Unsupervised and Semisupervised Machine Learning Frameworks for Multiclass Tool Wear Recognition

Assafo, Maryam; Langendoerfer, Peter

doi:10.1109/ojies.2024.3455264

IEEE Open J. Ind. Electron. Soc.

2024

DOI: 10.1109/ojies.2024.3455264

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Unsupervised and Semisupervised Machine Learning Frameworks for Multiclass Tool Wear Recognition

Maryam Assafo,

Peter Langendoerfer

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2024

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Cited by 1 publication

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A Review of Physics-Based, Data-Driven, and Hybrid Models for Tool Wear Monitoring

Zhang,

Jiang,

Gao

et al. 2024

Machines

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Tool wear is an inevitable phenomenon in the machining process. By monitoring the wear state of a tool, the machining system can give early warning and make advance decisions, which effectively ensures improved machining quality and production efficiency. In the past two decades, scholars have conducted extensive research on tool wear monitoring (TWM) and obtained a series of remarkable research achievements. However, physics-based models have difficulty predicting tool wear accurately. Meanwhile, the diversity of actual machining environments further limits the application of physical models. Data-driven models can establish the deep mapping relationship between signals and tool wear, but they only fit trained data well. They still have difficulty adapting to complex machining conditions. In this paper, physics-based and data-driven TWM models are first reviewed in detail, including the factors that affect tool wear, typical data-based models, and methods for extracting and selecting features. Then, tracking research hotspots, emerging physics–data fusion models are systematically summarized.

show abstract

A Review of Physics-Based, Data-Driven, and Hybrid Models for Tool Wear Monitoring

Zhang,

Jiang,

Gao

et al. 2024

Machines

View full text Add to dashboard Cite

show abstract

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Unsupervised and Semisupervised Machine Learning Frameworks for Multiclass Tool Wear Recognition

Cited by 1 publication

References 32 publications

A Review of Physics-Based, Data-Driven, and Hybrid Models for Tool Wear Monitoring

A Review of Physics-Based, Data-Driven, and Hybrid Models for Tool Wear Monitoring

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