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Monitoring and diagnosis of the tapping process for product quality and automated manufacturing

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Abstract

Tapping has been widely used throughout industry, and its proper operation is paramount in ensuring product quality. Therefore, monitoring and diagnosis are needed to detect the tapping process conditions. They are also important for automated manufacturing. In this work, a combination of ten indices of the tapping process was extracted from tapping torque, thrust force, and lateral forces. The Sequential Forward Search (SFS) algorithm has been used to select the best feature sets. Adaptive Neuro-Fuzzy Inference Systems (ANFIS) were used for the monitoring and diagnosis of tapping process. A 3 × 2 ANFIS structure can distinguish normal tapping process from abnormal tapping process with 100 % reliability. The tapping process conditions can be further classified into five categories with over 95 % success rate using a 10 × 2 ANFIS structure for diagnostic purpose. In simple words, monitoring and diagnosis of tapping process can be carried out successfully using SFS and ANFIS.

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References

  1. Baker A (1976) Fundamentals of taps and tapping. Cutting Tool Engineering 28(3–4):6–10

    Google Scholar 

  2. Dogra APS, Kapoor SG, DeVor RE (1999) Mechanistic model for tapping process with emphasis on process faults and hole geometry. Proceedings of the 1999 ASME MED10, 14–19 Nov, 271–283

  3. Althoefer K, Lara B, Seneviratne LD (2005) Monitoring of self-tapping screw fastenings using artificial neural networks. J Manuf Sci Eng 127:236–243

    Article  Google Scholar 

  4. Coelho RT, Arai R, Watanuki HM, Borges E (2006) An experimental investigation on wear aspects of tapping operation on hardened steels. Mach Sci Technol 10(6):235–250

    Article  Google Scholar 

  5. Kalvoda T, Hwang YR (2010) A cutter tool monitoring in machining process using Hilbert–Huang transform. Int J Mach Tool Manuf 50(5):495–501

    Article  Google Scholar 

  6. Lee S, Li L, Ni J (2010) Online degradation assessment and adaptive fault detection using modified hidden Markov model. ASME J Manufact Sci Eng 132:021010–1–021010–11

    Google Scholar 

  7. Li W, Li D, Ni J (2003) Diagnosis of tapping process using spindle motor current. Int J Mach Tool Manuf 43:73–79

    Article  Google Scholar 

  8. Cao H, Chen X, Zi Y, Ding F, Chen H, Tan J, He Z (2008) End milling tool breakage detection using lifting scheme and Mahalanobis distance. Int J Mach Tool Manuf 48:141–151

    Article  Google Scholar 

  9. Chow MY (1997) Methodologies of using neural network and fuzzy logic for motor incipient fault detection. World Scientific, Singapore

    Book  Google Scholar 

  10. Jammu VB, Danai K, Lewicki DG (1998) Experimental evaluation of a structural-based connectionist network for fault diagnosis of helicopter gearboxes. ASME J Mech Des 120(1):106–112

    Article  Google Scholar 

  11. Jantunen E, Vaajoensuu E (2010) Self adaptive diagnosis of tool wear with a microcontroller. J Intell Manuf 21(2):223–230

    Article  Google Scholar 

  12. Li B, Chow MY, Tipsuwan Y, Hung JC (2000) Neural-network-based motor rolling bearing fault diagnosis. IEEE Trans Ind Electron 47(5):1060–1069

    Article  Google Scholar 

  13. Wang X, Wang W, Huang Y, Nguyen N, Krishnakumar K (2008) Design of neural network-based estimator for tool wear modeling in hard turning. J Intell Manuf 19(4):383–396

    Article  Google Scholar 

  14. Chen JC (2000) An effective fuzzy-nets training scheme for monitoring tool breakage. J Intell Manuf 11:85–101

    Article  Google Scholar 

  15. Choi YJ, Park MS, Chu CN (2008) Prediction of drill failure using features extraction in time and frequency domains of feed motor current. Int J Mach Tool Manuf 48:29–39

    Article  Google Scholar 

  16. Devillez A, Dudzinski D (2008) Tool vibration detection with eddy current sensors in machining process and computation of stability lobes using fuzzy classifiers. Mech Syst Signal Process 21:441–456

    Article  Google Scholar 

  17. Ghosh N, Ravi YB, Patra A, Mukhopadhyay S, Paul S, Mohanty AR, Chattopadhyay AB (2007) Estimation of tool wear during CNC milling using neural network-based sensor fusion. Mech Syst Signal Process 21:466–479

    Article  Google Scholar 

  18. Liu TI, Kumagai A, Wang YC, Song SD, Fu Z, Lee J (2010) On-line monitoring of boring tools for control of boring operations. Int J Robotic Comput Integr Manufact 26:230–239

    Article  Google Scholar 

  19. Mesina OS, Langari R (2001) A neuro-fuzzy system for tool condition monitoring in metal cutting. J Manuf Sci Eng 123:312–318

    Article  Google Scholar 

  20. Rubio EM, Teti R (2009) Cutting parameters analysis for the development of a milling process monitoring system based on audible energy sound. J Intell Manuf 20(1):43–45

    Article  Google Scholar 

  21. Sharma VS, Sharma SK, Sharma AK (2008) Cutting tool wear estimation for turning. J Intell Manuf 19(1):99–108

    Article  Google Scholar 

  22. Sick B (2002) Review on-line and indirect tool wear monitoring in turning with artificial neural networks: a review of more than a decade of research. Mech Syst Signal Process 16(4):487–546

    Article  Google Scholar 

  23. Subrahmanya N, Shin YC (2008) Automated sensor selection and fusion for monitoring and diagnostics of plunge grinding. ASME J Manufact Sci Eng 130:031014–1–031014–11

    Google Scholar 

  24. Suprock CA, Roth JT, Downey LM (2009) Endmill condition monitoring and failure forecasting method for curvilinear cuts of nonconstant radii. ASME J Manufact Sci Eng 131:021003–1–021003–8

    Google Scholar 

  25. Yeo SH, Khoo LP, Neo SS (2000) Tool condition monitoring using reflectance of chip surface and neural network. J Intell Manuf 11:507–514

    Article  Google Scholar 

  26. Chen YB, Sha JL, Wu SM (1990) Diagnosis of the tapping process by information measure and probability voting approach. J Eng Ind 122:319–325

    Article  Google Scholar 

  27. Liu TI, Chen WY, Ko EJ (1994) Intelligent recognition of drill wear states. ASM J Mater Eng Perform 3:490–495

    Article  Google Scholar 

  28. Liu TI, Ko EJ, Sha SL (1990) Intelligent monitoring of tapping tools. J Mater Shap Technol 8:249–254

    Article  Google Scholar 

  29. Liu TI, Ko EJ, Sha SL (1991) Diagnosis of tapping operations using an AI approach. J Mater Shap Technol 9:39–46

    Article  Google Scholar 

  30. Liu TI, Kumagai A, Lee C (2003) Enhancement of drilling safety and quality using online sensors and artificial neural networks. Int J Occup Saf Ergon (JOSE) 9(1):37–56

    Google Scholar 

  31. Chungchoo C, Saini D (2002) On-line tool wear estimation in CNC turning operations using fuzzy neural network model. Int J Mach Tool Manuf 42:29–40

    Article  Google Scholar 

  32. He W, Zhang YF, Lee KS, Liu TI (2001) Development of a fuzzy-neuro system for parameter resetting of injection molding. J Manuf Sci Eng 123:110–118

    Article  Google Scholar 

  33. Kumagai A, Liu TI, Hozian P (2006) Control of shape memory alloy actuators with a neuro-fuzzy feedforward model element. J Intell Manuf 17:45–56

    Article  Google Scholar 

  34. Liu TI, Ordukhani F, Jani D (2005) Monitoring and diagnosis of roller bearing conditions using neural networks and soft computing. Int J Knowl Base Intell Eng Syst 9:1–9

    Article  Google Scholar 

  35. Liu TI, Song SD (2004) Intelligent monitoring and measurement of tool wear for the turning of stainless steel parts. Proceedings of the 2004 ASME International Mechanical Engineering Congress and Exposition, Anaheim, California, 13–19 Nov, 1–7

  36. Peng Y (2004) Intelligent condition monitoring using fuzzy inductive learning. J Intell Manuf 15:373–380

    Article  Google Scholar 

  37. Zhao D, Xue D (2010) Parametric design with neural network relationships and fuzzy relationships considering uncertainties. Comput Ind 61:287–296

    Article  Google Scholar 

  38. Demuth M, Beale M (2004) Fuzzy logic toolbox user’s guide for use with MATLAB. The Math Works Inc., Natick

    Google Scholar 

  39. Jang JS (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–685

    Article  Google Scholar 

  40. Jang JSR, Sun CT, Mizutani E (1997) Neuro-fuzzy and soft computing. Prentice-Hall, Englewood Cliffs

    Google Scholar 

  41. Mooney R, Shavlik J, Towell G, Gove A (1990) An experimental comparison of symbolic and connectionist learning algorithms. Morgan Kaufmann, San Mateo

    Google Scholar 

  42. Cover TM (1965) Geometrical and statistical properties of systems of linear inequalities with applications in pattern recognition. IEEE Trans Comput 14:326–334

    Article  MATH  Google Scholar 

  43. Devijver PA, Kittler J (1982) Pattern recognition—a statistical approach. Prentice-Hall, Englewood Cliffs

    MATH  Google Scholar 

  44. Whitney A (1971) A direct method of non-parametric measurement selection. IEEE Trans Comput 20:1100–1103

    Article  MathSciNet  MATH  Google Scholar 

  45. Fuzzy Logic Toolbox (2011) Adaptive neuro-fuzzy inference system, Mathworks

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Authors and Affiliations

  1. Department of Mechanical Engineering, California State University - Sacramento, Sacramento, CA, USA

    Tien-I Liu & Junyi Lee

  2. Mori Seiki Manufacturing Inc., Davis, CA, USA

    George Liu

  3. School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, Singapore

    Zhang Wu

Authors
  1. Tien-I Liu
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  2. Junyi Lee
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  3. George Liu
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  4. Zhang Wu
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Correspondence to Tien-I Liu.

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Liu, TI., Lee, J., Liu, G. et al. Monitoring and diagnosis of the tapping process for product quality and automated manufacturing. Int J Adv Manuf Technol 64, 1169–1175 (2013). https://doi.org/10.1007/s00170-012-4058-7

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  • DOI: https://doi.org/10.1007/s00170-012-4058-7

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