Towards zero-defect manufacturing (ZDM)—a data mining approach

doi:10.1007/s40436-013-0010-9

Abstract

Abstract: The quality of a product is dependent on both facilities/equipment and manufacturing processes. Any error or disorder in facilities and processes can cause a catastrophic failure. To avoid such failures, a zero- defect manufacturing (ZDM) system is necessary in order to increase the reliability and safety of manufacturing systems and reach zero-defect quality of products. One of the major challenges for ZDM is the analysis of massive raw datasets. This type of analysis needs an automated and self-organized decision making system. Data mining (DM) is an effective methodology for discovering interesting knowledge within a huge datasets. It plays an important role in developing a ZDM system. The paper presents a general framework of ZDM and explains how to apply DM approaches to manufacture the products with zero-defect. This paper also discusses 3 ongoing projects demonstrating the practice of using DM approaches for reaching the goal of ZDM.

Key words: Data mining (DM) , Quality of product , Zero- defect manufacturing (ZDM) , Knowledge discovery

Ke-Sheng Wang. Towards zero-defect manufacturing (ZDM)—a data mining approach[J]. Advances in Manufacturing, 2013, 1(1): 62-74.

TrendMD

References

1. Arumugam V, Antony J, Douglas A (2012) Observation: a lean tool for improving the effectiveness of lean six sigma. TQM J 24(3):275–287

2. Halpin JF (1996) Zero defects: a new dimension in quality assurance. McGraw-Hill, New York

3. Crosby PB (1979) Quality is free: the art of making qualify certain. McGraw Hill, New York

4. Calvin TW (1983) Quality control techniques for ‘‘zero defects’’. IEEE Trans Compon Hybrids Manuf Technol 6:323–328

5. Calvin TW (1983) Quality control techniques for ‘‘Zero Defects’’. IEEE Trans Compon Hybrids Manuf Technol 6:323–328

6. Wang K (2007) Applying data mining to manufacturing: the nature and implications. J Intell Manuf 18(4):487–495

7. Wang K (2005) Applied computational intelligence in intelligent manufactruing systems. Advanced Knowledge International, Adelaide

8. Baragoin C, Andersen CM, Bayerl S et al (2001) Mining your own business in health care using DB2 intelligent miner for data. IBM Redbook, California

9. Marishin AD, Stasenko IK (1970) Operating experience of pump manufacturing plants using the zero-defect system. Khimieheskoei Neftyanoe Mashinostroenie: 44–46

10. Alpern P, Nelle P, Barti E et al (2009) On the way to zero defect of plastic-encapsulated electronic power devices—Part I: metallization. Device Mater Reliab 9:269–278

11. Alpern P, Nelle P, Barti E et al (2009) On the way to zero defect of plastic-encapsulated electronic power devices—Part II: molding compound. Device Mater Reliab 9:279–287

12. Alpern P, Nelle P, Barti E et al (2009) On the way to zero defect of plastic-encapsulated electronic power devices—Part III: chip coating, passivation, and design. Device Mater Reliab 9:288–295

13. Ga¨bler U, O¨ sterreicher I, Bosk P et al (2007) Zero defect manufacturing as a challenge for advanced failure analysis. In: Advanced semiconductor manufacturing conference, Stresa, Italy, 11–12 June 2007

14. Raina R (2008) Achieving zero-defects for automotive applications. IEEE international test conference, Santa Clara, USA, 28–30 Oct. 2008

15. Linger RC (1993) Cleanroom software engineering for zero defect software. In: The 15th international conference on software engineering, Baltimore, USA, 17–21 May 1993

16. Mills HD, Dyer M, Linger RC (1987) Cleanroom software engineering. IEEE Softw 4(5):19–25

17. Westkamper E, Warnecke HJ (1994) Zero-defect manufacturing by means of a learning supervision of process chains. Ann CIRP 43(1):406–408

18. Dakshinamoorthy S (2008) Zero defects quality and reliability challenges for growing markets. In: Integrated reliability workshop final report, South Lake Tahoe, USA, 12–16 Oct. 2008

19. Hu W, Starr AG, Zhou Z et al (2000) A systematic approach to integrated fault diagnosis of flexible manufacturing systems. Int J Mach Tools Manuf 40:1560–1587

20. Kegg RL (1984) On-line machine and process diagnostics. Annals of the CIRP 32(2):469–573

21. Zhou ZD, Chen YP, Fuh JH et al (2000) Integrated condition monitoring and fault diagnosis for modern manufacturing system. Ann CIRP 49(1):387–390

22. Piewak S (1991) A predictive monitoring and diagnosis system for manufacturing. Ann CIRP 40(1):401–404

23. Monostori L (1993) A step towards intelligent manufacturing: modeling and monitoring of manufacturing processes with artificial neural network. Ann CIRP 42(1):485–488

24. Shinno H, Hashizume H (1997) In-process monitoring method for machining environment based on simultaneous multi-phenomena sensing. Ann CIRP 46(1):53–56

25. Wang K (2003) Intelligent condition monitoring and diagnosis systems: a computational intelligence approach. IOSPress,Amsterdam
26. Wang K (2002) Intelligent condition monitoring and diagnosis systems. IOS Press, Amsterdam

27. Lee J, Ni J, Djurdjanovic D et al (2006) Intelligent prognostics tools and e-maintenance. Comput Ind 57:476–489

28. Allocca A, Stuart A (1984) Transducers: theory and applications. Prentice-Hall. Englewood Cliff, NJ

29. Zhao G, Jiang D, Li K et al (2005) Data mining for fault diagnosis and machine learning. Key Eng Mater 293(294):175–182

30. Lian J, Lai X, Lin Z et al (2002) Application of data mining and process knowledge discovery in sheet metal assembly dimensional variation diagnosis. J Mater Process Technol 129:315–320

31. Wang H, Chen P, Wang S (2009) Condition diagnosis method based on statistic features and information divergence. In: 2009 sixth international conference on fuzzy systems and knowledge discovery, Tianjin, China, 14–16 August 2009

32. Ray A, Tangirala S (1994) Stochastic modeling of fatigue crack dynamics for on-line failure prognostics. IEEE Trans Control Syst Technol 4(4):443–451

33. Schwabacher MA (2005) A survey of data-driven prognostics. American Institute of Aeronautics and Astronautics Report, Virginia
34. Wang K (2001) Computational intelligent in agile manufacturing engineering. In: Gunasekaran A (ed) Agile manufacturing: the 21st century competitive strategy. Elsevier, New York, pp 297–315

35. Amin AMA, Korfally MI, Sayed AA et al (2007) Swarm intelligence-based controller of two-asymmetric windings induction motor. In: Processings of IEEE international electric machines and drives conference, Antalya, Turkey, 3–5 May 2007

36. Zomaya AY (2006) Handbook of natural-inspired and innovative computing: integrating classical models with emerging technologies. Springer Science ? Business Media, Inc., New York

37. Bontoux B, Feillet D (2008) Ant colony optimization for the traveling purchaser problem. Comput Oper Res 35:628–637

38. Saha S, Pathak SS (2007) A novel swarm intelligence based routing scheme for MANET using weighted pheromone paths. In: IEEE military communications conference MILCOM military communications conference, Orlando

39. Padma S, Bhuvaneswari R, Subramanian S (2007) Application of soft computing techniques to induction motor design. COMPELInt J Comp Mathematics Elec Electron Eng 26(5):1324–1345

40. Wang K, Wang Y (2012) Data mining for zero–defect manufacturing. Tpir Academic Press, London

41. Connolly C (2009) Machine vision advances and applications. Assem Autom 29(2):106–111

42. Barbero BR, Ureta ES (2011) Comparative study of different digitization techniques and their accuracy. Comput Aided Des 43(2):188–206

43. Xu J, Xi N, Zhang C et al (2011) Real-time 3D shape inspection system of automotive parts based on structured light pattern. Opt Laser Technol 43(1):1–8

44. Huang W, Kovacevic R (2011) A laser-based vision system for weld quality inspection. Sensors 11(1):506–521

45. Lin CS, Lin CH, Chen DC et al (2011) Measurement method of three-dimensional profiles of small lens with gratings projection and a flexible compensation system. Expert Syst Appl 38(5): 6232–6238

46. Gentilini I, Shimada K (2011) Predicting and evaluating the postassembly shape of thin-walled components via 3D laser digitization and FEA simulation of the assembly process. Comput Aided Des 43(3):316–328

47. Bruno F, Bianco G, Muzzupappa M et al (2011) Experimentation of structured light and stereo vision for underwater 3D reconstruction. ISPRS J Photogrammet Remote Sens 66(4):508–518

48. Wang K, Yu Q (2011) 3D computer vision using structured light systems: principles, systems and applications. SINTEFF Report, Trondheim

49. Wang K, Yu Q (2011) Accurate 3D object measurement and inspection using structured light systems. In: The 12th international conference on computer systems and technologies, Wien, Austria, 16–17 June 2011

50. Lee MY, Yang CS (2010) Entropy-based feature extraction and decision tree induction for breast cancer diagnosis with standardized thermograph images. Comput Methods Programs Biomed 100(3):269–282

51. Ravikumar S, Ramachandran KI, Sugumaran V (2011) Machine learning approach for automated visual inspection of machine components. Expert Syst Appl 38(4):3260–3266

52. Skotheim Ø, Couweleers F (2004) Structured light projection for accurate 3D shape determination. ICEM12-12th international conference on experimental mechanics, Bari, Italy, 29 August–2 September 2004

53. Tellaeche A, Arana R, Ibarguren A et al (2010) Automatic quality inspection of percussion cap mass production by means of 3D machine vision and machine learning techniques. Hybrid artificial intelligence systems, San Sebastian

54. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297

55. Bishop CM (2006) Pattern recognition and machine learning. Springer, New York