AI-enabled dynamic finish machining optimization for sustained surface integrity

Julius Schoop, Hasan A. Poonawala, David Adeniji, Benton Clark

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

While machining processes are typically leveraged to establish geometric features, many functional characteristics of advanced materials are directly determined by their machining-induced quality, i.e. surface integrity. Current modeling approaches struggle to predict surface integrity, and typically neglect the effects of progressive tool-wear, resulting in inefficient ‘static’ process parameters. We present a novel integrated approach based on model-informed artificial intelligence (AI), which quickly and efficiently optimizes ‘dynamic’ process parameters. By maximizing the useful life of a cutting tool over which required quality parameters can be maintained, our paradigm will enable significantly more efficient processing of next-generation materials and components.

Original languageEnglish
Pages (from-to)42-46
Number of pages5
JournalManufacturing Letters
Volume29
DOIs
StatePublished - Aug 2021

Bibliographical note

Funding Information:
This material is based upon work supported by the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy (EERE) under the Advanced Manufacturing Office’s (AMO) DE-FOA-0001980, Award Number DE-EE0009121/0000, project title “AI-Enabled Discovery and Physics Based Optimization of Energy Efficient Processing Strategies for Advanced Turbine Alloys”.

Publisher Copyright:
© 2021 Society of Manufacturing Engineers (SME)

Keywords

  • Machining
  • Process modeling
  • Reinforcement learning
  • Smart manufacturing
  • Surface integrity

ASJC Scopus subject areas

  • Mechanics of Materials
  • Industrial and Manufacturing Engineering

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