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
Subjects:	T Technology T Technology > TS Manufactures T Technology > TA Engineering (General). Civil engineering (General)
Journal or Publication Title:	JOURNAL OF MATERIALS PROCESSING TECHNOLOGY
Publisher:	ELSEVIER SCIENCE SA LAUSANNE
ISSN:	0924-0136
Date:	15 February 1995
Volume:	49
Number:	3-4
Number of Pages:	10
Page Range:	pp. 255-264
Publication Status:	Published
URI:	http://wrap.warwick.ac.uk/id/eprint/19796

Data sourced from Thomson Reuters' Web of Knowledge

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