An improved neural network model for residual stress prediction in turning

Results presented in this paper are related to the prediction of the longitudinal residual stress generated by the turning process. The main problems associated with the prediction capability of empirical models developed using the design of experiment (DOE) method are given. Their limited aptitude...

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Bibliographic Details
Published in:	2004 IEEE International Conference on Industrial Technology, 2004. IEEE ICIT '04 Vol. 2; pp. 1012 - 1016 Vol. 2
Main Authors:	Amamou, R., Fredj, N.B., Rhouma, A.B., Fnaiech, F.
Format:	Conference Proceeding
Language:	English
Published:	piscataway NJ IEEE 2004
Subjects:	Applied sciences Artificial neural networks Computer science; control theory; systems Exact sciences and technology Fundamental areas of phenomenology (including applications) Intelligent networks Machining Neural networks Physics Predictive models Residual stresses Solid mechanics Structural and continuum mechanics Surface cracks Surface resistance Turning US Department of Energy Experimental design High precision Neural network Empirical model Train Residual stress Confidence interval
Online Access:	Get full text
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