Abstract
While the physics of chip formation have been widely studied, there remains a need for greater qualitative and quantitative understanding of the way thermal and mechanical loads, and particularly their dynamic variability across length and time scales affect both the magnitude and variability of machining-induced residual stress (MIRS). This paper leverages an advanced in-situ characterization technique along with a physics-based process model to accurately and quickly predict key MIRS variables for aerospace alloys Inconel 718 and Ti-6Al4V. Our analysis clearly shows opportunities for digitally enabled predictability of engineered surface integrity to evaluate the fatigue performance of aerospace alloys more effectively.
| Original language | English |
|---|---|
| Pages (from-to) | 109-112 |
| Number of pages | 4 |
| Journal | CIRP Annals |
| Volume | 74 |
| Issue number | 1 |
| DOIs | |
| State | Published - Jan 2025 |
Bibliographical note
Publisher Copyright:© 2025 The Author(s)
Funding
J. Schoop gratefully acknowledges support by the U.S. National Science Foundation , grant number 2143806 (CAREER award).
| Funders | Funder number |
|---|---|
| National Science Foundation Arctic Social Science Program | 2143806 |
Keywords
- Modelling
- Predictive model
- Processing
- Surface integrity
- Sustainable machining
- Uncertainty
ASJC Scopus subject areas
- Mechanical Engineering
- Industrial and Manufacturing Engineering
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