Tool wear estimation in micro-machining.: Part II: neural-network-based periodic inspector for non-metals
Cutting forces are small, and in many cases insignificant, compared with noise during the micro-machining of many non-metals. The Neural-Network-based Periodic Tool Inspector (N 2PTI) is introduced to evaluate tool condition periodically on a test piece during the machining of non-metal workpieces....
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| Veröffentlicht in: | International journal of machine tools & manufacture 2000, Vol.40 (4), p.609-620 |
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| container_title | International journal of machine tools & manufacture |
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| creator | Tansel, I.N. Arkan, T.T. Bao, W.Y. Mahendrakar, N. Shisler, B. Smith, D. McCool, M. |
| description | Cutting forces are small, and in many cases insignificant, compared with noise during the micro-machining of many non-metals. The Neural-Network-based Periodic Tool Inspector (N
2PTI) is introduced to evaluate tool condition periodically on a test piece during the machining of non-metal workpieces. The cutting forces are measured when a slot is being cut on the test piece and the neural network estimates the tool life from the variation of the feed- and thrust-direction cutting forces. The performances of three encoding methods (force variation, segmental averaging and wavelet transformations) and two neural networks [backpropagation (BP) and probabilistic neural network (PNN)] are compared. The advantages of N
2PTI are simplicity, low cost, reliability and simple computational requirements. |
| doi_str_mv | 10.1016/S0890-6955(99)00074-7 |
| format | Article |
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2PTI) is introduced to evaluate tool condition periodically on a test piece during the machining of non-metal workpieces. The cutting forces are measured when a slot is being cut on the test piece and the neural network estimates the tool life from the variation of the feed- and thrust-direction cutting forces. The performances of three encoding methods (force variation, segmental averaging and wavelet transformations) and two neural networks [backpropagation (BP) and probabilistic neural network (PNN)] are compared. The advantages of N
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| source | Elsevier ScienceDirect Journals |
| subjects | Applied sciences Backpropagation Cutting tools End-mill Exact sciences and technology Industrial metrology. Testing Mechanical engineering. Machine design Micro-machining Micro-tool Micromachining Milling Milling (machining) Monitoring Neural network Neural networks Non-metal Precision engineering, watch making Probabilistic logics Wavelet transformation Wavelet transforms Wear Wear of materials |
| title | Tool wear estimation in micro-machining.: Part II: neural-network-based periodic inspector for non-metals |
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