Results 251 to 260 of about 5,320,548 (290)

Relevance vector machine for tool wear prediction

Mechanical Systems and Signal Processing, 2019
Abstract In order to realize real-time and accurate monitoring of the tool wear in machining process, this paper presents a tool wear predictive model based on the integrated radial basis function based kernel principal component analysis (KPCA_IRBF) and relevance vector machine (RVM). The traditional methods such as partial least squares regression (
Yongjie Chen, Lixin Lu, Ning Li
exaly   +2 more sources

Tool Wear Prediction in Milling Using Neural Networks

2002
An intelligent supervisory system, which is supported on a modelbased approach, is presented herein. A model, created using Artificial Neural Networks (ANN), able to predict the process output is introduced in order to deal with the characteristics of such an ill-defined process.
Rodolfo E. Haber, Angel Alique, José R. Alique   +2 more
openaire   +2 more sources

A tool wear predictive model based on SVM

2010 Chinese Control and Decision Conference, 2010
Tool wear monitoring is an integral part of modern CNC machine control. This paper presents a new tool wear predictive model by combination of workpiece surface texture analysis and support vector machine with genetic algorithm (SVMG). Firstly, the column projection method and the Gabor filter method are proposed to extract texture features of machined
null Yiqiu Qian, null Jia Tian, null Libing Liu, null Yu Zhang, null Yingshu Chen   +4 more
openaire   +1 more source

Tool Wear Prediction Based on BiLSTMA Networks

2022 the 14th International Conference on Computer Modeling and Simulation, 2022
Chunyan Qian, Qingqing Huang, Haofei Xie, Dong Yan, Yushuang Wu   +4 more
openaire   +2 more sources

Enhanced particle filter for tool wear prediction

Journal of Manufacturing Systems, 2015
Timely assessment and prediction of tool wear is essential to ensuring part quality, minimizing material waste, and contributing to sustainable manufacturing. This paper presents a probabilistic method based on particle filtering to account for uncertainties in the tool wear process.
Jinjiang Wang, Peng Wang, Robert X. Gao
openaire   +1 more source

Research on Predicting Tool Wear Based on the BP Network

2011
Established 3D turning model by the solid works, based on DEFORM-3D software for turning process simulation with intelligent materials, obtained the data in the process of turning the intelligent materials, curve fitting the tools wear by BP neural network, and obtained the prediction curve of tool wear based on the BP network.
Ping Jiang, ZhiPing Deng
openaire   +1 more source

Pattern recognition of tool wear and failure prediction

2008 7th World Congress on Intelligent Control and Automation, 2008
A method of pattern recognition of tool wear based on Discrete Hidden Markov Models (DHMM) is proposed to monitor tool wear and to predict tool failure. FFT features are first extracted from the vibration signal and cutting force in cutting process, and then FFT vectors are presorted and converted into integers by SOM.
null Jing Kang, null Ni Kang, null Chang-jian Feng, null Hong-ying Hu   +3 more
openaire   +1 more source

Monitoring and prediction of tool wear in microturning of copper

International Journal of Precision Technology, 2010
This paper presents experimental results of microturning of commercially available copper rods with cermet (TiC + Al2O3) inserts as cutting tool. Machining was done in DT-110 integrated multiprocess micromachine tool. Full factorial experiments were conducted with various combinations of cutting speed, feed and depth of cut.
P. Ranjith Kumar, A.S.S. Balan, S. Gowri
openaire   +1 more source

Neural-Network Prediction of Tool Wear

Russian Engineering Research, 2022
I. F. Dyakov, Yu. V. Moiseev
openaire   +1 more source

Prediction of Milling Tool Wear Based on Digital Simulation

2023 28th International Conference on Automation and Computing (ICAC), 2023
Chaozhong Guo, Wentao Zheng, Jihong Yan
openaire   +1 more source