Abstract
This paper describes a physics-guided logistic classification method for tool life modeling and process parameter optimization in machining. Tool life is modeled using a classification method since the exact tool life cannot be measured in a typical production environment where tool wear can only be directly measured when the tool is replaced. In this study, laboratory tool wear experiments are used to simulate tool wear data normally collected during part production. Two states are defined: tool not worn (class 0) and tool worn (class 1). The non-linear reduction in tool life with cutting speed is modeled by applying a logarithmic transformation to the inputs for the logistic classification model. A method for interpretability of the logistic model coefficients is provided by comparison with the empirical Taylor tool life model. The method is validated using tool wear experiments for milling. Results show that the physics-guided logistic classification method can predict tool life using limited datasets. A method for pre-process optimization of machining parameters using a probabilistic machining cost model is presented. The proposed method offers a robust and practical approach to tool life modeling and process parameter optimization in a production environment.
| Original language | English |
|---|---|
| Pages (from-to) | 522-534 |
| Number of pages | 13 |
| Journal | Journal of Manufacturing Systems |
| Volume | 59 |
| DOIs | |
| State | Published - Apr 2021 |
Funding
This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05−00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan ( http://energy.gov/downloads/doe-public-access-plan ). This research was supported by the DOE Office of Energy Efficiency and Renewable Energy (EERE) , Manufacturing Science Division, and used resources at the Manufacturing Demonstration Facility , a DOE-EERE User Facility at Oak Ridge National Laboratory.
| Funders | Funder number |
|---|---|
| DOE-EERE | |
| Manufacturing Science Division | |
| U.S. Department of Energy | |
| Office of Energy Efficiency and Renewable Energy | |
| Oak Ridge National Laboratory |
Keywords
- Classification
- Machine learning
- Machining
- Optimization
- Tool life
- Uncertainty