Development of empirical models for surface roughness prediction in finish turning

被引：207

作者：

Wang X. ^{[2
]}

Feng C.X. ^{[1
]}

机构：

[1] Department of Industrial and Manufacturing Engineering and Technology, Bradley University, Peoria, IL

[2] College of Power and Environmental Engineering, Wuhan University of Technology, Wuhan, Hubei

来源：

The International Journal of Advanced Manufacturing Technology | 2002年 / 20卷 / 5期

关键词：

Data mining; Predictive process engineering; Quality design and control; Regression analysis; Surface roughness;

D O I：

10.1007/s001700200162

中图分类号：

学科分类号：

摘要：

Surface roughness plays an important role in product quality. This paper focuses on developing an empirical model for the prediction of surface roughness in finish turning. The model considers the following working parameters: workpiece hardness (material); feed; cutting tool point angle; depth of cut; spindle speed; and cutting time. One of the most important data mining techniques, nonlinear regression analysis with logarithmic data transformation, is applied in developing the empirical model. The values of surface roughness predicted by this model are then verified with extra experiments and compared with those from some of the representative models in the literature. Metal cutting experiments and statistical tests demonstrate that the model developed in this work produces smaller errors than those from some of the existing models and have a satisfactory goodness in both model construction and verification. Finally, further research directions are presented.

引用

页码：348 / 356

页数：8

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