Matches in SemOpenAlex for { <https://semopenalex.org/work/W4384404785> ?p ?o ?g. }
- W4384404785 endingPage "110599" @default.
- W4384404785 startingPage "110599" @default.
- W4384404785 abstract "Tool wear monitoring (TWM) is essential to improve product quality and maintain machining safety. Within recent years, numerous techniques have been developed for TWM, including data-driven methods and physics-based models. However, traditional data-driven methods are highly dependent on measured data. The physics-based models are difficult to model complex machining processes. To solve these problems, a hybrid-driven probabilistic state space model is proposed to improve the accuracy and robustness of TWM. In this work, a sensitive feature extraction scheme is first constructed to eliminate interferences and redundancies for improving monitoring efficiency. Subsequently, the Gaussian process is innovatively developed to integrate the data mining and physical model from a probabilistic perspective. On this basis, the particle filter is then utilized to estimate the model parameters and estimate tool wear conditions. Finally, an adaptive wear state recognition method is further established for the predictive maintenance of cutting tools. The practical data obtained from milling experiments are used to validate the effectiveness of the proposed methodology. The results show that the proposed hybrid-driven method effectively improves tool wear prediction accuracy to over 97.7%, while the constructed probabilistic model successfully evaluates the prediction results with over 95% confidence. Therefore, the developed methodology may provide a promising way for tool health management to improve economic efficiency and maintain machining safety." @default.
- W4384404785 created "2023-07-16" @default.
- W4384404785 creator A5020545623 @default.
- W4384404785 creator A5045877983 @default.
- W4384404785 creator A5056081646 @default.
- W4384404785 creator A5078956553 @default.
- W4384404785 date "2023-10-01" @default.
- W4384404785 modified "2023-10-10" @default.
- W4384404785 title "A hybrid-driven probabilistic state space model for tool wear monitoring" @default.
- W4384404785 cites W1977578272 @default.
- W4384404785 cites W1996118088 @default.
- W4384404785 cites W2001129496 @default.
- W4384404785 cites W2003947476 @default.
- W4384404785 cites W2461218363 @default.
- W4384404785 cites W2553288752 @default.
- W4384404785 cites W2692693673 @default.
- W4384404785 cites W2773549135 @default.
- W4384404785 cites W2807490715 @default.
- W4384404785 cites W2810813609 @default.
- W4384404785 cites W2885446150 @default.
- W4384404785 cites W2904700581 @default.
- W4384404785 cites W2915034955 @default.
- W4384404785 cites W2938021361 @default.
- W4384404785 cites W2938369548 @default.
- W4384404785 cites W2949666355 @default.
- W4384404785 cites W2971297588 @default.
- W4384404785 cites W2988017727 @default.
- W4384404785 cites W2997308049 @default.
- W4384404785 cites W3003767554 @default.
- W4384404785 cites W3005302330 @default.
- W4384404785 cites W3037906335 @default.
- W4384404785 cites W3088834512 @default.
- W4384404785 cites W3090279272 @default.
- W4384404785 cites W3093010392 @default.
- W4384404785 cites W3124241600 @default.
- W4384404785 cites W3136408793 @default.
- W4384404785 cites W3136651874 @default.
- W4384404785 cites W3183414894 @default.
- W4384404785 cites W3190646162 @default.
- W4384404785 cites W3190840618 @default.
- W4384404785 cites W3194417176 @default.
- W4384404785 cites W3194659978 @default.
- W4384404785 cites W4200318892 @default.
- W4384404785 cites W4220925901 @default.
- W4384404785 cites W4221121733 @default.
- W4384404785 cites W4224228003 @default.
- W4384404785 cites W4224237346 @default.
- W4384404785 cites W4224257146 @default.
- W4384404785 cites W4225263750 @default.
- W4384404785 cites W4280581965 @default.
- W4384404785 cites W4281701749 @default.
- W4384404785 cites W4292994712 @default.
- W4384404785 cites W4304113988 @default.
- W4384404785 doi "https://doi.org/10.1016/j.ymssp.2023.110599" @default.
- W4384404785 hasPublicationYear "2023" @default.
- W4384404785 type Work @default.
- W4384404785 citedByCount "1" @default.
- W4384404785 countsByYear W43844047852023 @default.
- W4384404785 crossrefType "journal-article" @default.
- W4384404785 hasAuthorship W4384404785A5020545623 @default.
- W4384404785 hasAuthorship W4384404785A5045877983 @default.
- W4384404785 hasAuthorship W4384404785A5056081646 @default.
- W4384404785 hasAuthorship W4384404785A5078956553 @default.
- W4384404785 hasConcept C104317684 @default.
- W4384404785 hasConcept C119857082 @default.
- W4384404785 hasConcept C124101348 @default.
- W4384404785 hasConcept C127413603 @default.
- W4384404785 hasConcept C133731056 @default.
- W4384404785 hasConcept C154945302 @default.
- W4384404785 hasConcept C157286648 @default.
- W4384404785 hasConcept C185592680 @default.
- W4384404785 hasConcept C200601418 @default.
- W4384404785 hasConcept C2776450708 @default.
- W4384404785 hasConcept C41008148 @default.
- W4384404785 hasConcept C49937458 @default.
- W4384404785 hasConcept C523214423 @default.
- W4384404785 hasConcept C52421305 @default.
- W4384404785 hasConcept C55493867 @default.
- W4384404785 hasConcept C63479239 @default.
- W4384404785 hasConcept C78519656 @default.
- W4384404785 hasConceptScore W4384404785C104317684 @default.
- W4384404785 hasConceptScore W4384404785C119857082 @default.
- W4384404785 hasConceptScore W4384404785C124101348 @default.
- W4384404785 hasConceptScore W4384404785C127413603 @default.
- W4384404785 hasConceptScore W4384404785C133731056 @default.
- W4384404785 hasConceptScore W4384404785C154945302 @default.
- W4384404785 hasConceptScore W4384404785C157286648 @default.
- W4384404785 hasConceptScore W4384404785C185592680 @default.
- W4384404785 hasConceptScore W4384404785C200601418 @default.
- W4384404785 hasConceptScore W4384404785C2776450708 @default.
- W4384404785 hasConceptScore W4384404785C41008148 @default.
- W4384404785 hasConceptScore W4384404785C49937458 @default.
- W4384404785 hasConceptScore W4384404785C523214423 @default.
- W4384404785 hasConceptScore W4384404785C52421305 @default.
- W4384404785 hasConceptScore W4384404785C55493867 @default.
- W4384404785 hasConceptScore W4384404785C63479239 @default.
- W4384404785 hasConceptScore W4384404785C78519656 @default.
- W4384404785 hasFunder F4320321001 @default.