Your browser doesn't support javascript.
loading
An Advanced Tool Wear Forecasting Technique with Uncertainty Quantification Using Bayesian Inference and Support Vector Regression.
Rong, Zhiming; Li, Yuxiong; Wu, Li; Zhang, Chong; Li, Jialin.
Affiliation
  • Rong Z; Applied Technology College, Dalian Ocean University, Dalian 116023, China.
  • Li Y; School of Mechanical Engineering, Dalian Jiaotong University, Dalian 116028, China.
  • Wu L; School of Mechanical Engineering, Dalian Jiaotong University, Dalian 116028, China.
  • Zhang C; School of Mechanical Engineering, Dalian Jiaotong University, Dalian 116028, China.
  • Li J; Chongqing Engineering Laboratory for Transportation Engineering Application Robot, Chongqing Jiaotong University, Chongqing 400074, China.
Sensors (Basel) ; 24(11)2024 May 24.
Article in En | MEDLINE | ID: mdl-38894185
ABSTRACT
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.
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Sensors (Basel) Year: 2024 Type: Article Affiliation country: China

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Sensors (Basel) Year: 2024 Type: Article Affiliation country: China