Enhancing machining efficiency: Integrating machine-learning-based cutting tool condition monitoring in industrial application
Abstract
In industrial practice, cutting tools are often changed early to maintain process reliability, which results in increased tool costs and unused potential. Machine learning (ML) based tool condition monitoring can be used to detect tool wear and reduce the risk of tool failure; facilitating longer tool operation times. However, these models have not found widespread use in industrial practice. Therefore, this paper presents an approach on how ML models can be integrated into the business models of tool manufacturers. It is shown how anomaly detection models and the prediction of remaining useful life serve as the foundation for this purpose.
Fields of Study & Subjects
Advanced machining processes and optimization
Machine Fault Diagnosis Techniques
Digital Transformation in Industry
Publication details
- Journal / source
- Procedia CIRP
- Publisher
- Elsevier BV
- Volume
- 138
- Pages
- 381–386
- Publication date
- 1 January 2026
- Language
- EN