Abstract
This paper describes the application of logistic classification for tool life modeling and prediction in an industrial setting using shop floor data. Tool life is treated as a classification problem since tool wear can only be measured at the time of tool replacement in a production environment. Laboratory tool wear experiments are used to simulate shop floor wear data by two states: not worn (class 0); and worn (class 1). To incorporate non-linearity in logistic classification, a log-transformation of input features is performed. The logistic classification approach, results, and interpretability of the logistic model are presented.
| Original language | English |
|---|---|
| Title of host publication | 9th CIRP Conference on High Performance Cutting, HPC 2020 |
| Editors | Erdem Ozturk, David Curtis, Hassan Ghadbeigi |
| Publisher | Elsevier B.V. |
| Pages | 106-109 |
| Number of pages | 4 |
| ISBN (Electronic) | 9781713835431 |
| DOIs | |
| State | Published - 2020 |
| Event | 9th CIRP Conference on High Performance Cutting, HPC 2020 - Virtual, Online Duration: May 24 2021 → May 26 2021 |
Publication series
| Name | Procedia CIRP |
|---|---|
| Volume | 101 |
| ISSN (Print) | 2212-8271 |
Conference
| Conference | 9th CIRP Conference on High Performance Cutting, HPC 2020 |
|---|---|
| City | Virtual, Online |
| Period | 05/24/21 → 05/26/21 |
Funding
This research was supported by the DOE Office of Energy Efficiency and Renewable Energy (EERE), Energy and Transportation Science Division, and used resources at the Manufacturing Demonstration Facility, a DOE-EERE User Facility at Oak Ridge National Laboratory.
Keywords
- Logistic classification
- Machine learning
- Tool wear