Dự đoán mòn dụng cụ dựa trên máy vector liên quan nhiều lõi trọng số và phân tích thành phần chính xác suất lõi

Dimla DE (2000) Sensor signals for tool-wear monitoring in metal cutting operations—a review of methods. Int J Mach Tools Manuf 40:1073–1098. https://doi.org/10.1016/S0890-6955(99)00122-4 Rehorn AG, Jiang J, Orban PE (2005) State-of-the-art methods and results in tool condition monitoring: a review. The International Journal of Advanced Manufacturing Technology 26:693–710. https://doi.org/10.1007/s00170-004-2038-2 Teti R, Jemielniak K, Donnell OG, Dornfeld D (2010) Advanced monitoring of machining operations. CIRP Ann 59:717–739. https://doi.org/10.1016/j.cirp.2010.05.010 Siddhpura A, Paurobally R (2013) A review of flank wear prediction methods for tool condition monitoring in a turning process. Int J Adv Manuf Technol 65:371–393 Liu T, Zhu K (2021) A switching hidden semi-Markov model for degradation process and its application to time-varying tool wear monitoring. IEEE Trans Industr Inf 17:2621–2631. https://doi.org/10.1109/TII.2020.3004445 Han D, Yu J, Tang D (2021) An HDP-HMM based approach for tool wear estimation and tool life prediction. Qual Eng 33:208–220. https://doi.org/10.1080/08982112.2020.1813760 Huang C, Yin X, Huang H, Li Y (2020) An enhanced deep learning-based fusion prognostic method for RUL prediction. IEEE Trans Reliab 69:1097–1109. https://doi.org/10.1109/TR.2019.2948705 Sun H, Zhang J, Mo R, Zhang X (2020) In-process tool condition forecasting based on a deep learning method. Robotics and Computer-Integrated Manufacturing 64:101924. https://doi.org/10.1016/j.rcim.2019.101924 Xu L, Huang C, Li C, Wang J, Liu H, Wang X (2021) Estimation of tool wear and optimization of cutting parameters based on novel ANFIS-PSO method toward intelligent machining. J Intell Manuf 32:77–90. https://doi.org/10.1007/s10845-020-01559-0 Shi C, Panoutsos G, Luo B, Liu H, Li B, Lin X (2019) Using multiple-feature-spaces-based deep learning for tool condition monitoring in ultraprecision manufacturing. IEEE Trans Industr Electron 66:3794–3803. https://doi.org/10.1109/TIE.2018.2856193 Wang G, Zhang Y, Liu C, Xie Q, Xu Y (2019) A new tool wear monitoring method based on multi-scale PCA. J Intell Manuf 30:113–122. https://doi.org/10.1007/s10845-016-1235-9 Huang Z, Zhu J, Lei J, Li X, Tian F (2020) Tool wear predicting based on multi-domain feature fusion by deep convolutional neural network in milling operations. J Intell Manuf 31:953–966. https://doi.org/10.1007/s10845-019-01488-7 Shen Y, Yang F, Habibullah MS, Ahmed J, Das AK, Zhou Y, Ho CL (2021) Predicting tool wear size across multi-cutting conditions using advanced machine learning techniques. J Intell Manuf 32:1753–1766. https://doi.org/10.1007/s10845-020-01625-7 Tipping ME (2001) Sparse Bayesian learning and the relevance vector machine. J Mach Learn Res 1:211–244. https://doi.org/10.1162/15324430152748236 Wang G, Yang Y, Xie Q, Zhang Y (2014) Force based tool wear monitoring system for milling process based on relevance vector machine. Adv Eng Softw 71:46–51. https://doi.org/10.1016/j.advengsoft.2014.02.002 Kong D, Chen Y, Li N, Duan C, Lu L, Chen D (2019) Relevance vector machine for tool wear prediction. Mech Syst Signal Process 127:573–594. https://doi.org/10.1016/j.ymssp.2019.03.023 Chen F, Cheng M, Tang B, Xiao W, Chen B, Shi X (2020) A novel optimized multi-kernel relevance vector machine with selected sensitive features and its application in early fault diagnosis for rolling bearings. Measurement 156:107583. https://doi.org/10.1016/j.measurement.2020.107583 Xue J, Shen B (2020) A novel swarm intelligence optimization approach: sparrow search algorithm. Systems Science & Control Engineering 8:22–34. https://doi.org/10.1080/21642583.2019.1708830 Wang J, Xie J, Zhao R, Zhang L, Duan L (2017) Multisensory fusion based virtual tool wear sensing for ubiquitous manufacturing. Robotics and Computer-Integrated Manufacturing 45:47–58. https://doi.org/10.1016/j.rcim.2016.05.010 Ge Z, Song Z (2010) Kernel generalization of PPCA for nonlinear probabilistic monitoring. Ind Eng Chem Res 49:11832–11836. https://doi.org/10.1021/ie100852s Kim D, Lee I (2003) Process monitoring based on probabilistic PCA. Chemom Intell Lab Syst 67:109–123. https://doi.org/10.1016/S0169-7439(03)00063-7 Neil DL (2005) Probabilistic non-linear principal component analysis with Gaussian process latent variable models. J Mach Learn Res 6 Tanaka A, Imai H, Kudo M, Miyakoshi M (2007) Integrated kernels and their properties. Pattern Recogn 40:2930–2938. https://doi.org/10.1016/j.patcog.2007.02.014 Zhang Z, Wang G, Yeung DY, Kwok JT (2004) Probabilistic kernel principal component analysis Rashedi E, Nezamabadi-pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179:2232–2248. https://doi.org/10.1016/j.ins.2009.03.004 Yang XS (2010) Engineering optimization (an introduction with metaheuristic applications) || Engineering Optimization Jamil M, Yang X (2013) A literature survey of benchmark functions for global optimization problems[Z]. Cornell University Library, arXiv.org, Ithaca Yang XS (2014) [Studies in Computational Intelligence] Cuckoo Search and Firefly Algorithm Volume 516 || Intelligent Firefly Algorithm for Global Optimization. In:315–330 Sm A, Smm B, Al A (2014) Grey wolf optimizer. Advances in Engineering Software:46–61 Eberhart, SY (2002) Particle swarm optimization: developments, applications and resources: Congress on Evolutionary Computation Heidari AA, Mirjalili S, Faris H, Aljarah I, Mafarja M, Chen H (2019) Harris hawks optimization: algorithm and applications. Futur Gener Comput Syst 97:849–872. https://doi.org/10.1016/j.future.2019.02.028