An Advanced Tool Wear Forecasting Technique with Uncertainty Quantification Using Bayesian Inference and Support Vector Regression

被引:1
|
作者
Rong, Zhiming [1 ]
Li, Yuxiong [2 ]
Wu, Li [2 ]
Zhang, Chong [2 ]
Li, Jialin [3 ]
机构
[1] Dalian Ocean Univ, Appl Technol Coll, Dalian 116023, Peoples R China
[2] Dalian Jiaotong Univ, Sch Mech Engn, Dalian 116028, Peoples R China
[3] Chongqing Jiaotong Univ, Chongqing Engn Lab Transportat Engn Applicat Robot, Chongqing 400074, Peoples R China
关键词
cutting tool wear prediction; brownian motion; bayesian inference; uncertainty quantification; support vector regression; NEURAL-NETWORK; MACHINE;
D O I
10.3390/s24113394
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Tool wear prediction is of great significance in industrial production. Current tool wear prediction methods mainly rely on the indirect estimation of machine learning, which focuses more on estimating the current tool wear state and lacks effective quantification of random uncertainty factors. To overcome these shortcomings, this paper proposes a novel method for predicting cutting tool wear. In the offline phase, the multiple degradation features were modeled using the Brownian motion stochastic process and a SVR model was trained for mapping the features and the tool wear values. In the online phase, the Bayesian inference was used to update the random parameters of the feature degradation model, and the future trend of the features was estimated using simulation samples. The estimation results were input into the SVR model to achieve in-advance prediction of the cutting tool wear in the form of distribution densities. An experimental tool wear dataset was used to verify the effectiveness of the proposed method. The results demonstrate that the method shows superiority in prediction accuracy and stability.
引用
收藏
页数:18
相关论文
共 50 条
  • [41] Copper Price Prediction Using Support Vector Regression Technique
    Astudillo, Gabriel
    Carrasco, Raul
    Fernandez-Campusano, Christian
    Chacon, Max
    APPLIED SCIENCES-BASEL, 2020, 10 (19):
  • [42] Heterogeneous uncertainty quantification using Bayesian inference for simulation-based design optimization
    Li, Mingyang
    Wang, Zequn
    STRUCTURAL SAFETY, 2020, 85
  • [43] An Error-Pursuing Adaptive Uncertainty Analysis Method Based on Bayesian Support Vector Regression
    Zhou, Sheng-Tong
    Jiang, Jian
    Zhou, Jian-Min
    Chen, Pei-Han
    Xiao, Qian
    MACHINES, 2023, 11 (02)
  • [44] Uncertainty quantification in regression neural networks using evidential likelihood-based inference
    Denoeux, Thierry
    INTERNATIONAL JOURNAL OF APPROXIMATE REASONING, 2025, 182
  • [45] Decision making and uncertainty quantification for individualized treatments using Bayesian Additive Regression Trees
    Logan, Brent R.
    Sparapani, Rodney
    McCulloch, Robert E.
    Laud, Purushottam W.
    STATISTICAL METHODS IN MEDICAL RESEARCH, 2019, 28 (04) : 1079 - 1093
  • [46] Forecasting One Day Ahead Stream Flow Using Support Vector Regression
    Londhe, Shreenivas
    Gavraskar, Seema S.
    INTERNATIONAL CONFERENCE ON WATER RESOURCES, COASTAL AND OCEAN ENGINEERING (ICWRCOE'15), 2015, 4 : 900 - 907
  • [47] Modelling and forecasting cotton production using tuned-support vector regression
    Saha, Amit
    Singh, K. N.
    Ray, Mrinmoy
    Rathod, Santosha
    Choudhury, Sharani
    CURRENT SCIENCE, 2021, 121 (08): : 1090 - 1098
  • [48] Energy Load Forecasting Using Empirical Mode Decomposition and Support Vector Regression
    Ghelardoni, Luca
    Ghio, Alessandro
    Anguita, Davide
    IEEE TRANSACTIONS ON SMART GRID, 2013, 4 (01) : 549 - 556
  • [49] Short-Term Wind Energy Forecasting Using Support Vector Regression
    Kramer, Oliver
    Gieseke, Fabian
    SOFT COMPUTING MODELS IN INDUSTRIAL AND ENVIRONMENTAL APPLICATIONS, 6TH INTERNATIONAL CONFERENCE SOCO 2011, 2011, 87 : 271 - 280
  • [50] Forecasting Energy Consumption of a Public Building Using Transformer and Support Vector Regression
    Huang, Junhui
    Kaewunruen, Sakdirat
    ENERGIES, 2023, 16 (02)