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A tool wear condition monitoring approach for end milling based on numerical simulation

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
As an important research area of modern manufacturing, tool condition monitoring (TCM) has attracted much attention, especially artificial intelligence (AI)- based TCM method. However, the training samples obtained in practical experiments have the problem of sample missing and sample insufficiency. A numerical simulation- based TCM method is proposed to solve the above problem. First, a numerical model based on Johnson-Cook model is established, and the model parameters are optimized through orthogonal experiment technology, in which the KL divergence and cosine similarity are used as the evaluation indexes. Second, samples under various tool wear categories are obtained by the optimized numerical model above to provide missing samples not present in the practical experiments and expand sample size. The effectiveness of the proposed method is verified by its application in end milling TCM experiments. The results indicate the classification accuracies of four classifiers (SVM, RF, DT, and GRNN) can be improved significantly by the proposed TCM method.
Rocznik
Strony
371--380
Opis fizyczny
Bibliogr. 50 poz., rys., tab.
Twórcy
autor
  • College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou, China
autor
  • College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou, China
autor
  • College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou, China
autor
  • School of Mechatronics and Transportation, Jiaxing Nanyang Polytechnic Institute, Jiaxing, China
Bibliografia
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  • 4. Ducobu F, Rivière-Lorphèvre E, Filippi E. On the importance of the choice of the parameters of the Johnson-Cook constitutive model and their influence on the results of a Ti6Al4V orthogonal cutting model. International Journal of Mechanical Sciences 2017; 122: 143-155, https://doi.org/10.1016/j.ijmecsci.2017.01.004.
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  • 23. Kumar A, Kumar R. Least Square Fitting for Adaptive Wavelet Generation and Automatic Prediction of Defect Size in the Bearing Using Levenberg-Marquardt Backpropagation. Journal of Nondestructive Evaluation 2017; 36, 7, https://doi.org/10.1007/s10921-016-0385-1.
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  • 25. Lei Z, Zhou YQ, Sun BT, Sun WF. An intrinsic timescale decomposition-based kernel extreme learning machine method to detect tool wear conditions in the milling process. The International Journal of Advanced Manufacturing Technology 2020; 106(3-4): 1203-1212, https://doi.org/10.1007/s00170-019- 04689-9.
  • 26. Li GF, Wang YB, He JL, Hao QB, Yang HJ, Wei JF. Tool wear state recognition based on gradient boosting decision tree and hybrid classification RBM. The International Journal of Advanced Manufacturing Technology 2020; 110: 511-522, https://doi.org/10.1007/s00170-020-05890-x.
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  • 32. Pawełczyk M, Fulara S, Sepe M, Luca AD, Badora M. Industrial gas turbine operating parameters monitoring and data-driven prediction. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2020; 22(3):391-399. https://doi.org/10.17531/ein.2020.3.2
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  • 46. Zhou YQ, Sun BT, Sun WF, Lei Z. Tool Wear Condition Monitoring Based on a Two-layer Angle Kernel Extreme Learning Machine using Sound Sensor for Milling Process. Journal of Intelligent Manufacturing 2020; 9, https://doi.org/ 10.1007/s10845-020-01663-1.
  • 47. Zhou YQ, Sun WF. Tool Wear Condition Monitoring in Milling Process Based on Current Sensors. IEEE Access 2020; 8: 95491-95502, https://doi.org/10.1109/ACCESS.2020.2995586.
  • 48. Zhou YQ, Xue W. A multisensor fusion method for tool condition monitoring in milling. Sensors 2018; 18(11): 3866, https://doi.org/10.3390/s18113866.
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Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-33f2a9c5-f557-4e90-b26c-d390e575cc16
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