Assessment of machining features for tool condition monitoring in face milling using an artificial neural network-Reference-Cited by-同舟云学术

Assessment of machining features for tool condition monitoring in face milling using an artificial neural network

Published:2000-07-01 Issue:7 Volume:214 Page:535-546
ISSN:0954-4054
Container-title:Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture
language:en
Short-container-title:Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture

Author:

Dutta R K¹,Kiran G²,Paul S³,Chattopadhyay A B³

Affiliation:

1. Assam Engineering College Department of Mechanical Engineering Guwahati, India

2. Tata Consultancy Services, India

3. Indian Institute of Technology Department of Mechanical Engineering Kharagpur, India

Abstract

Because of a wide scatter in the tool lives of face milling inserts and limitations of conventional methods for predicting the same, artificial neural systems have become advantageous for their ability to learn input-output mappings. Process parameters coupled with machining responses and experimental observations provide a basis for monitoring the tool wear in face milling. Chip characteristics such as shape and colour together with features of force and vibration are potential candidates for wear prediction in the field of tool condition monitoring.

Publisher

SAGE Publications

Subject

Industrial and Manufacturing Engineering,Mechanical Engineering

Link

http://journals.sagepub.com/doi/pdf/10.1243/0954405001518233

Reference73 articles.

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