Development of an intelligent tool condition monitoring system to identify manufacturing tradeoffs and optimal machining conditions

Wo Jae Lee, Gamini P. Mendis, John W. Sutherland

Research output: Contribution to journalConference articlepeer-review

33 Scopus citations

Abstract

Smart manufacturing has leveraged the evolution of a sensor-based and data-driven platform to improve manufacturing outcomes. As a result of increased use of sensors and networked machines in manufacturing operations, artificial intelligence techniques play a key role to derive meaningful value from big data infrastructure. These techniques can inform decision making and can enable the implementation of more sustainable practices in the manufacturing industry. In machining processes, a considerable amount of waste (scrap) is generated as a result of failure to monitor a tool condition. Therefore, an intelligent tool condition monitoring system is developed in this paper to identify sustainability-related manufacturing tradeoffs and a set of optimal machining conditions by monitoring the status of the machine tool. An evolutionary algorithm-based multi-objective optimization is used to find the optimal operating conditions, and the solutions are visualized using a Pareto optimal front.

Original languageEnglish (US)
Pages (from-to)256-263
Number of pages8
JournalProcedia Manufacturing
Volume33
DOIs
StatePublished - 2019
Event16th Global Conference on Sustainable Manufacturing, GCSM 2018 - Lexington, United States
Duration: Oct 2 2018Oct 4 2018

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Industrial and Manufacturing Engineering

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