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.
|Number of pages||5|
|State||Published - Aug 2021|
Bibliographical noteFunding 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”.
© 2021 Society of Manufacturing Engineers (SME)
- Process modeling
- Reinforcement learning
- Smart manufacturing
- Surface integrity
ASJC Scopus subject areas
- Mechanics of Materials
- Industrial and Manufacturing Engineering