Results 241 to 250 of about 5,074,594 (286)

In-Process Prediction of Milling Tool Wear

1984
In process sensinq of cutting tool condition, which may include wear as well as breakage, has become a major interest to machine tool users. The various methods proposed so far can be divided into direct and indirect methods. A good example of the former is the use of a TV camera directed at the cutting zone, so that careful examination of the tool ...
A. Shumsheruddin, J. C. Lawrence
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Research on Tool Wear Prediction Based on LSTM and ARIMA

Proceedings of the 2018 International Conference on Big Data Engineering and Technology, 2018
Precise tool wear prediction is the key to improving the productivity of the entire workpiece. Reliable tool wear prediction technology can reduce the machine downtime caused by the tool change process, and can also make the entire machining process more efficient.
Zhenkun Zhang, Juan Lu, Gang Zhou, Xiaoping Liao   +3 more
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Research of Tool Wear Monitoring and Tool Life Prediction Models

2020 13th International Conference on Intelligent Computation Technology and Automation (ICICTA), 2020
Tool wear is inevitable in manufacturing and affects the surface quality and geometric tolerance significantly. Detection of the tool wear and residual life can significantly improve the usage rate of machining and reduce the economic losses due to the tool breakage so as to optimize input-output ratio.
Qilin Xiang, Aibo Xu, Ling Yuan, Xiang Hu, Liwei Luo, Fei Huang   +5 more
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Prediction Of Abrasive And Diffusive Tool Wear Mechanisms In Machining

AIP Conference Proceedings, 2011
Tool wear prediction is regarded as very important task in order to maximize tool performance, minimize cutting costs and improve the quality of workpiece in cutting. In this research work, an experimental campaign was carried out at the varying of cutting conditions with the aim to measure both crater and flank tool wear, during machining of an AISI ...
S. Rizzuti, D. Umbrello, Francisco Chinesta, Yvan Chastel, Mohamed El Mansori   +4 more
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Tool Wear Prediction in Broaching Based on Tool Geometry

Volume 9: Manufacturing Materials and Metallurgy; Microturbines, Turbochargers, and Small Turbomachines; Oil & Gas Applications; Steam Turbine
Abstract The aircraft engine is a safety-critical part of an aircraft. Constructive modifications resulting in ecological and economic improvements of the engine lead to necessary changes in manufacturing processes. The turbine discs in the low-pressure section are made of temperature resistant nickel-based superalloys.
Christoph Zachert, Markus Meurer, Thomas Bergs   +2 more
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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
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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
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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
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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
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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
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