Results 231 to 240 of about 5,074,594 (286)
Quantifying wing wear in bumble bees using fractal geometry. [PDF]
PLoS OneNirmalaraj I, Sookhan N, MacIvor JS.
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Gaussian process regression for tool wear prediction
Mechanical Systems and Signal Processing, 2018Abstract To realize and accelerate the pace of intelligent manufacturing, this paper presents a novel tool wear assessment technique based on the integrated radial basis function based kernel principal component analysis (KPCA_IRBF) and Gaussian process regression (GPR) for real-timely and accurately monitoring the in-process tool wear parameters ...
Yongjie Chen, Ning Li, Dongdong Kong
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Tool wear prediction in turning
Journal of Materials Processing Technology, 2004Abstract The aim of the present work is to develop a reliable method to predict flank wear precisely in a turning process by developing a mathematical model and comparing it with the experimental results. Some important factors like the index of diffusion, wear coefficient, rate of increase of normal load with respect to flank wear and the hardness ...
S.K. Choudhury, P. Srinivas
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Prediction of Tool Wear Progress in Machining of Carbon Steel using different Tool Wear Mechanismsl
International Journal of Material Forming, 2008In this paper the prediction of tool wear on carbide uncoated tools was taken into account. In particular, two different tool wear models based on the diffusion mechanism and on the abrasion mechanism were considered. The calibration of the utilized models was done using the results obtained by experimental analysis performed on an orthogonally ...
UMBRELLO, Domenico +4 more
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Physics-informed Gaussian process for tool wear prediction
ISA Transactions, 2023The tool wear monitoring (TWM) system plays an increasingly important role to ensure high quality finishing and system safety in advanced CNC machining process. The pure data-based TWM approaches generally needs to develop complex machine learning models and require massive sensory data to learn the models to reach high monitoring accuracy, while the ...
Kunpeng Zhu +3 more
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Prediction of wear of carbide cutting tools
International Journal of Production Research, 1977SUMMARY This paper presents an analysis of tool-wear data mainly from the view-point of flank wear. Mechanism of wear of cemented carbide tools is considered to be of two main types: (i) mechanical interaction, with adhesion-transfer type being the main cause and mechanical abrasion of secondary importance; (ii) thermochemical type, which is considered
C. F. HO, N. N. S. CHEN
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Feature selection for predicting tool wear of machine tools
The International Journal of Advanced Manufacturing Technology, 2020In this study, the vibration transmitted solely from a spindle to the worktable is proposed to be a crucial feature of wear prediction models for machine tools. To validate the effectiveness of the proposed feature, a feature ranking and screening methodology was also used for developing a tool wear prediction model.
Wen-Nan Cheng +3 more
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Analytical prediction of cutting tool wear
Wear, 1984Abstract An analytical method is presented which enables the crater and flank wear of tungsten carbide tools to be predicted for a wide variety of tool shapes and cutting conditions in practical turning operations based only on orthogonal cutting data from machining and two wear characteristic constants.
E. Usui, T. Shirakashi, T. Kitagawa
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Prediction of tool wear in micro USM
CIRP Annals, 2012Abstract Micro Ultrasonic Machining (USM) is used to generate micro features in hard and brittle materials. However, tool wear occurs during machining. In this paper, low cycle fatigue is identified as the dominant factor causing tool wear in micro USM. A theoretical model is proposed to estimate the tool wear.
Zuyuan Yu +4 more
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A Review: Sensors Used in Tool Wear Monitoring and Prediction
2022Tool wear prediction/monitoring of CNCs is crucial for improving manufacturing efficiency, guaranteeing product quality, and minimizing tool costs. As a computer-aided application, it has a significant role in the future and development of Industry 4.0.
Perin Ünal +2 more
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