Application of Computational Intelligence Methods in Control And Diagnosis of Production Processes

• Autorzy: Marcin Perzyk , Jacek Kozłowski , Krzysztof Zarzycki

Warianty tytułu

Zastosowanie metod inteligencji obliczeniowej do sterowania i diagnostyki procesów produkcyjnych

• Języki publikacji: EN

Abstrakty

EN

This chapter presents actual and potential applications of advanced data-driven models in control and fault diagnosis of manufacturing processes. Types of process control are discussed and the role of the computational intelligence as well as other data mining methods in them is shown. The main findings of the present authors, based on results of the previous works, are presented. They include the methodologies of determination of relative significances of process parameters and evaluation of prediction capabilities of time-series modeling. Results of a new research, aimed at assessment of capabilities of learning systems to detect out-of-control patterns of points observed in SPC charts, are presented. (original abstract)

Niniejsze opracowanie przedstawia rzeczywiste i potencjalne zastosowania zaawansowanych modeli opartych na danych w sterowaniu i diagnostyce usterek procesów wytwarzania. Omówiono rodzaje sterowania procesem oraz pokazano rolę, jaką pełnią w nich metody inteligencji obliczeniowej i inne metody eksploracji danych. Zaprezentowano główne stwierdzenia, do jakich doszli autorzy na podstawie wyników wcześniejszych badań. Obejmują one metody określania istotności względnych parametrów procesu oraz ocenę zdolności predykcyjnych modelowania szeregów czasowych. Przedstawiono także wyniki nowych badań, mających na celu ocenę zdolności systemów uczących się do wykrywania układów punktów na kartach kontrolnych SSP, świadczących o rozregulowaniu procesu. (abstrakt oryginalny)

Słowa kluczowe

EN

Production process; Diagnostic method; Control charts

PL

Proces produkcji; Metody diagnostyczne; Karty kontrolne

• Czasopismo: Systems Supporting Production Engineering
• Rocznik: 2013
• Numer: 1(3) Review of Problems and Solutions
• Strony: 104--125

Twórcy

autor

Marcin Perzyk

• Warsaw University of Technology, Poland

autor

Jacek Kozłowski

• Warsaw University of Technology, Poland

autor

Krzysztof Zarzycki

• Reckitt Benckiser Production (Poland) Sp. z o.o.

Bibliografia

• Chen W. H., Tseng S. S., Hsiao K. R., Liu C. C., A data mining project for solving low-yield situations of semiconductor manufacturing, Proc. IEEE International Symposium on Semiconductor Manufacturing Conference, Boston, MA, USA, May 4-6, 2004, 129-134.
• Chiu C. C, Shao Y. E., Lee T. S., Lee K. M., Identification of process disturbance using SPC/EPC and neural networks, Journal of Intelligent Manufacturing, 14 (2003), 379-388.
• Harding J. A., Shahbaz M., Srinivas M., Kusiak A., Data mining in manufacturing: a review, Trans. ASME, J Mfg Sci Engng 128(2006) 969-976.
• Huang C. H. and Lin Y. N., Decision rule of assignable causes removal under an SPC-EPC integration system, International Journal of Systems Science, 33 (2002) 855-867.
• Huang H., Wu D., Product quality improvement analysis using data mining: A case study in ultra-precision manufacturing industry, Lect Notes Comput Sci 3614LNAI (2006) 577-580.
• Hwarng, H. B. and Hubele, N. F., Back-propagation Pattern Recognizers for X Control Charts: Methodology and Performance, Computers and Industrial Engineering, 24 (1993) 219-235.
• Jacobs D. A., Luke S. R., Training Artificial Neural Networks For Statistical Process Control, Proceedings of the Tenth Biennial University/Government/Industry Microelectronics Symposium, Research Triangle Park, NC, USA, May 18-19, 1993, 235-239.
• Jiang W. and Farr J. V., Integrating SPC and EPC Methods for Quality Improvement, Quality Technology & Quantitative Management, 4 (2007) 345-363.
• Kamal A. M. M., A data mining approach for improving manufacturing processes quality control, Proc. 2nd Intern. Conf. on Next Generation Information Technology, ICNIT 2011, Hong Kong, China, 2011, p 207-212.
• Koonce D., Fang C. H., Tsai S. C., Data mining tool for learning from manufacturing systems, Comput Ind Eng 33 (1997) 27-30.
• Kusiak A., Data mining: manufacturing and service applications, Int. J. Prod. Res. 44 (2006) 4175-4191.
• Lewicki P., Hill T., Multivariate Quality Control, Quality Magazine, 2007, April, 38-45. Available from: www.qualitymag-digital.com/qualitymag/200704/.
• Masters T., Practical neural network recipes in C++, Academic Press, San Diego, 1993, 248-249.
• Montgomery D. C., Keats J. B., Runger G. C. and Messina W. S., Integrating statistical process control and engineering process control, Journal of Quality Technology; 26 (1994) 79-87.
• Ozturk A., Kajaligil S., Ozdemirel N. E., Manufacturing lead time estimation using data mining, European Journal of Operational Research 173 (2006) 683-700.
• Perzyk M., Data mining in foundry production, in Research in Polish metallurgy at the beginning of XXI century, Committee of Metallurgy of the Polish Academy of Sciences, Cracow, Poland, 2006, 255-275.
• Perzyk M., Biernacki R., Kozlowski J., Data mining in manufacturing: significance analysis of process parameters, J Eng Manuf, Proc Inst Mech Eng Part B, 222 (2008) 1503-1516.
• Perzyk M., Kochanski A., Kozlowski J., Soroczynski A. and Biernacki R., Applications of data mining to diagnosis and control of manufacturing processes in "Knowledge - Oriented Applications in Data Mining", Kimito Funatsu (editor), ISBN 978-95-307-154-1, Publ. InTech (2011), 147-166.
• Perzyk M., Krawiec K., Kozłowski J., Application of time-series analysis in foundry production, Archives of Foundry Engineering, 9 (2009), 109-114.
• Perzyk M., Rodziewicz A., Application of Time-series Analysis in Control of Chemical Composition of Grey Cast Iron, Archives of Foundry Engineering, 12 (2012), 171-175.
• Shahbaz M., Srinivas M., Harding J. A., Turner M., Product design and manufacturing process improvement using association rules, Proc. IMechE Part B: J. Engineering Manufacture, 220 (2006) 243-254.
• Shao Y. E., Wu C. H., Ho B. Y., Liu J. F., Identifying the Change Point of a Process with the Integration of SPC Charts and Neural Networks, Proc. Second International Conference on Innovative Computing, Information and Control, 2007. ICICIC '07, Kumamoto, Japan, 400-403.
• Shen L., Tay F. E. H., Qu L., Shen Y., Fault diagnosis using Rough Sets Theory, Computers in Industry 43 (2000) 61-72.
• Shu-guang H. E., Li L. I., Er-shi Q. I., Study on the Continuous Quality Improvement Systems of LED Packaging based on Data Mining, Proc. International Conference on Wireless Communications, Networking and Mobile Computing, 2007. WiCom 2007, Shanghai, China, 5625-5628.
• Stanley G. M., Guide to Fault Detection and Diagnosis, White Paper available from: http://gregstanleyandassociates.com/whitepapers/FaultDiagnosis/faultdiagnosis.htm, 2013.
• StatSoft, Inc. (2012). Electronic Statistics Textbook. Tulsa, OK: StatSoft. WEB: http://www.statsoft.com/textbook/.
• Stites R., Ward B. Walters R., Defect Prediction With Neural Networks, Proceedings of the Conference on Analysis of Neural Network Applications ANNA '91, May 29-31, 1991, Fairfax, VA, USA, 199-206.
• Tsang K. F., Lau H. C. W., Kwok S. K., Development of a data mining system for continual process quality improvement, Proc Inst Mech Eng Part B: J Eng Manuf 221 (2007) 179-193.
• Tseng T. L., Jothishankar M. C., Wu T., Xing G., Jiang F., Applying data mining approaches for defect diagnosis in manufacturing industry, IIE Annual Conference and Exhibition, Institute of Industrial Engineers, Houston, 2004, 1441-1447.
• Vazan P., Tanuska P., Kebisek M., Moravcik O., Data Mining Model Building as a Support for Decision Making in Production Management, Advances in Computer Science, Engineering & Applications, Proc. Second International Conference on Computer Science, Engineering and Applications (ICCSEA 2012), New Delhi, India, Vol. 1, May 25-27, 2012, 695-701.
• Wang K., Applying data mining to manufacturing: the nature and implications, J Intell Manuf, 18 (2007) 487-495.
• Wardell, D. G., Moskowitz, H. and Plante, R. D., Control charts in the presence of data correlation. Management Science, 38 (1992), 1084-1105.
• Zobel C. W., Cook D. F., Nottingham Q. J., An augmented neural network classification approach to detecting mean shifts in correlated manufacturing process parameters, International Journal of Production Research, 42 (2004) 741-758.