Chatter prediction in boring process using machine learning technique

被引：0

作者：

Saravanamurugan S. ^{[1
]}

Thiyagu S. ^{[2
]}

Sakthivel N.R. ^{[1
]}

Nair B.B. ^{[3
]}

机构：

[1] Department of Mechanical Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Amrita University, Coimbatore

[2] Department of Mechanical Engineering, K.P.R. Institute of Engineering and Technology, Arasur, Coimbatore

[3] Department Electronics and Communication Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Amrita University, Coimbatore

来源：

Saravanamurugan, S. (s_saravana@cb.amrita.edu) | 2017年 / Inderscience Publishers, 29, route de Pre-Bois, Case Postale 856, CH-1215 Geneva 15, CH-1215, Switzerland卷 / 12期

关键词：

Boring; Chatter; Discrete wavelet transformation; DWT; Support vector machine; Surface roughness; SVM;

D O I：

10.1504/IJMR.2017.088399

中图分类号：

学科分类号：

摘要：

Chatter is the main reason behind the failure of any part in the machining centre and lowers the productivity. Chatter occurs as a dynamic interaction between the tool and the work piece resulting in poor surface finish, high-pitch noise and premature tool failure. In this paper, the chatter prediction is done by active method by considering the parameters like spindle speed, depth of cut, feed rate and including the dynamics of both the tool and the workpiece. The vibration signals are acquired using an accelerometer in a closed environment. From the acquired signals discrete wavelet transformation (DWT), features are extracted and classified into three different patterns (stable, transition and chatter) using support vector machine (SVM). The classified results are validated using surface roughness values (Ra). Copyright © 2017 Inderscience Enterprises Ltd.

引用

页码：405 / 422

页数：17

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