NDLI: Tool condition monitoring (TCM) using neural networks

Content Provider	Springer Nature Link
Author	Liu, Tien I Jolley, Bob
Copyright Year	2015
Abstract	Cutting tool conditions significantly influence the quality and precision of the machined parts. With the ability to monitor the cutting tool condition, machining quality can be maintained and catastrophic failure can be eliminated. In this manner, production automation can be achieved. Therefore, tool condition monitoring (TCM) is extremely important to achieve high quality and automation of boring processes. Neural networks have been widely used in condition monitoring. Counterpropagation neural networks (CPNs), which are based on competitive learning, have been utilized in TCM in this research for high quality and automated boring. The inputs of the CPNs were the indexes acquired from three-axis cutting force data. The output was either the tool state or the value of tool wear. Seventy CPN network structures have been utilized for both real-time recognition and real-time measurements. The performance of the CPNs for TCM depends on the network structures. The results of this research are exceedingly successful. Real-time recognition of tool states showed excellent results, using a 2 × 30 × 1 CPN, of being able to predict tool states real time with 100 % accuracy. Real-time measurements can achieve a minimum error of 8.46 % using a 3×69×1 CPN, which is sufficient for continuous assessment of the tool degradation. Control actions can be taken to stop the boring process in order to avoid catastrophic failure and to enhance quality and automation of the boring process.
Starting Page	1999
Ending Page	2007
Page Count	9
File Format	PDF
ISSN	02683768
Journal	The International Journal of Advanced Manufacturing Technology
Volume Number	78
Issue Number	9-12
e-ISSN	14333015
Language	English
Publisher	Springer London
Publisher Date	2015-01-21
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Counterpropagation neural networks Competitive learning Industrial and Production Engineering Production/Logistics/Supply Chain 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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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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