Abstract
An innovative hybrid machining called as slotted-electrical discharge diamond face grinding (S-EDDFG) has been developed by comprising the electrical discharge face grinding and diamond face grinding. Due to complexity in S-EDDFG process, the selection of suitable combinations of input parameters is a challenging task for manufacturers. In recent, soft computing techniques are gaining more attention of researchers for modeling and optimization for the process parameters of complex manufacturing processes. In the present paper, two soft computing techniques known as artificial neural network (ANN) and non-dominated sorting genetic algorithm-II (NSGA-II) have been applied for modeling and optimization of process parameters (pulse current, pulse on-time, pulse off-time, wheel RPM and grit number) during machining of Aluminium–Silicon Carbide–Graphite (Al/SiC/Gr) composite. The results shown that two hidden layer ANN architecture was more appropriate to predict the material removal rate and average surface roughness during S-EDDFG of Al/SiC/Gr workpiece. The optimal results obtained by combined approach of ANN–NSGA-II are experimentally validated and found that optimal data are under acceptable limits.
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Acknowledgments
Authors would like to thank the Aeronautics R&D Board, Government of India, New Delhi for providing the financial support for this work of the project entitled “Electro-Discharge Diamond Grinding: Development and Optimization” [Project no.: DARO/08/2031574/M/I].
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Yadav, R.N., Yadava, V. Application of Soft Computing Techniques for Modeling and Optimization of Slotted-Electrical Discharge Diamond Face Grinding Process. Trans Indian Inst Met 68, 981–990 (2015). https://doi.org/10.1007/s12666-015-0536-6
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DOI: https://doi.org/10.1007/s12666-015-0536-6