Title: Prediction of tool wear during micro-milling Inconel 718 thin-walled parts

Authors: Xiaohong Lu; Pengrong Hou; Yihan Luan; Kun Yang; Feixiang Ruan; Ning Zhao

Addresses: School of Mechanical Engineering, Dalian University of Technology, Dalian City, Liaoning Province, 116024, China ' School of Mechanical Engineering, Dalian University of Technology, Dalian City, Liaoning Province, 116024, China ' School of Mechanical Engineering, Dalian University of Technology, Dalian City, Liaoning Province, 116024, China ' School of Mechanical Engineering, Dalian University of Technology, Dalian City, Liaoning Province, 116024, China ' School of Mechanical Engineering, Dalian University of Technology, Dalian City, Liaoning Province, 116024, China ' School of Mechanical Engineering, Dalian University of Technology, Dalian City, Liaoning Province, 116024, China

Abstract: During micro-milling Inconel 718 thin-walled parts, the fluctuation law of cutting force is complex, and the influence of cutting force on tool wear is complicated. How to quantitatively evaluate the status of tool wear and realise the prediction of tool wear during micro-milling Inconel 718 thin-walled parts is a challenge. Experiments of micro-milling Inconel 718 thin-walled parts are carried out. Based on the experimental results, the influence of cutting parameters on cutting forces and tool wear is studied. The relationship between tool wear and milling time is obtained, and the process of tool wear can be divided into early wear stage, accelerated wear stage, and stable wear stage. A regression prediction model of tool wear varying with cutting time is built and validated by experiments. The research explores a feasible way for prediction of tool wear during micro-milling Inconel 718 thin-walled parts. [Submitted 6 January 2020; Accepted 13 June 2020]

Keywords: micro-milling; tool wear; thin-walled parts; Inconel 718.

DOI: 10.1504/IJMR.2022.121631

International Journal of Manufacturing Research, 2022 Vol.17 No.1, pp.82 - 94

Received: 06 Jan 2020
Accepted: 13 Jun 2020
Published online: 21 Mar 2022 *

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