Journal Published Online: 21 December 2021
Volume 5, Issue 1

Monte Carlo Method–Based Tool Life Prediction during the End Milling of Ti-6Al-4V Alloy for Smart Manufacturing

CODEN: SSMSCY

Abstract

Tool wear prediction during machining is of significant interest for the development of intelligent functionalities in manufacturing industry. A data-driven Bayesian Monte Carlo–based probabilistic approach is used for predicting the wear of TiAlN-coated carbide inserts during the end milling of Ti-6Al-4V alloy. A series of slot milling passes at varying combinations of speed, feed, and depth of cut were conducted, and wear was measured after each pass. Each insert was used for successive passes at a particular cutting condition until the flank wear crosses the failure threshold of 0.3 mm of average flank wear. The wear estimation from the model is good at tracking wear growth for the unknown data sets, which can provide a timely tool change command before the tool failure. This model thus leads to the formulation of an adaptive control strategy for timely replacement of cutting tools for optimal machining.

Author Information

Tiwari, K.
Department of Mechanical Engineering, Indian Institute of Technology Madras, Chennai, TN, India
Arunachalam, N.
Department of Mechanical Engineering, Indian Institute of Technology Madras, Chennai, TN, India
Pages: 26
Price: Free
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Details
Stock #: SSMS20210013
ISSN: 2520-6478
DOI: 10.1520/SSMS20210013