappropriate cutting speeds

and feed rate values, hard shell and sandy areas onthe outer surface of the part reduced the cutting tool

life. It was also determined that early fractures of cut-

ting tools were occurred during machining and differ-

ent from the sizes programmed in CNC part

programs were obtained because of the insert corner

wear. Since tool wear was not determined dynami-

cally, size dimensions were determined as larger than

they should be after a number of parts manufactured.Since the tool wear was not measured and the tool life

was not determined correctly, the production out of

tolerance values were occurred. In order to prevent

this, the cutting parameters in the CNC part program

namely cutting speed, feed rate and cutting depth

were examined [13].

� Taking into account of insert nose radius values of

CCGT09T304, HC2DNGA 443TN and CCGT-

09T304 used for machining 62.500–62.250, 56.410–

56.490 and 63.512–63.550 sizes, (0.4 mm, 1.2 mm

and 0.4 mm respectively) and the desired surfaceroughness values (N8 = 0.16 lm, N7 = 0.8 lm and

N8 = 0.16 lm respectively), the feed rate values were

redetermined as 0.130 mm/rev, 0.163 mm/rev and

0.130 mm/rev respectively [13].

� When machining the spheroidal cast part, 100mm/

min cutting speed were selected and spindle speeds

were entered to the CNC part program as 505 rev/

min, 568 rev/min and 497 rev/min for machining62.500–62.250, 56.410–56.490 and 63.512–63.550

sizes respectively [13].

� In order to prevent excessive tool wear, cracks and

fractures when machining the outer shell of the part,

the rough cutting depth of the first pass was increased

and the damages generated by intermittent impacts

and sandy areas to the tool nose and cutting edge

minimized.� After entering the new calculated parameters into the

CNC program, it was determined that how much the

cutting tool wore and the tool life expired after how


many parts. According to the criteria determined, the

operator was told to change the insert before it�s toollife.

After rearranging the machining parameters, the pro-

duction was resumed, it was investigated that how thenew values had affected the process and the statistical

studies were accomplished. As a result of the studies

the parts were manufactured within the tolerance limits

and ovality and surface roughness problems were elimi-

nated. These results were observed by measuring and

controlling the parts manufactured.

5. Results and discussion

Statistical quality control is applying statistical rules

and techniques to each phase of design, manufacturing

and service. One of the techniques for assuring the qual-

ity during production is to apply the statistical period

control techniques to each phase of production during

or after manufacturing. Statistical quality control havehad great deal of improvements in companies using

mass production. Capability analysis helps to determine

the ability for manufacturing between tolerance limits

and engineering specifications. Capability analysis can

be applied not only to production period but also to a

machine or machine tool. Capability analysis gives the

information about changes and tendencies of the system

during production. It is used to determine the systemtendencies between tolerance limits. Deviations and

faults of the average of process dimensions can be seen.

Cp and Cpk are used to determine capability.

In this study, statistical calculations for a product

manufactured using mass production in a medium sized

company were carried out. An X–R chart was con-

structed for each production line using the manufactur-

ing data obtained from each line respectively and themachine tool – process capability was determined.

The X–R graphics of the data obtained from the pro-

duction line 1 and whole production lines and capability

values were compared in order to determine the differ-

ences in sizes having tolerances of the part manufac-

tured by five different production lines.

It was determined that the production had not car-

ried on normally before the statistical work. After thestatistical study, during the machining the sizes having

quality problems the production lines where parts were

manufactured out of tolerance were determined. It was

detected that the machine tool and process capability

for the whole process was inadequate and the mass pro-

duction was unstable. Ovality, faults regarding manu-

facturing out of tolerance limits were eliminated and

surface roughness was improved.

As a result of this study, the employee understood the

quality requirement, the cost due to unquality produc-

tion was reduced and the production was enabled to car-

ry on normally with the help of problem solving desire

of them. Since this study reduced the unquality costs

during production, the company management sup-ported the study. However, because of economic prob-

lems, the necessary financial support was not given

and therefore the statistical quality control studies could

not pursued efficiently.

As can be seen from the study accomplished, the sta-

tistical quality control and capability method is effective

for determining the quality problems and solving them

in small and medium sized companies that manufactureparts by machining. In today�s competitive market, one

of the problems of small companies is insufficient invest-

ments and lacking sufficient qualified employee having

practical and theoretical knowledge for the job.

References

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tion Centre, no. 25, Ankara; 1995.

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Documents

Process Capability