Though there are a number of benefits associated with cellular manufacturing systems, its implementation (identification of part families and corresponding machine groups) for real life problems is still a challenging task. To handle the complexity of optimizing multiple objectives and larger size of the problem, most of the researchers in the past two decades or so have focused on developing genetic algorithm (GA) based techniques. Recently this trend has shifted from standard GA to hybrid GA (HGA) based approaches in the quest for greater effectiveness as far as convergence on to the optimum solution is concerned. In order to prove the point, that HGAs possess better convergence abilities than standard GAs, a methodology, initially based on standard GA and later on hybridized with a local search heuristic (LSH), has been developed during this research. Computational experience shows that HGA maintains its accuracy level with increase in problem size, whereas standard GA looses its effectiveness as the problem size grows.
High surface quality, optimum Material Removal Rate (MRR) and Tool-Chip Interface temperature (T c ) have significant importance in machining process. Similarly, dimensional accuracy in machining process also relies heavily on machining parameters. In machining operations, there are a number of parameters which have direct or indirect effect on the Surface Roughness (Ra) and MRR of the product. The surface roughness and MRR in turning process are affected by spindle speed (SS), feed rate (FR) and depth of cut (DOC). The optimization of turning parameters will be very helpful in improving quality of manufacturing and machining cost. In order to have an improved product, extensive research has been carried out to optimize machining process. The current research is focused at Response Surface Methodology (RSM) of turning process of Aluminum alloy (BS-1474 2014 A) by using variable sets of machining parameters i.e., SS, FR and DOC with carbide tipped tool. Multiple experiments were performed on CNC Lathe machine by using different combinations of process parameters. Response surface methodology was applied to reach theoretical values of the responses parameters (i.e, Ra, MRR, T c ) and an agreement was observed between actual machining results and methodology values. Design Expert-7 was used as a statistical tool to come to a conclusion on whether achieved results are optimum for turning process or otherwise. For a close correlation, comparison between hypothetical and investigational data is also the part of this research. Significant agreement between theoretically optimized machining parameters and experimental data has been observed.
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