Enhancement of Drilling Safety and Quality Using Online Sensors and Artificial Neural Networks

• Autorzy: Liu T.- I. , Kumagai A. , Lee C.
• Języki publikacji: EN

Abstrakty

EN

Cutting force sensors and neural networks have been used for the occupational safety of the drilling process. The drill conditions have been online classified into 3 categories: safe, caution, and danger. This approach can change the drill just before its failure. The inputs to neural networks include drill size, feed rate, spindle speed, and features that were extracted from drilling force measure-ments. The outputs indicate the safety states. This detection system can reach a success rate of over 95%. Furthermore, the one misclassification during online tests was a one-step ahead pre-alarm that is acceptable from the safety and quality viewpoint. The developed online detection system is very robust and can be used in very complex manufacturing environments.

Słowa kluczowe

EN

drilling safety; online sensors; artificial neural networks

PL

bezpieczeństwo pracy; ocena ryzyka zawodowego; wiertnictwo; sieci neuronowe

• Wydawca: Taylor & Francis
• Czasopismo: International Journal of Occupational Safety and Ergonomics
• Rocznik: 2003
• Tom: Vol. 9, No. 1
• Strony: 37--56
• Opis fizyczny: Bibliogr. 30 poz., rys., tab., wykr.

Twórcy

autor

Liu T.- I.

• Department of Mechanical Engineering California State University, Sacramento, USA

autor

Kumagai A.

• Department of Mechanical Engineering California State University, Sacramento, USA

autor

Lee C.

• Department of Mechanical Engineering California State University, Sacramento, USA

Bibliografia

• 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, 8(4), 255-265.