NDLI: Predictive modeling of surface roughness in lenses precision turning using regression and support vector machines

Content Provider	Springer Nature Link
Author	Wang, Xingsheng Kang, Min Fu, Xiuqing Li, Chunlin
Copyright Year	2013
Abstract	Slow tool servo (STS) turning is superior in machining precision and in complicated surface. However, STS turning is a complex process in which many variables can affect the desired results. This paper focuses on surface roughness prediction in lenses STS turning. An exponential model, based on the five main cutting parameters including tool nose radius, feed rate, depth of cut, C-axis speed, and discretization angle, for surface roughness prediction of lenses is developed by means of orthogonal experiment regression analysis. Meanwhile, a prediction model of surface roughness based on least squares support vector machines (LS-SVM) with radial basis function is constructed. Orthogonal experiment swatches are studied, and chaotic particle swarm optimization and leave-one-out cross-validation are applied to determine the model parameters. The comparison of LS-SVM model and exponential model is also carried out. Predictive LS-SVM model is found to be capable of better predictions for surface roughness and has absolute fraction of variance R 2 of 0.99887, the mean absolute percent error e M of 8.96 %, and the root mean square error e R of 10.68 %. The experimental results and prediction of LS-SVM model show that effects of tool nose radius and feed rate are more significant than that of depth of cut on surface roughness of lenses turning.
Starting Page	1273
Ending Page	1281
Page Count	9
File Format	PDF
ISSN	02683768
Journal	The International Journal of Advanced Manufacturing Technology
Volume Number	87
Issue Number	5-8
e-ISSN	14333015
Language	English
Publisher	Springer London
Publisher Date	2013-08-16
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Lenses Slow tool servo Orthogonal regression analysis LS-SVM Prediction model Industrial and Production Engineering Production Mechanical Engineering Computer-Aided Engineering (CAD, CAE) and Design
Content Type	Text
Resource Type	Article
Subject	Industrial and Manufacturing Engineering Control and Systems Engineering Mechanical Engineering Computer Science Applications Software

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