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TOOL-WEAR PREDICTION USING ARTIFICIAL NEURAL NETWORKS
Tools
UNSPECIFIED (1995) TOOL-WEAR PREDICTION USING ARTIFICIAL NEURAL NETWORKS. JOURNAL OF MATERIALS PROCESSING TECHNOLOGY, 49 (3-4). pp. 255-264. ISSN 0924-0136.
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Abstract
A mixed-oxide ceramic cutting tool (type K090) has been used to machine grey cast iron (grade G-14) in a turning process. Different values of feed rate and cutting speed have been used for machining at a constant depth of cut. Tool life and failure mode have been recorded for each experiment and the associated data have been used to train an artificial neural network (multi-layer perceptron) using the back-propagation algorithm. The trained network has been used to predict tool lives and failure modes for experiments not used in training. The best results are 58.3% correct tool-life prediction (within 20% of the actual tool life) and 87.5% correct failure-mode prediction, but it was felt that these could be improved significantly if more real data was generated for the training of the neural network.
Item Type: | Journal Article | ||||
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Subjects: | T Technology T Technology > TS Manufactures T Technology > TA Engineering (General). Civil engineering (General) |
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Journal or Publication Title: | JOURNAL OF MATERIALS PROCESSING TECHNOLOGY | ||||
Publisher: | ELSEVIER SCIENCE SA LAUSANNE | ||||
ISSN: | 0924-0136 | ||||
Official Date: | 15 February 1995 | ||||
Dates: |
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Volume: | 49 | ||||
Number: | 3-4 | ||||
Number of Pages: | 10 | ||||
Page Range: | pp. 255-264 | ||||
Publication Status: | Published |
Data sourced from Thomson Reuters' Web of Knowledge
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