A Review of Physics-Based, Data-Driven, and Hybrid Models for Tool Wear Monitoring Article Swipe
Haoyuan Zhang
,
Shanglei Jiang
,
Daqing Gao
,
Yuwen Sun
,
Weirong Bai
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/machines12120833
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/machines12120833
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.
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Metadata
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4404586629Canonical identifier for this work in OpenAlex
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https://doi.org/10.3390/machines12120833Digital Object Identifier
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A Review of Physics-Based, Data-Driven, and Hybrid Models for Tool Wear MonitoringWork title
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enPrimary language
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2024Year of publication
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2024-11-21Full publication date if available
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Haoyuan Zhang, Shanglei Jiang, Daqing Gao, Yuwen Sun, Weirong BaiList of authors in order
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https://doi.org/10.3390/machines12120833Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.3390/machines12120833Direct OA link when available
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24Total citation count in OpenAlex
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2025: 24Per-year citation counts (last 5 years)
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387Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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