Abstract
The term “machinability”, introduced over hundred years ago, is vague and cannot fully describe the performance of machining systems. Machinability databases established over many decades are outdated: missing recent advances, e.g., cutting tool grades, geometry, coatings, and cutting fluids effects. This keynote paper summarizes findings of a CIRP-sponsored three-year collaborative study in five interrelated topics. The paper presents a critical review of the state-of-the-art on these topics, the results of two major round robin tests, three industry-based case studies, and a novel predictive system of machining performance, utilizing advanced deep learning methods. Outlook and future directions are also presented.
| Original language | English |
|---|---|
| Pages (from-to) | 817-842 |
| Number of pages | 26 |
| Journal | CIRP Annals |
| Volume | 74 |
| Issue number | 2 |
| DOIs | |
| State | Published - Jan 2025 |
Bibliographical note
Publisher Copyright:© 2025 CIRP
Keywords
- Cutting tool
- Machinability
- Modeling
ASJC Scopus subject areas
- Mechanical Engineering
- Industrial and Manufacturing Engineering