New M.E. Thesis Submitted from PROD Student



Many real world problems in optimization are too complex to be given tractable mathematical formulations. Most of manufacturing industries still highly depend on experience-based trial and error method to plan the machining process. It has long been recognized that conditions during cutting, such as feed rate, cutting speed and depth of cut, should be selected to optimize the economics of machining operations, as assessed by productivity, total manufacturing cost per component or some other suitable criterion. Manufacturing industries have long depended on the skill and experience of shop-floor machine-tool operators for optimal selection of cutting conditions and cutting tools. Considerable efforts are still in progress on the use of handbook based conservative cutting conditions and cutting tool selection at the process planning level.
The need for selecting and implementing optimal machining conditions and the most suitable cutting tool has been felt over the last few decades. In workshop practice, cutting parameters are selected from machining databases or specialized handbooks, but the range given in these sources are actually starting values, and are not the optimal values. The optimization of these parameters is an important aspect to get required production rate and tool life. To optimize these parameters, a number of different tools have been used by various researchers and have found the values according to application. In this work, Genetic Algorithm has been used to optimize these variables. The objective function being tool life, production rate and surface finish. The theoretical values have been compared with experimental values.
By this comparison, the optimal tool life is found to be 88.93min, production rate is 6 pieces/min, surface roughness is 2.9389μm under the condition of speed=260.5591m/min, feed =0.1961mm/rev, depth of cut=0.5572mm (optimized by GA tool).
The experimental result of surface finish is 2.8 to2.97µm which is nearly same as the value calculated by GA tool (2.9389µm).

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