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International Journal of General Systems Volume 35, 2006 - Issue 5
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Original Articles

Dynamic neural network approach for tool cutting force modelling of end milling operations

Franc Cus Faculty of Mechanical Engineering, University of Maribor, Smetanova 17, 2000, Maribor, SloveniaCorrespondence[email protected]
,
Uros Zuperl Faculty of Mechanical Engineering, University of Maribor, Smetanova 17, 2000, Maribor, SloveniaView further author information
&
Matjaz Milfelner Faculty of Mechanical Engineering, University of Maribor, Smetanova 17, 2000, Maribor, SloveniaView further author information
Pages 603-618 | Received 27 Aug 2004, Accepted 05 May 2006, Published online: 27 Nov 2006

Citations (11)

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H. El-Mounayri & H. Deng. (2010) A generic and innovative approach for integrated simulation and optimisation of end milling using solid modelling and neural network. International Journal of Computer Integrated Manufacturing 23:1, pages 40-60.
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Articles from other publishers (10)

Ming-Hsu Tsai, Jeng-Nan Lee, Hung-Da Tsai, Ming-Jhang Shie, Tai-Lin Hsu & Hung-Shyong Chen. (2023) Applying a Neural Network to Predict Surface Roughness and Machining Accuracy in the Milling of SUS304. Electronics 12:4, pages 981.
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Jingyang Feng, Zhaocheng Wei, Minjie Wang, Minglong Guo & Xueqin Wang. (2021) Force prediction model of high efficiency U pass milling. The International Journal of Advanced Manufacturing Technology 117:3-4, pages 1101-1115.
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Wei Ma, Rongqi Wang, Xiaoqin Zhou & Xuefan Xie. (2020) The finite element analysis–based simulation and artificial neural network–based prediction for milling processes of aluminum alloy 7050. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 235:1-2, pages 265-277.
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Islam A. Alexandrov, Aslan A. Tatarkanov & Aleksandr S. Sannikov. (2020) Development of Algorithms of Automated Products Quality Control System in Technological Processes of Machining. Development of Algorithms of Automated Products Quality Control System in Technological Processes of Machining.
Chaojie Liu, Wenfeng Ding, Zheng Li & Changyong Yang. (2016) Prediction of high-speed grinding temperature of titanium matrix composites using BP neural network based on PSO algorithm. The International Journal of Advanced Manufacturing Technology 89:5-8, pages 2277-2285.
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Omid Geramifard, Jian-Xin Xu, Jun-Hong Zhou & Xiang Li. (2012) A Physically Segmented Hidden Markov Model Approach for Continuous Tool Condition Monitoring: Diagnostics and Prognostics. IEEE Transactions on Industrial Informatics 8:4, pages 964-973.
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Indrajit Mukherjee & Srikanta Routroy. (2012) Comparing the performance of neural networks developed by using Levenberg–Marquardt and Quasi-Newton with the gradient descent algorithm for modelling a multiple response grinding process. Expert Systems with Applications 39:3, pages 2397-2407.
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Omid Geramifard, Jian-Xin Xu, Jun-Hong Zhou & Xiang Li. (2011) Continuous health condition monitoring: A single Hidden Semi-Markov Model approach. Continuous health condition monitoring: A single Hidden Semi-Markov Model approach.
Francisco Jesus Martín–Mateos, Luis Carlos González Valencia & Rafael Serrano Bello. 2010. Current Topics in Artificial Intelligence. Current Topics in Artificial Intelligence 281 290 .
Indrajit Mukherjee & Pradip Kumar Ray. (2009) A case-based and practical approach for multivariate modeling of grinding process. The International Journal of Advanced Manufacturing Technology 45:3-4, pages 245-260.
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