Sage Journals: Discover world-class research

Abstract

Quality issues are important to manufacturers so that they can ensure their products are up to the highest standard. However, due to various causes associated with the operational processes, it is hard to identify all the sources of quality problems in a timely manner. A product's defects may cause a very serious loss in market share and low profit margin in the competitive environment of short product life cycles. In order to address this, a systemized and well-designed approach has been formulated to perform the desired task of problem identification based on the specific process. A data mining system (DMS), which is incorporated into emerging technologies including on-line analytical processing (OLAP) and decision tree-based artificial neural networks (ANNs), is presented. With this DMS, essential support for users who wish to identify the cause and source of problems can be provided so that immediate action can be taken for their rectification. The system prototype has been implemented in the factory of a manufacturer of magnetic heads for hard disk drives (HDDs) in order to validate the workability of the proposed methodology.

Keywords

process quality improvement slider fabrication data warehousing application data mining system

References

Fan

H. T.

S. M.

Case studies on modeling manufacturing process using artificial neural networks. Proceedings of American Society of Mechanical Engineers (ASME) Winter Annual Meeting on Neural networks in manufacturing and robotics, November 1992, PED-Vol. 57, pp. 127–141.

Westkämper

Schmidt

Computer-assisted manufacturing process optimization with neural networks. J. Intell. Mfg, 1998, 9, 289–294.

Thompson

J. R.

Koronacki

Statistical process control, the Deming paradigm and beyond, 2nd Edition, 2002 (Chapman and Hall, Florida).

S. M.

Next generation quality control in manufacturing - real-time defect prevention. In Proceedings of the American Society of Mechanical Engineers (ASME) Winter Annual Meeting, San Fransisco, 1989.

Guh

R. S.

Real-time pattern recognition in statistical process control: A hybrid neural network/decision tree-based approach. Proc. IMechE, Part B: J. Engineering Manufacture, 2005, 219, 283–298.

Dahr

Stein

Seven methods for transforming corporate data into business intelligence, 1997 (Prentice Hall, Englewood Cliffs, New Jersey).

Leung

R. W. K.

Lau

H. C. W.

Kwong

C. K.

An expert system to support the optimization of ion plating process: An OLAP-based fuzzy-cum-GA approach. Expert Syst. Applic., 2003, 25, 313–330.

Kaya

Alhajj

Fuzzy OLAP association rules mining-based modular reinforcement learning approach for multiagent systems. IEEE Trans. Syst., Man and Cybernetics, Part B: Cybernetics, 2005, 35(2), 326–338.

G. T. S.

Lau

H. C. W.

W. H.

Ning

An intelligent information infrastructure to support the streamlining of integrated logistics workflow. Expert Syst., 2004, 21(3), 123–137.

10.

Haykin

Neural network: A comprehensive foundation, 2nd Edition, 1997 (Prentice Hall, Upper Saddle River, New Jersey).

11.

Kim

D. W.

Park

G. T.

Prediction of plasma etching using a polynomial neural network. IEEE Trans. Plasma Science, 2003, 31(6), 1330–1336.

12.

Haouant

Lefebvre

Zerhouni

Elmoudni

Neural networks implementation for modeling and control design of manufacturing systems. J. Intell. Mfg, 2000, 11, 29–40.

13.

Liu

T. C.

R. K.

Chen

M. C.

Development of an artificial neural network to predict lead frame dimensions in an etching process. Int. J. Advd Mfg Technol., 2006, 27, 1211–1216.

14.

Kim

GA-optimized backpropagation neural network with multi-parameterized gradients and applications to predicting plasma etch data. Chemometrics and Intell. Lab. Syst., 2005, 79, 123–128.

15.

Rutledge

R. A.

Data warehousing for manufacturing yield improvement. In Proceedings of the 25th Annual SAS Users Group International Conference, 2000.

16.

Whitney

M. C.

Fowler

Motorola's engineering data analysis system: 10 years of analytical excellence. In Proceedings of the 25th Annual SAS User Group International Conference, paper 44–25. Available from http://www.units.muohio.edu/doc/sassystem/sugi25/25/ad/25p044.pdf.

17.

Introduction to data mining and knowledge discovery, 3rd Edition, 1999 (Two Cows Corporation, Potomac, Maryland).

18.

Breiman

Friedman

J. H.

Olshen

R. A.

Stone

C. J.

Classification and regression trees, 1984 (Wadsworth, Belmont).

19.

Rastogi

Shim

PUBLIC: A decision tree classifier that integrates building and prucing. Data Min. Knowledge Discovery, 2000, 4, 315–344.

20.

Fayyad

On the induction of decision trees for multiple concept learning. PhD Thesis, The University of Michigan, Ann Arbor, 1991.

21.

Mitchie

Spiegelhalter

D. J.

Taylor

C. C.

Machine learning, neural and statistical classification, 1994 (Ellis Horwood, New York).

22.

Oakland

J. S.

Statistical process control, 3rd Edition, 1996 (Butterworth-Heinemann, Oxford, Boston).

23.

Wang

S. X.

Taratorin

A. M.

Magnetic information storage technology, 1999 (Academic Press, USA).

24.

Zant

P. V.

Microchip fabrication: A practical guide to semiconductor processing, 4th Edition, 2000 (McGraw-Hill, USA).

Development of a Data Mining System for Continual Process Quality Improvement

Abstract

Abstract

Keywords

References