Skip to main content
SpringerLink
Log in

Data mining algorithm for manufacturing process control

  • Original Article
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

In this paper, a new data mining algorithm based on the rough sets theory is presented for manufacturing process control. The algorithm extracts useful knowledge from large data sets obtained from manufacturing processes and represents this knowledge using “if/then” decision rules. Application of the data mining algorithm developed in this paper is illustrated with an industrial example of rapid tool making (RTM). RTM is a technology that adopts rapid prototyping (RP) techniques, such as spray forming, and applies them to tool and die making. A detailed discussion on how to control the output of the manufacturing process using the results obtained from the data mining algorithm is also presented. Compared to other data mining methods, such decision trees and neural networks, the advantage of the proposed approach is its accuracy, computational efficiency, and ease of use.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Ruggieri S (2002) Efficient C 4.5. IEEE Trans Knowl Data Eng 14(2):438–444

    Article  Google Scholar 

  2. Cantu-Paz E, Kamath C (2003) Inducing oblique decision trees with evolutionary algorithms. IEEE Trans Evol Comput 7(1):54–68

    Article  Google Scholar 

  3. Breiman L, Friedman JH (1984) Classification and regression trees. Wansworth International, Belmont, CA

  4. Quinlan JR (1986) Induction of decision trees. Mach Learn 1:81–106

    Google Scholar 

  5. Quinlan JR (1993) C4.5: programs for machine learning. Morgan Kaufmann, San Mateo, CA

  6. Murthy SK (1998) Automatic construction of decision trees from data: a multidisciplinary survey. Data Mining Knowl Discovery 2:345–389

    Article  MathSciNet  Google Scholar 

  7. Kusiak A (2000) Decomposition in data mining: an industrial case study. IEEE Trans Electr Packag Manuf 23(4):345–353

    Article  Google Scholar 

  8. Mehta M, Agrawal R, Risanen J (1996) SLIQ: a fast scalable classifier for data mining. In: Proceedings of the Fifth International Conference on Extending Database Technology, Avignon, France

  9. Shafer J, Agrawal R, Mehta M (1996) SPRINT: a scalable parallel classifier for data mining. In: Proceedings of the International Conference on Very Large Databases, Morgan Kaufmann, pp 544–555

  10. Gehrke JE, Ramakrishnan R, Ganti V (2000) RainForest – a framework for fast decision tree construction of large datasets. Data Mining Knowl Discovery 4:127–162

    Article  Google Scholar 

  11. Tickle B, Andrews R, Golea M, Diederich J (1998) The truth will come to light: directions and challenges in extracting the knowledge embedded within trained artificial neural networks. IEEE Trans Neural Netw 9:1057–1068

    Article  Google Scholar 

  12. Lu HJ, Setiono R, Liu H (1996) Effective data mining using neural networks. IEEE Trans Knowl Data Eng 8:957–961

    Article  Google Scholar 

  13. Zhang GP (2000) Neural networks for classification: a survey. IEEE Trans Syst Man Cybernetics 30(4):451–462

    Article  Google Scholar 

  14. Ripley BD, Ripley RM (1998) Neural networks as statistical methods in survival analysis. In: Dubrowski R, Gant V (eds) Artificial Neural Networks: Prospects for Medicine, Landes Biosciences Publishers, Georgetown, TX, pp 1–13

  15. Baxt WG (1995) Applications of artificial neural networks to clinical Medicine. Lancet 346:1135–1138

    Article  Google Scholar 

  16. Ostermark R (1997) A fuzzy neural network algorithm for multigroup Classification. Fuzzy Sets Syst 105:113–122

    Article  MathSciNet  Google Scholar 

  17. Hemsathapat K, Dagli CH, Enke D (2001) Using a neuro-fuzzy-genetic data mining architecture to determine a marketing strategy in a charitable organization’s donor database. In: Proceedings of the IEEE International Engineering Management Conference, Albany, NY, pp 64–69

  18. Pawlak Z Rough sets and decision analysis. INFOR 38(3):132–143

    Google Scholar 

  19. Pawlak Z (1991) Rough sets – theoretical aspect of reasoning about data. Kluwer, Dordrecht

  20. Pawlak Z (1982) Rough sets. Int J Comput Inf Sci 11:341–356

    Article  MATH  MathSciNet  Google Scholar 

  21. Kusiak A, Kurasek C (2001) Data mining of printed-circuit board defects. IEEE Trans Robot Automat 17(2):191–197

    Article  Google Scholar 

  22. Kusiak A, Law IH, Dick M (2001) The G-algorithm for extraction of robust decision rules – children’s postoperative intra-atrial arrhythmia case study. IEEE Trans Inf Technol Biomed 5(3):234–255

    Article  Google Scholar 

  23. Ohrn A, Ohno-Machado L, Rowland T (1998) Building manageable rough set classifiers. In: Proceedings of the AMIA Symposium, pp 60–64

  24. Kusiak A, Kern JA, Kernstine KH, Tseng BT (2000) Autonomous decision-making: a data mining approach. IEEE Trans Inf Technol Biomed 4(4):274–285

    Article  Google Scholar 

  25. Das-Gupta P (1988) Rough sets and information retrieval. ACM Press, New York, pp 567–581

  26. Kanungo T, Netanyaho NS, Wu AY (2000) An efficient k-means clustering algorithm: analysis and implementation. IEEE Trans Pattern Anal Mach Intell 24(7):887–892

    Google Scholar 

  27. Han J, Kamber M (2001) Data mining – concepts and techniques. Morgan Kaufmann, San Francisco, CA

  28. Mohanty PS, Allor R, Lechowicz P, Parker R, Craig J (2003) Particle temperature and velocity characterization in spray tooling process by thermal imaging technique. Therm Spray ASM, pp 1183–1190, May, Orlando, FL

Download references

Author information

Authors and Affiliations

  1. Department of Industrial and Manufacturing Systems Engineering, University of Michigan – Dearborn, USA

    Hovhannes Sadoyan & Armen Zakarian

  2. Department of Mechanical Engineering, University of Michigan – Dearborn, Dearborn, MI, 48128, U.S.A.

    Pravansu Mohanty

Authors
  1. Hovhannes Sadoyan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Armen Zakarian
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pravansu Mohanty
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hovhannes Sadoyan.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Sadoyan, H., Zakarian, A. & Mohanty, P. Data mining algorithm for manufacturing process control. Int J Adv Manuf Technol 28, 342–350 (2006). https://doi.org/10.1007/s00170-004-2367-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-004-2367-1

Keywords

Search

Navigation