We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

A Data Mining Approach for Developing Quality Prediction Model in Multi-Stage Manufacturing

by Fahmi Arif, Nanna Suryana, Burairah Hussin
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 69 - Number 22
Year of Publication: 2013
Authors: Fahmi Arif, Nanna Suryana, Burairah Hussin
10.5120/12106-8375

Fahmi Arif, Nanna Suryana, Burairah Hussin . A Data Mining Approach for Developing Quality Prediction Model in Multi-Stage Manufacturing. International Journal of Computer Applications. 69, 22 ( May 2013), 35-40. DOI=10.5120/12106-8375

@article{ 10.5120/12106-8375,
author = { Fahmi Arif, Nanna Suryana, Burairah Hussin },
title = { A Data Mining Approach for Developing Quality Prediction Model in Multi-Stage Manufacturing },
journal = { International Journal of Computer Applications },
issue_date = { May 2013 },
volume = { 69 },
number = { 22 },
month = { May },
year = { 2013 },
issn = { 0975-8887 },
pages = { 35-40 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume69/number22/12106-8375/ },
doi = { 10.5120/12106-8375 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:31:03.515617+05:30
%A Fahmi Arif
%A Nanna Suryana
%A Burairah Hussin
%T A Data Mining Approach for Developing Quality Prediction Model in Multi-Stage Manufacturing
%J International Journal of Computer Applications
%@ 0975-8887
%V 69
%N 22
%P 35-40
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Quality prediction model has been developed in various industries to realize the faultless manufacturing. However, most of quality prediction model is developed in single-stage manufacturing. Previous studies show that single-stage quality system cannot solve quality problem in multi-stage manufacturing effectively. This study is intended to propose combination of multiple PCA+ID3 algorithm to develop quality prediction model in MMS. This technique is applied to a semiconductor manufacturing dataset using the cascade prediction approach. The result shows that the combination of multiple PCA+ID3 is manage to produce the more accurate prediction model in term of classifying both positive and negative classes.

References
  1. Kano, M. , and Nakagawa, Y. 2008. Data-Based Process Monitoring, Process Control, and Quality Improvement: Recent Developments and Applications in Steel Industry. Computers & Chemical Engineering 32 (1–2), 12–24.
  2. Guo, X. , Wang, F. , and Jia, M. 2005. A Stage-Based Quality Prediction and Control Method for Batch Processes. In Proceeding of the International Conference on Machine Learning and Cybernetics.
  3. Kim, D. , Kang, P. , Cho, S. , Lee, H. , and Doh, S. , 2012. Machine Learning-Based Novelty Detection for Faulty Wafer Detection in Semiconductor Manufacturing, Expert Systems with Applications. 39 (4), 4075–4083.
  4. Chen, P, and Luo, J. 2008 Data Detection and Pattern Recognition on FMS Control Charts. In Proceedings of the International Conference on Industrial Technology.
  5. Chen, W. , Tai, P. , Wang, M. , Deng, W. , and Chen, C. 2008. A Neural Network-Based Approach for Dynamic Quality Prediction in A Plastic Injection Molding Process. Expert Systems with Applications. 35 (3), 843–849.
  6. Ho, W. , Tsai, J. , Lin, B. , and Chou, J. 2009. Adaptive Network-Based Fuzzy Inference System for Prediction of Surface Roughness in End Milling Process Using Hybrid Taguchi-Genetic Learning Algorithm. Expert Systems with Applications. 36 (2), 3216–3222.
  7. Ho, G. T. S. , Lau, H. C. W. , Lee, C. K. M. , Ip, A. W. H. , and Pun, K. F. 2005. An Intelligent Production Workflow Mining System for Continual Quality Enhancement. The International Journal of Advanced Manufacturing Technology. 28(7–8), 792–809.
  8. Ho, G. T. S. , Lau, H. C. W. , Kwok, S. K. , Lee, C. K. M. , and Ho, W. 2009. Development of a Co-Operative Distributed Process Mining System for Quality Assurance. International Journal of Production Research. 47(4), 883–918.
  9. Ho, G. T. S. , Lau, H. C. W. , Chung, S. H. , Fung, R. Y. K. , Chan, T. M. , and Lee, C. K. M. 2008. Fuzzy Rule Sets for Enhancing Performance in A Supply Chain Network. Industrial Management & Data Systems. 108 (7), 947–972.
  10. Hua, D. , Shi-yuan, Y. , and De-hui, W. 2007. Intelligent Prediction Method for Small-Batch Producing Quality Based on Fuzzy Least Square SVM. Systems Engineering-Theory & Practice. 27(3).
  11. Huang, Y. , Cheng, F. , and Hung, M. 2009. Developing A Product Quality Fault Detection Scheme. In Proceedings of International Conference on Robotics and Automation.
  12. Johnston, A. B. , Maguire, L. P. , and McGinnity, T. M. 2009. Downstream Performance Prediction for A Manufacturing System Using Neural Networks and Six-Sigma Improvement Techniques. Robotics and Computer-Integrated Manufacturing. 25(3), 513–521.
  13. Melin, P. , and Castillo, O. 2007. An Intelligent Hybrid Approach for Industrial Quality Control Combining Neural Networks, Fuzzy Logic and Fractal Theory. Information Sciences. 177 (7), 1543–1557.
  14. Rokach, L. , and Maimon, O. 2006. Data Mining for Improving the Quality of Manufacturing: A Feature Set Decomposition Approach. Journal of Intelligent Manufacturing. 17 (3) 285–299.
  15. Xu, X. 2004. Fuzzy Control for Manufacturing Quality Based on Variable Precision Rough Set. In Proceeding of the Fifth World Congress on Intelligent Control and Automation.
  16. Yu, J. , and Xi, L. 2009. A Neural Network Ensemble-Based Model for On-Line Monitoring and Diagnosis of Out-of-Control Signals in Multivariate Manufacturing Processes. Expert Systems with Applications. 36 (1), 909–921.
  17. Chen, W. , Tseng, S. , and Wang, C. 2005. A Novel Manufacturing Defect Detection Method Using Association Rule Mining Techniques. Expert Systems with Applications. 29 (4), 40–52. .
  18. Lau, H. , Ho, G. , Chu, K. , Ho, W. , and Lee, C. 2009. Development of An Intelligent Quality Management System Using Fuzzy Association Rules. Expert Systems with Applications. 36 (2), 1801–1815.
  19. Kaya, I. , and Engin, O. 2007. A New Approach to Define Sample Size at Attributes Control Chart in Multistage Processes: An Application in Engine Piston Manufacturing Process. Journal of Materials Processing Technology. 183 (1), 38–48.
  20. Li, J. T. , Freiheit, J. , Hu, S. J. , and Koren Y. . 2007. A Quality Prediction Framework for Multistage Machining Processes Driven by an Engineering Model and Variation Propagation Model. Journal of Manufacturing Science and Engineering. (129. (6), 1088–1100, 2007.
  21. Huang, Q. , and Shi, J. 2004. Stream of Variation Modeling and Analysis of Serial-Parallel Multistage Manufacturing Systems. Journal of Manufacturing Science and Engineering, 126 (3), 611.
  22. Tsung, F. , Li, Y. , and Jin, M. 2008. Statistical Process Control for Multistage Manufacturing and Service Operations: A Review and Some Extensions. International Journal of Services Operations and Informatics. 3 (2), 449–452.
  23. Zou, C. , and Tsung, F. 2008. Directional MEWMA schemes for multistage process monitoring and diagnosis. Journal of Quality Technology. 40 (4), 407–427.
  24. Arif, F. , Suryana, N. , and Hussin, B. 2013. Framework of Cascade Quality Prediction Method Using Latent Variables for Multi-Stage Manufacturing. International Journal of Management Theroy and Application. 1 (1).
  25. Zhao, C. , Wang, F. , Lu, N. , and Jia M. 2007. Stage-Based Soft-Transition Multiple PCA Modeling and On-Line Monitoring Strategy for Batch Processes. Journal of Process Control. 17 (9), 728–741
  26. Qi, Y. , Wang, P. , and Gao, X. 2011. Enhanced Batch Process Monitoring and Quality Prediction Using Multi-Phase Dynamic PLS, Control Conference (CCC).
  27. Ge, Z. , Zhao, L. , Yao, Y. , Song, Z. , and Gao, F. , Utilizing Transition Information in Online Quality Prediction of Multiphase Batch Processes, Journal of Process Control.
  28. Shlens, J. , 2003, A Tutorial on Principal Component Analysis: Derivation, Discussion and Singular Value Decomposition, 2003. [Online]. Available: http://www. cs. princeton. edu/picasso/mats/PCA-Tutorial-Intuition_jp. pdf. [Accessed: 01-Dec-2011].
  29. Salahshoor, K. , Alaei, H. K. , and Alaei, H. K. , 2010. A New On-line Predictive Monitoring Using Integrated Approach Adaptive Filter and PCA. In Proceeding of International Workshop on Soft Computing Applications.
  30. Bersimis, S. , Psarakis, S. , and Panaretos, J. 2007. Multivariate Statistical Process Control Charts: An Overview, Quality and Reliability Engineering International. 23 (5), 517–543.
  31. Abdi, H. , and Williams, L. J. 2010. Principal Component Analysis, Wiley Interdiciplinay Reviews: Computational Statistics. New York: John Wiley & Sons.
  32. Smith, L. I. . 2002. A Tutorial on Principal Components Analysis, [Online]. Available: http://www. cs. otago. ac. nz/cosc453/student_tutorials/principal_components. pdf.
  33. Akthar, F. , and Hahne, C. , 2012. RapidMiner 5: Operator Reference. Rapid-I GmbH. .
  34. Ture, M. , Tokatli, F. , and Kurt, I. 2009. Using Kaplan–Meier analysis together with decision tree methods (C&RT, CHAID, QUEST, C4. 5 and ID3) in determining recurrence-free survival of breast cancer patients. Expert Systems with Applications. 36 (2), 2017–2026.
  35. Shrivastava, S. K. , and Tantuway, M. 2011. A Decision Tree Algorithm Based on Rough Set Theory after Dimensionality Reduction. International Journal of Computer Applications. 17(7), 29–34.
  36. Anyanwu, M. N. , and Shiva, S. G. 2009. Comparative Analysis of Serial Decision Tree Classification Algorithms. Journal of Computer Science and Security, 3, 230–240.
  37. Du, J. , Cui, H. , H. Li, H. , and Zhao, Z. 2011. Fault Diagnosis of Vacuum Circuit Breakers Based on ID3 Method. In Proceeding of the 1st International Conference on Electric Power Equipment.
  38. Jin, C. , De-lin, L. , and Fen-xiang, M. 2009. An Improved ID3 Decision Tree Algorithm. In Proceeding of the 4th International Conference on Computer Science & Education.
  39. Jingjun, F. , and Shuting, Y. 2009. Alumina Production Operations Management Information System Based on Data Mining Technology. In Proceeding of the International Forum on Computer Science-Technology and Applications.
  40. McCann, M. and Johnston, A. , 2008. Secom Dataset, UCI Machine Learning Repository, [Online]. Available: http://archive. ics. uci. edu/ml/datasets. html.
  41. May, G. S. , and Spanos, C. J. 2006. Fundamentals of Semiconductor Manufacturing and Process Control. New Jersey: John Wiley & Sons
  42. DeVor, R. E. , Chang, T. , and Sutherland, J. W. 2007. Statistical Quality Design and Control: Contemporary Concepts and Methods, 2nd ed. Prentice-Hall.
  43. García, S. , Fernández, A. and Herrera, F. , 2009) Enhancing the Effectiveness and Interpretability of Decision Tree and Rule Induction Classifiers with Evolutionary Training Set Selection Over Imbalanced Problems. Applied Soft Computing. 9 (4), 1304–1314. .
Index Terms

Computer Science
Information Sciences

Keywords

Principal Component Analysis ID3 Quality Prediction Data Mining Multi-stage Manufacturing

Powered by PhDFocusTM