ONLINE MONITORING OF MACHINEOPERATIONSBY NEURAL NETWROK
0
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
Neural Networks have been used on line to classify drilling conditions into three categories: safe, caution and danger. This approach allows changing a drill just before failure, a valuable benefit. Drill size, feed rate, spindle speed and other operational features are used as inputs of neural networks. The output of the neural network indicates the drilling condition. An on line drill condition detection system can achieve a success rate over 95%. Numerous values of initial weights, learning rates, and smoothing factors were systematically explored and tested to achieve high reliability.
Abstract
Neural Networks have been used on line to classify drilling conditions into three categories: safe, caution and danger. This approach allows changing a drill just before failure, a valuable benefit. Drill size, feed rate, spindle speed and other operational features are used as inputs of neural networks. The output of the neural network indicates the drilling condition. An on line drill condition detection system can achieve a success rate over 95%. Numerous values of initial weights, learning rates, and smoothing factors were systematically explored and tested to achieve high reliability.
참고문헌 (14)
- Barker, R. W., "Monitoring Rotating Tool Wear Using Higher?Order Spectral Features", ASME Journal of Engineering for Industry, 115, pp.23~29 (1993)
- Fu, Lianyu, "Neural Network Based On?Line Detection of Drill Breakage in Micro Drilling Process", Proceedings of the 9th International Conference on Neural Information Processing, 4, pp.2054~2058 (2002)
- Huang Po?Tsang, "Neural Network?Based Tool Breakage Monitoring System for End Milling operations", Journal of Industrial Technology, 16(2), pp.2~7 (2000)
- Judd, J. S., "Neural Network Design and the Complexity of Learning" (1990), The MIT Press
- Lawrence, J., "Introduction to Neural Networks Design,Theory,and Application" (1994), California Scientific Software Press
- Li, G. S., "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), pp.855~867 (1992)
- Liu, T. I., "Intelligent Recognition of Drill Wear States", ASM Journal of Materials Engineering and Performance, 3(4), pp.490~495 (1994)
- Loquasto, Fred, "Monitoring Model Predictive Control Systems Using Pattern Classification and Neural Networks", Industrial & Engineering Chemistry Research, 42(20), pp.4689~4701 (2003)
- National Safety Council, "Accident Prevention Manual for Business and Industry", pp.703~723 (1992), Engineering and Technology
- NSWI, "Basic Information on Workplace Safety and Health in the United States", pp.1~10 (1992), National Safety Workplace Institute Including a State by State Analysis and Profile
- Purushothaman, S., "A Back Propagation Algorithm Applied to Tool Wear Monitoring", International Journal of Machine Tools and ManufactureDesign Research and Application, 34(5), pp.625~631 (1994)
- Subramanian, K., "Sensing of Drill Wear and Prediction of Drill Life", ASME Journal of Engineering for Industry, pp.295~301 (1977)
- Widyanto, Muhammad R., "Improving recognition and generalization capability of back?propagation NN using a self?organized network inspired by immune algorithm(SONIA)", Applied Soft Computing, 6, pp.72~84 (2005)
- Xie, Q., "On Tool Wear and Its Effect on Machined Surface Integrity", ASM Journal of Materials Shaping Technology, 8(4), pp.255~265 (1990)