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A Design of Inspection Sampling Plans for Incoming Aluminum Parts

1*, 2 1,2 . 20130,

E-mail: Blila@buu.ac.th*

Banhan Lila1* Jakrawarn Kunadilok2

1,2Department of Industrial Engineering, Faculty of Engineering, Burapha University, Chonburi

E-mail: Blila@buu.ac.th*

MIL STD 105E Visual C++

32.53% 82.08% 7.20% 0.99%

682,000

Abstract

Quality of parts is a significant factor affecting the subsequence operations. The acceptable performance

of an incoming inspection with reasonable cost is required. This paper presents a design and an

implementation of the inspection plans for incoming aluminum parts of a safety belt production process. The decision support system written in C++ were designed and used for inspecting of attribute quality

characteristics. The application in the screening process indicated that 82.08% of defectives were found at the incoming inspection station compared to only 32.53% previously. This improvement resulted in the

reduction of the leakages of defective parts into the production process from 7.20% to 0.99%. Consequently, cost related to the quality problem was decreased by the average of 682,000 baht per

month which could be viewed as a solid evidence and a guideline for implementation such techniques in the similar situations.

Keywords: Inspection Plan, Aluminum Parts, Decision Support System

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1.

[5] William Cooper

Proctor Procter & Gamble

. . 1887 �

�

Production and Operations

Management Roger, Kevin, and Dongli [6]

TQM

[6] [7]

[1],

[2] [3]

(Inspection) Mistake-Proofing, 100%

(Sampling Inspection) (SPC)

Mistake-Proofing Poka-Yoke Go-

NoGo gages

(Acceptance Sampling Plan)

MIL-STD 105D, MIL-STD 105E

American Society of Quality Control (ASQC) ANSI/ASQC Z1.4 [8] [9]

[1], [2]

[3]

MIL-STD 105E

[1], [2], [3] [4]

(Producer�s

Risk, ) (Consumer�s Risk, )

MIL STD 105E Visual C++

MS Excel

,

(Average

Outgoing Quality, AOQ) (Average Total Inspection, ATI)

2.

(Incoming Materials) (Work in Process)

(Outgoing Products) [9]

(Lot)

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16-18 2556

(Accept) (Reject)

( Lot-

Disposition Action)

(Single Sampling Plan, SSP) (Double-Sampling Plan, DSP) (Multiple-

Sampling Plan, MSP) (Sequential-

Sampling Plan)

, 2550 [1]

CKD

, 2550 [2]

MIL-STD-105E

, 2552 [3] Poka-Yoke

MIL-STD-105E

, . 2552, [4]

Marvin, Kim, and Park, 2009 [7]

Belmiro and Pedro, 2008 [8]

SSP DSP

Andreas et al,

2011 [10]

Escherchia coli O157

Vellaisamy, Sankar and Taniguchi, 2003 [11] Aminzadeh, 2008 [14]

(Autoregressive

Moving Average, ARMA) [11] DSP [14]

Muhammad, 2011 [12] Pearn and Chien-Wei, 2006 [13]

[12]

EOQ [13]

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3.

MIL-STD-105E

3.1

MIL STD 105E Visual C++

MS Excel

Acceptable Quality Level (AOQ) 0.1% 10%, Lot Size (N),

(Continuous Isolated), ( SSP, DSM MSP)

(General I - III Special

I - IV) C++ MS DOS 1

1 C++

Notepad

(n) (c Ac) MS Excel 2007 2

2 MS Excel 2007

OC Curve, AOQ, AOQL,

ATI ASN

3.2

MIL-STD-105E

Notepad C++

3.3

30

4.

4

[3]

1 669

731 1400 (

) 47.8 3

1

P 5800 158 0.699 68 0.301

M 2500 144 0.735 52 0.265

A 7500 259 0.325 537 0.675

AY 3950 108 0.593 74 0.407 : A = M = A =

AY =

300

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7500 259 796

32.53 1

3.9, 7.8, 10.6 4.6

3

537

67.5

3

4

4 ( ) ( )

2 POKA-YOKE

1 Coordination Measuring (CMM)

n=5 (c=0)

1 (p) 0.106

Pa (1) AOQ (2) ATI (3)

Pa = 0.57, AOQ = 0.06 ATI = 132 3

57

1 67.5

(1)

(2)

(3)

d c n

N p

AOQ = 0.06

6 7500

446 ( 1 537 )

132

(1)

(OC Curve) AOQ ATI 5, 6 7

n=5 c=0

MIL-STD-105E

Pa, AOQ, ATI

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16-18 2556

5 OC n=5, c=0

6 AOQ N=300, n=5, c=0

7 ATI N=300, n=5, c=0

5.

2 1

2

General Level I, II III Nornal, Tightened Reduced AQL= 1%

2

2

I II III

F H J

Normal 20/0 50/1 80/2

Tightened 20/0 80/1 125/2

Reduced 5/0 20/1 50/1

2 20/0

Normal I n=20, c=0

n=5 c=0 Reduced

(Switching Rule) [8] Normal 3

8, 9 10

8 OC

9 AOQ

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10 ATI

8, 9 10

Normal N= 300

n/c 20/0, 50/1 80/2 8 9 10

3

3

I II III

0.106 0.026 0.007

AOQ 0.056 0.050 0.044

ATI 140 157 174

I

n=20 c=0

0.106

AOQ 3

10

6.

n=20, c=0

10 3126 Reject 7 142

100%

31

10 173

(p) 0.055 (0.106)

82.08

0.99

32.53 82.08

7.2 0.99

n=20, c=0

(p)

0.106 0.055

p = 0.106

p = 0.055

AQL=1%

Normal Reduced

AQL

7.

MIL-STD-105E

682,000 [3]

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2552 57/2552

[1] . 2550.

(CKD). ,

, , . [2] . 2550.

MIL-STD-100E.

, ,

, . [3] . 2552.

.

, , 21-22 2552: .

[4] , . 2552.

MIL-STD-105E. ,

, ,

, 40-55. [5] James R.E. and William M.L. 2005. The

Management and Control of Quality, Thomson South-Western, Singapore; 3-4.

[6] Roger S., Kevin L., and Dongli Z. 2005. Evolution of Quality: First Fifty Issues of

Production and Operations Management. Production and Operations Management, Vol. 14,

No.4 : 468-481.

[7] Marvin R., Kim D., and Park E. 2009. A Sampling Policy for the Reduction of Quality

Cost and Improvement of Accepted Percentage

in Company L. The Asian Journal on Quality, Vol. 10, No. 3 : 99-113.

[8] Belmiro P. and Pedro M. 2008. An Optimization-Based Approach for Designing Attribute

Acceptance Sampling Plans. International Journal of Quality & Reliability Management,

Vol. 25, No. 8 : 824-841. [9] Douglas C. M. 2005. Introduction to Statistical

Quality Control (5th ed.). John Wiley

International, U.S.A : 646-709. [10] Andreas K., Glen M., Robert B. and IAN J.

2011. Assumptions of Acceptance Sampling and the Implications for Lot Contamination:

Escherichia coli O157 in Lots of Australian Manufacturing Beef. Journal of Food Protection,

Vol. 74, No. 4 : 539-544.

[11] Vellaisamy P., Sankar S. and Taniguchi M. 2003. Estimation and Design of Sampling Plans

for Monitoring Dependent Production Processes. Methodology and Computing in Applied

Probability, Vol. 5, No.1 : 85-108. [12] Muhammad A.. 2011. Economic Order Quantity

with Imperfect Quality, Destructive Testing Acceptance Sampling, and Inspection Errors.

Advances in Management & Applied Economics,

Vol.1, no.2 : 59-75. [13] Pearn WL. and Chien-Wei W. 2006. Variables

Sampling Plans with PPM Fraction of Defectives and Process Loss Consideration. Journal of the

Operational Research Society, Vol.57: 450-459. [14] Aminzadeh M. S. 2008. Sequential and Non-

Sequential Acceptance Sampling Plans for

Autocorrelated Processes using ARMA(p,q) Models. Comput Stat, Vol.24 : 95-111.