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A multi-sensor based online tool condition monitoring system for milling process
Faculty of Engineering, Environment and Computing, Coventry University, Coventry, United Kingdom.
Faculty of Engineering, Environment and Computing, Coventry University, Coventry, United Kingdom.
Faculty of Engineering, Environment and Computing, Coventry University, Coventry, United Kingdom.
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. (Produktion och automatiseringsteknik, Production and Automation Engineering)ORCID iD: 0000-0003-1781-2753
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2018 (English)In: Procedia CIRP, ISSN 2212-8271, E-ISSN 2212-8271, Vol. 72, p. 1136-1141Article in journal (Refereed) Published
Abstract [en]

Tool condition monitoring has been considered as one of the key enabling technologies for manufacturing optimization. Due to the high cost and limited system openness, the relevant developed systems have not been widely adopted by industries, especially Small and Medium-sized Enterprises. In this research, a cost-effective, wireless communication enabled, multi-sensor based tool condition monitoring system has been developed. Various sensor data, such as vibration, cutting force and power data, as well as actual machining parameters, have been collected to support efficient tool condition monitoring and life estimation. The effectiveness of the developed system has been validated via machining cases. The system can be extended to wide manufacturing applications.

Place, publisher, year, edition, pages
Elsevier, 2018. Vol. 72, p. 1136-1141
Keywords [en]
tool condition monitoring, multipe sensors, vibration
National Category
Reliability and Maintenance
Research subject
INF201 Virtual Production Development; Production and Automation Engineering
Identifiers
URN: urn:nbn:se:his:diva-16508DOI: 10.1016/j.procir.2018.03.092Scopus ID: 2-s2.0-85049556291OAI: oai:DiVA.org:his-16508DiVA, id: diva2:1271752
Conference
51st CIRP Conference on Manufacturing Systems, CIRP CMS 2018, Stockholm Waterfront Congress Centre, 16 May 2018 through 18 May 2018; Code 137494
Available from: 2018-12-18 Created: 2018-12-18 Last updated: 2019-10-04Bibliographically approved

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Wang, Wei

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