Abstract
Recent advances in computers and manufacturing techniques have made it easy to collect and store all kinds of data in manufacturing enterprises. The problem of how to enable engineers and managers to understand large amount of data remains. Traditional data analysis methods are no longer the best alternative to be used. Data Mining (DM) approaches have created new intelligent tools for extracting useful information and knowledge automatically. All these will have a profound impact on current practices in manufacturing. In this paper the nature and implications of DM techniques in manufacturing and their implementations on product design and manufacturing are discussed.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Adriaans P., Zantinge D. (1998) Data Mining. Harlow, Addison-Wesley
Baragoin, C., Andersen, C. M., Bayerl, S., Bent, G., Lee, J., & Schommer, C. (2001). Mining your own business in telecoms, International technical support organization, IBM (http://www.redbooks.ibm.com).
Braha D. (Eds). (2001) Data Mining for design and manufacturing—methods and applications. The Nederland, Kluwer Academic Publishers
Breiman L., Friedman J., Olshen R., Stone C. (1984) Classification and regression trees. Belmont, CA, Wadsworth International Group
Chen S.Y., Liu X. (2005) Data Mining from 1994–2004: An application-oriented review. International Journal of Business Intelligence and Data Mining 1(1): 4–21
Chen W.C., Tseng S.S., Wang C.Y. (2005) A novel manufacturing defect detection methods using association rule mining techniques. Expert systems with applications 29(4): 807–815
Fayyad U., Piatetsky-Shapiro G., Smyth P. (1996) From Data Mining to knowledge discovery: An overview. In: Fayyad U., Piatetsky-Shapiro G., Smyth P., Uthurusamy R. (eds). Advances in Knowledge Discovery and Data Mining. MIT Press, Cambridge MA, pp. 1–36
Fayyad U., Stolorz P. (1997) Data Mining and KDD: Promise and challenge. Future Generation Computer Systems 13(2): 99–115
Fountain T., Dietterich T., Sundyka B, (2000). Mining IC test data to optimization VLSI testing. In Simoff J., Zaiane O. (eds). Proceedings 6th ACM SIGKDD Conference, (pp. 18–25). Boston, MA, USA.
Gardner M., Bieker J. (2000) Data mining solves tough semiconductor manufacturing problem. In: Simoff J., Zaiane O. (Eds), Proceedings 6th ACM SIGKDD Conference (pp. 376–383). Boston, MA, USA.
Hsu C.H., Wang M.J.J. (2005) Using decision tree-based Data Mining to establish a sizing system for manufacture of garments. International Journal of Advance Manufacturing Technology 26, 669–674
Kantardzic, M. (2003). Data Mining—concepts, models, methods, and algorithms. Wiley-Interscience
Kasravi, K. (1997). Data Mining and knowledge discovery in manufacturing. AUTOFACT’97, Information Technologies for the Manufacturing Enterprise, Michigan
Klosgen, W., & Zytkow, J. M. (Eds.), (2002). Handbook of Data Mining and knowledge discovery. Oxford University Press, Inc.
Kusiak A. (2006) Data mining: Manufacturing and service applications. International Journal of Production Research 44(18–19): 4175–4191
Last M., Kandel A. (2004) Discovering useful and understandable patterns in manufacturing data. Robotics and Autonomous Systems 49, 137–152
Li X.B., Sweigart J., Teng J., Donohue J., Thombs L. (2001) A dynamic programming based pruning method for decision trees. Journal on Computing 13(4): 332–344
Milne R., Drummond M., Renoux P. (1998). Predicting paper making defect on-line using data mining. Knowledge-Based Systems 11, 331–338
Quinlan J.R. (1992) C4.5: Programs for machine learning. San Mateo, CA, Morgan Kaufmann
Reinschmidt, J., Gottschalk, H., Kim, H., & Zwietering, D. (1999). Intelligent miner for data: Enhance your business intelligence. International technical support organization, IBM (http://www.redbooks.ibm.com).
Rokach L., Maimon O. (2006). Data mining for improving the quality of manufacturing: a feature set decomposition approach. Journal of Intellectual Manufacturing 17, 285–299
Tsironis L., Bilalis N., Moustakis V. (2005) Using machine learning to support quality management—framework and experimental investigation. The TQM magazine 17(3): 237–248
Wang, K. (2005). Applied computational intelligence in intelligent manufacturing systems. Advanced Knowledge International.
Wang, K., Fabien, C., Wang, Y., Yuan, Q., & Fang, M. (2004). Proceedings of the 6th international conference on frontiers of design and manufacturing. June 26–28, Xi’an, China.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Wang, K. Applying data mining to manufacturing: the nature and implications. J Intell Manuf 18, 487–495 (2007). https://doi.org/10.1007/s10845-007-0053-5
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10845-007-0053-5