Enhancement of drilling safety and quality using online sensors and artificial neural networks

International Journal of Occupational Safety and Ergonomics

Volumn 9, Issue 1, 2003, Pages 37-56

  Liu, Tien I ^a   Kumagai, Akihiko ^a   Lee, Chongchan ^a  

^a CALIFORNIA STATE UNIVERSITY   (United States)

Author keywords

Artificial neural networks; Drilling safety; Online sensors

Indexed keywords

ARTICLE; ARTIFICIAL NEURAL NETWORK; EQUIPMENT; HUMAN; INSTRUMENTATION; OCCUPATIONAL HEALTH; ONLINE SYSTEM; STATISTICS; UNITED STATES;

EQUIPMENT SAFETY; HUMANS; NEURAL NETWORKS (COMPUTER); OCCUPATIONAL HEALTH; ONLINE SYSTEMS; UNITED STATES;

EID: 0038320793     PISSN: 10803548     EISSN: None     Source Type: Journal    
DOI: 10.1080/10803548.2003.11076553     Document Type: Article

References (30)

1. Altintas, Y. (1992). Prediction of cutting forces and tool breakage in milling from feed drive current measurements. ASME Journal of Engineering for Industry, 114, 386-392.
2. Barker, R.W., Klutke, G., & Hinich, M.J. (1993). Monitoring rotating tool wear using higherorder spectral features. ASME Journal of Engineering for Industry, 115, 23-29.
3. Etherton, J.R., & Myers, M.L. (1990). Machine safety research at NIOSH and the future directions. International Journal of Industrial Ergonomics, 6, 163-174.
4. Govekar, E., & Grabec, I. (1994). Self-organizing neural network application to drill wear classification. ASME Journal of Engineering for Industry, 116(2), 233-238.
5. He, W., Zhang, Y.F., Lee, K.S., & Liu, T.I. (2001). Development of a fuzzy-neuro system for parameter resetting of injection molding. ASME Journal of Manufacturing Science and Engineering, 123, 110-118.
6. Kumagai, A., Hozian P., & Kirkland M. (2000). Neuro-fuzzy model based feedback controller for shape memory alloy actuator. In Proceedings of SPIE’s 7th Annual International Symposium on Smart Structures and Materials, Newport Beach, California (pp. 291-299). Bellingham, WA, USA: Society of Photo-Optical Instrumentation Engineer (SPIE).
7. Li, G.S., Lau, W.S., & Zhang, Y.Z. (1992). In-process drill wear and breakage monitoring for a machining center based on cutting force parameters. International Journal of Machine Tools and Manufacture—Design, Research and Application, 32(6), 855-867.
8. Liu, T.I. (1998). Tools for intelligent manufacturing process and systems: Neural networks, fuzzy logic, and expert systems. In The CRC Handbook of Mechanical Engineering (pp.13-98-13-102). New York, NY, USA: CRC Press.
9. Liu, T.I., & Anantharaman, K.S. (1994). Intelligent classification and measurement of drill wear. ASME Journal of Engineering for Industry, 116, 392-397.
10. Liu, T.I., Chen, W.Y., & Anantharaman, K.S. (1998). Intelligent detection of drill wear. Journal of Mechanical Systems and Signal Processing, 12(6), 863-873.
11. Liu, T.I., Chen, W.Y., & Ko, E.J. (1994). Intelligent recognition of drill wear states. ASM Journal of Materials Engineering and Performance, 3(4), 490-495.
12. Liu, T.I., Lee, J., & Wang, Y.C. (2001). On-line monitoring of boring tools using virtual instrumentation and neural networks. In Proceedings of the 6th International Conference on Manufacturing Technology, Hong Kong (pp. 1-5). Hong Kong: Sino Electronic Publishing.
13. Liu, T.I., Wang, Y.C., & Lee, J. (2000). Predictive monitoring for precision boring of titanium parts. In Proceedings of the NSF Workshop on Intelligent Maintenance Systems, Milwaukee, Wisconsin, USA, November 16-17, 2000 (pp. 1-27). Milwaukee, WI, USA: NSF Center for Intelligent Maintenance Systems.
14. Millard, D.L. (1991). Toward a reliable safety sensor implementation for industrial automation. International Journal of Industrial Ergonomics, 7, 277-286.
15. National Safety Council. (1992). Accident prevention manual for business and industry— Engineering and technology (10th ed.). Itasca, IL, USA: Author.
16. National Safety Workplace Institute (NSWI). (1992). Basic information on workplace safety and health in the United States including a state by state analysis and profile. Chicago, IL, USA: Author.
17. Niu, Y.M., Wong, Y.S., Hong, G.S., & Liu, T.I. (1998). Multi-category classification of tool conditions using wavelet packets and ART2 network. ASME Journal of Manufacturing Science and Engineering, 120, 807-816.
18. Park, J.J., & Ulsoy, A.G. (1993a). On-line flank wear estimation using an adaptive observer and computer vision, part 1: Theory. ASME Journal of Engineering for Industry, 115, 30-36.
19. Park, J.J., & Ulsoy, A.G. (1993b). On-line flank wear estimation using an adaptive observer and computer vision, part 2: Experiment. ASME Journal of Engineering for Industry, 115, 37-43.
20. Purushothaman, S., & Srinivasa, Y.G., (1994). A back propagation algorithm applied to tool wear monitoring. International Journal of Machine Tools and Manufacture—Design, Research and Application, 34(5), 625-631.
21. Ramamurthi, K., & Hough, C.L., Jr. (1993). Intelligent real-time predictive diagnostics for cutting tools and supervisory control of machining operations. ASME Journal of Engineering for Industry, 115, 268-277.
22. Rangwala, S., & Dornfeld, D. (1990). Sensor integration using neural networks for intelligent tool condition monitoring. ASME Journal of Engineering for Industry, 112, 219-228.
23. Roth, J.T., & Pandit, S.M. (1999). Monitoring end-mill wear and predicting tool fracture using accelerometers. ASME Journal of Manufacturing Science and Engineering, 121, 559-567.
24. Sata, T., Matsushima, K., Nagakura, T., & Kono, E. (1973). Learning and recognition of the cutting states by the spectrum analysis. Annals of the CIRP, 22(1), 41-42.
25. Sereno, M.E. (1993). Neural computation of pattern motion-modeling stages of motion analysis in the primate visual cortex. Cambridge, MA, USA: MIT Press.
26. Society of Manufacturing Engineers. (1976). Tools and manufacturing engineers handbook (Vol. 1, Machining, 4th ed.). Dearborn, MI, USA: Author.
27. Society of Manufacturing Engineers. (1984). Fundamentals of tool design (2nd ed.). Dearborn, MI, USA: Author.
28. Subramanian, K., & Cook, N.H. (1977). Sensing of drill wear and prediction of drill life. ASME Journal of Engineering for Industry, 103, 295-301.
29. Thangaraj, A., & Wright, P.K. (1988). Computer-assisted prediction of drill-failure using in-process measurements of thrust force. ASME Journal of Engineering for Industry, 110(2), 192-200.
30. Xie, Q., Bayoumi, A.E., & Kendall, L.A. (1990). On tool wear and its effect on machined surface integrity. ASM Journal of Materials Shaping Technology, #(4), 255-265.