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Online Machining Optimization with Continuous Learning
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Author(s): M. Chandrasekaran (North Eastern Regional Institute of Science and Technology, India), M. Muralidhar (North Eastern Regional Institute of Science and Technology, India), C. Murali Krishna (Maulana Azad National Institute of Technology, India)and U.S. Dixit (Indian Institute of Technology Guwahati, India)
Copyright: 2012
Pages: 26
Source title:
Computational Methods for Optimizing Manufacturing Technology: Models and Techniques
Source Author(s)/Editor(s): J. Paulo Davim (University of Aveiro, Portugal)
DOI: 10.4018/978-1-4666-0128-4.ch004
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Abstract
In offline optimization of machining process with traditional or soft computing techniques, the functional relationship between the tool life and the cutting parameters are often assumed. Application of these techniques in shop floor involves a number of constraints and has many limitations in its implementation. The complex machining process gets influenced by multiple process parameters, particularly in a finish turning operation, which often determines the final quality of the parts. In this work, an online optimization methodology with continuous learning is proposed and applied to finish turning process. Surface roughness is predicted using a virtual machine modeled with neural network and empirical equation. Minimization of machining cost is considered as an optimization objective. Optimization is carried out using simplex search or a fuzzy optimization method to determine optimum process parameters. The simulated data obtained from online machining can be stored and used for online learning of machining process. An artificial intelligence (AI) based online learning strategy proposed in this work determines the optimum cutting condition accurately without consuming significant time and resources at the shop floor. The new approach is more suitable and economical for shop floor applications.
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