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Process parameters optimization for micro end-milling operation for CAPP applications

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Abstract

In Computer Aided Process Planning (CAPP), process parameters selection for the given manufacturing feature is the final activity and it is the key area for research and development. In this work, an attempt has been made to optimize parameters for micro end-milling operation as a part of CAPP system development for micromachining processes using Artificial Intelligence (AI) approach. Genetic Algorithm (GA) has been found to be the robust and efficient tool to solve nonlinear optimization problems involved in process planning. Microfeatures of size 0.7 and 1 mm are considered and polymethyl methacrylate is chosen as the work material due to its potential application in microparts fabrication. Initially, experimental investigation has been carried out to analyze the impact of process conditions such as spindle speed and feed rate on surface roughness and machining time. Further multiobjective optimization for minimization of responses is carried out using GA. Finally, confirmation experiments are carried out to validate the accuracy of GA results. The optimized process parameters are stored in the database and it ensures foolproof parameters for micro end-milling operation for CAPP applications apart from manuals and catalogues. The proposed approach can be repeated for various other end mill features and for different work and tool material combination to ensure a complete parameters selection module for CAPP system applications.

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Authors and Affiliations

  1. Department of Production Engineering, National Institute of Technology, Tiruchchirappalli, 620015, India

    S. P. Leo Kumar, J. Jerald, S. Kumanan & Nargundkar Aniket

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  1. S. P. Leo Kumar
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  2. J. Jerald
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  3. S. Kumanan
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  4. Nargundkar Aniket
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Correspondence to S. P. Leo Kumar.

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Leo Kumar, S.P., Jerald, J., Kumanan, S. et al. Process parameters optimization for micro end-milling operation for CAPP applications. Neural Comput & Applic 25, 1941–1950 (2014). https://doi.org/10.1007/s00521-014-1683-0

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  • DOI: https://doi.org/10.1007/s00521-014-1683-0

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