Preprint Article Version 1 This version is not peer-reviewed

A Neuro-Fuzzy Approach Based Tool Condition Monitoring in AISI H13 Milling

Version 1 : Received: 22 July 2018 / Approved: 24 July 2018 / Online: 24 July 2018 (06:29:35 CEST)

How to cite: Alam, M.S.; Aramesh, M.; Veldhuis, S. A Neuro-Fuzzy Approach Based Tool Condition Monitoring in AISI H13 Milling. Preprints 2018, 2018070447 (doi: 10.20944/preprints201807.0447.v1). Alam, M.S.; Aramesh, M.; Veldhuis, S. A Neuro-Fuzzy Approach Based Tool Condition Monitoring in AISI H13 Milling. Preprints 2018, 2018070447 (doi: 10.20944/preprints201807.0447.v1).

Abstract

In the manufacturing industry, cutting tool failure is a probable fault which causes damage to the cutting tools, workpiece quality and unscheduled downtime. It is very important to develop a reliable and inexpensive intelligent tool wear monitoring system for use in cutting processes. A successful monitoring system can effectively maintain machine tools, cutting tool and workpiece. In the present study, the tool condition monitoring system has been developed for Die steel (H13) milling process. Effective design of experiment and robust data acquisition system ensured the machining forces impact in the milling operation. Also, ANFIS based model has been developed based on cutting force-tool wear relationship in this research which has been implemented in the tool wear monitoring system. Prediction model shows that the developed system is accurate enough to perform an online tool wear monitoring system in the milling process.

Subject Areas

TCM; cutting force; flank wear; ANFIS; fuzzy; LabVIEW

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