The chief objective of manufacturing process improvement efforts is to significantly minimize process resources such as time, cost, waste, and consumed energy while improving product quality and process productivity. This paper presents a novel physics-informed optimization approach based on artificial intelligence (AI) to generate digital process twins (DPTs). The utility of the DPT approach is demonstrated for the case of finish machining of aerospace components made from gamma titanium aluminide alloy (γ-TiAl). This particular component has been plagued with persistent quality defects, including surface and sub-surface cracks, which adversely affect resource efficiency. Previous process improvement efforts have been restricted to anecdotal post-mortem investigation and empirical modeling, which fail to address the fundamental issue of how and when cracks occur during cutting. In this work, the integration of in-situ process characterization with modular physics-based models is presented, and machine learning algorithms are used to create a DPT capable of reducing environmental and energy impacts while significantly increasing yield and profitability. Based on the preliminary results presented here, an improvement in the overall embodied energy efficiency of over 84%, 93% in process queuing time, 2% in scrap cost, and 93% in queuing cost has been realized for γ-TiAl machining using our novel approach.
|Title of host publication||Additive Manufacturing; Advanced Materials Manufacturing; Biomanufacturing; Life Cycle Engineering; Manufacturing Equipment and Automation|
|State||Published - 2021|
|Event||ASME 2021 16th International Manufacturing Science and Engineering Conference, MSEC 2021 - Virtual, Online|
Duration: Jun 21 2021 → Jun 25 2021
|Name||Proceedings of the ASME 2021 16th International Manufacturing Science and Engineering Conference, MSEC 2021|
|Conference||ASME 2021 16th International Manufacturing Science and Engineering Conference, MSEC 2021|
|Period||6/21/21 → 6/25/21|
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”.
Copyright © 2021 by ASME
- Energy efficiency
- Sustainable manufacturing
- Titanium aluminide
ASJC Scopus subject areas
- Industrial and Manufacturing Engineering