Faculty Publications
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Item Prediction and optimization of dimensional shrinkage variations in injection molded parts using forward and reverse mapping of artificial neural networks(2012) Gowdru Chandrashekarappa, G.C.; Krishna, P.The most significant process parameters affecting dimensional shrinkage in transverse and longitudinal directions of molded parts in Plastic Injection Molding (PIM) process are injection velocity, mold temperature, melt temperature and packing pressure. In the present work, ANN model was developed for forward and reverse mapping prediction. In forward mapping PIM process parameters are expressed as the input parameters to predict dimensional shrinkage, whereas in reverse mapping, attempts were made to predict an appropriate set of process parameters required for arriving at the required dimensional shrinkage. The trained network with one thousand input-output data randomly generated from regression equations reported by earlier researchers resulted in minimum mean squared error. The performance of developed model was compared with experimental values for ten different test cases. The results show that ANN model with both forward and reverse mapping is capable of prediction with an error level of less than ten percent. © (2012) Trans Tech Publications.Item Modelling and multi-objective optimisation of squeeze casting process using regression analysis and genetic algorithm(Taylor and Francis Ltd. michael.wagreich@univie.ac.at, 2016) Gowdru Chandrashekarappa, G.C.; Krishna, P.; Parappagoudar, M.B.In the present work, an attempt has been made using statistical tools to develop a non-linear regression model and to identify the significant contribution of squeeze cast process parameters on surface roughness, hardness and tensile strength. Microstructure examination performed on the squeeze cast samples has revealed that a maximum of 100 MPa pressure is good enough to eliminate all possible casting defects. Accuracy of the developed models has been tested with the help of ten test cases. It is important to note that the developed models predict responses with a reasonably good accuracy and the developed mathematical input–output relationship helps the foundry-man to make better predictions. The present work comprises four objectives, which are conflicting in nature. Hence, mathematical formulation is used to convert four objective functions into a single objective function. The popular evolutionary algorithm, that is genetic algorithm has been utilised to determine the optimal process parameters. © 2015 Engineers Australia.Item Multi-Objective Optimization of Squeeze Casting Process using Genetic Algorithm and Particle Swarm Optimization(De Gruyter Open Ltd peter.golla@degruyter.com, 2016) Gowdru Chandrashekarappa, G.C.; Krishna, P.; Vundavilli, P.R.; Parappagoudar, M.B.The near net shaped manufacturing ability of squeeze casting process requiresto set the process variable combinations at their optimal levels to obtain both aesthetic appearance and internal soundness of the cast parts. The aesthetic and internal soundness of cast parts deal with surface roughness and tensile strength those can readily put the part in service without the requirement of costly secondary manufacturing processes (like polishing, shot blasting, plating, hear treatment etc.). It is difficult to determine the levels of the process variable (that is, pressure duration, squeeze pressure, pouring temperature and die temperature) combinations for extreme values of the responses (that is, surface roughness, yield strength and ultimate tensile strength) due to conflicting requirements. In the present manuscript, three population based search and optimization methods, namely genetic algorithm (GA), particle swarm optimization (PSO) and multi-objective particle swarm optimization based on crowding distance (MOPSO-CD) methods have been used to optimize multiple outputs simultaneously. Further, validation test has been conducted for the optimal casting conditions suggested by GA, PSO and MOPSO-CD. The results showed that PSO outperformed GA with regard to computation time. © 2016 G.C.M. Patel et al., published by De Gruyter Open.