Correcting geometric deviations of CNC Machine-Tools: An approach with Artificial Neural Networks |
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| Affiliation: | 1. Universidade Federal de Minas Gerais, Programa de Pós-Graduação em Engenharia de Produção, Av. Antonio Carlos, 6627 Pampulha, Belo Horizonte, MG, Brazil;2. Universidade Federal de Minas Gerais, Departamento de Engenharia Mecânica, Av. Antônio Carlos, 6627 Pampulha, Belo Horizonte, MG, Brazil;3. Universidade de São Paulo, Escola de Engenharia de São Carlos, Departamento de Engenharia Mecânica, Av. Trabalhador São Carlense, 400 Centro, São Carlos, SP, Brazil;4. Centro Federal de Educação Tecnológica de Minas Gerais, Laboratório de Sistemas Inteligentes, Av. Amazonas, 7675 Nova Gameleira, Belo Horizonte, MG, Brazil;1. Federal Institute of Education, Science and Technology of Sao Paulo, Piracicaba, Brazil;2. University of Sao Paulo, Sao Carlos, Brazil;3. The Brazilian Power System Operator, Rio de Janeiro, Brazil;1. Electrical and Electronic Engineering, İnönü University, 44060 Malatya, Turkey;2. Electrical and Electronic Engineering, Batman University, 72060 Batman, Turkey;1. Department of Accounting, School of Business and Accounting, Federal University of Rio de Janeiro, Av. Pasteur 250, Rio de Janeiro 22290-240, Brazil;2. Department of Economic Theory, Institute of Economics, University of Campinas, Av. Pitágoras 353, Campinas 13083-857, Brazil;3. Department of Computer Engineering and Automation, School of Electrical and Computer Engineering, University of Campinas, Av. Albert Einstein 400, Campinas 13083-852, Brazil;1. Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China;2. Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing 100124, China;3. Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116620, China;1. School of Science, Xi’an Polytechnic University, Xi’an 710048, China;2. Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY 40292, USA;3. Spoleczna Akademia Nauk, 90-011 Lodz, Poland;1. Taiyuan University of Science and Technology, Taiyuan 030024, Shanxi, China;2. Centre for Health Technologies (CHT), University of Technology, Sydney 2007, Australia;3. Machine Intelligence Research Labs (MIR Labs), Scientific Network for Innovation and Research Excellence, P.O. Box 2259, Auburn, WA 98071-2259, USA |
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| Abstract: | This paper presents an experimental methodology of Design for Manufacturing (DFM) used for survey and analysis of geometric deviations of CNC Machine-Tools, through their final product. These deviations generate direct costs that can be avoided through the use of Intelligent Manufacturing Systems (IMS), by the application of Artificial Neural Networks (ANNs) to predict the fabrication parameters. Finally, after the experiments, it was possible to evaluate the experimental methodology used, the equations, the variables of data adjustment and thus enable the validation of the methodology used as a tool for DFM with high potential return on product quality, development time and reliability of the process with wide application in various CNC Machines. |
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| Keywords: | Artificial Neural Networks CNC Machine-Tools Error compensation Design for Manufacturing Precision technology |
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