Faculty Publications
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Item Squeeze casting parameter optimization using swarm intelligence and evolutionary algorithms(IGI Global, 2018) Gowdru Chandrashekarappa, G.C.; Krishna, K.; Parappagoudar, M.B.; Vundavilli, P.R.; Bharath Bhushan, S.N.This chapter is focused to locate the optimum squeeze casting conditions using evolutionary swarm intelligence and teaching learning-based algorithms. The evolutionary and swarm intelligent algorithms are used to determine the best set of process variables for the conflicting requirements in multiple objective functions. Four cases are considered with different sets of weight fractions to the objective function based on user requirements. Fitness values are determined for all different cases to evaluate the performance of evolutionary and swarm intelligent methods. Teaching learning-based optimization and multiple-objective particle swarm optimization based on crowing distance have yielded similar results. Experiments have been conducted to test the results obtained. The performance of swarm intelligence is found to be comparable with that of evolutionary genetic algorithm in locating the optimal set of process variables. However, TLBO outperformed GA, PSO, and MOPSO-CD with regard to computation time. © 2018, IGI Global.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 Squeeze casting process modeling by a conventional statistical regression analysis approach(Elsevier Inc. usjcs@elsevier.com, 2016) Gowdru Chandrashekarappa, G.C.; Krishna, P.; Parappagoudar, M.B.During the casting process, the alloy composition, melt treatment modification, processing method, and process variables change the microstructure, thereby affecting the mechanical properties. The hybrid squeeze casting method has been used to limit casting defects, refine the micro-structure, and enhance the mechanical properties. The process variables influence the mechanical and micro-structure properties during squeeze casting. In the present study, we established nonlinear input–output relationships and explored the physical behavior of this process based on the statistical design of experiments and using the response surface methodology. Experiments were conducted to measure the responses in terms of the density, hardness, and secondary dendrite arm spacing. Two nonlinear regression models, i.e., Box–Behnken design and central composite design, were used to conduct experiments, collect experimental data, identify significant process variables, analyze the collected data, and establish the complex input–output relationships. Surface plots were used to explore the effects of the squeeze pressure, pressure duration, pouring, and die temperature on the measured responses. Analysis of variance tests were conducted to evaluate the statistical suitability of the models developed. Furthermore, the accuracies of the predictions made by the models were investigated based on test cases. We found that both of the nonlinear models were statistically adequate and they provided complete insights into the complex nonlinear input–output relationships. Central composite design performed better for the secondary dendrite arm spacing and hardness responses, whereas its performance was the same as that of Box–Behnken design for the density response. The relationships between the responses (i.e., outputs) were established by generating large volumes of input–output data using the nonlinear regression models. We found that the density, hardness, and secondary dendrite arm spacing responses could be obtained by utilizing the nonlinear regression equations and the same set of process variables. Furthermore, the secondary dendrite arm spacing response could be expressed as third order nonlinear functions of density or hardness (structure to property relationship). The results showed that the secondary dendrite arm spacing had inverse relationships with density and hardness, whereas density and hardness had direct relationships. © 2016 Elsevier LtdItem 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.Item An intelligent system for squeeze casting process—soft computing based approach(Springer London, 2016) Gowdru Chandrashekarappa, G.C.; Krishna, P.; Parappagoudar, M.B.The present work deals with the forward and reverse modelling of squeeze casting process by utilizing the neural network-based approaches. The important quality characteristics in squeeze casting, namely surface roughness and tensile strength, are significantly influenced by its process variables like pressure duration, squeeze pressure, and pouring and die temperatures. The process variables are considered as input and output to neural network in forward and reverse mapping, respectively. Forward and reverse mappings are carried out utilizing back propagation neural network and genetic algorithm neural network. For both supervised learning networks, batch training is employed using huge training data (input-output data). The input-output data required for training is generated artificially at random by varying process variables between their respective levels. Further, the developed model prediction performances are compared for 15 random test cases. Results have shown that both models are capable to make better predictions, and the models can be used by any novice user without knowing much about the mechanics of materials and the process. However, the genetic algorithm tuned neural network (GA-NN) model prediction performance is found marginally better in forward mapping, whereas BPNN produced better results in reverse mapping. © 2016, Springer-Verlag London.Item Investigating fretting wear behavior of LPBF processed AlSi10Mg alloy under variable frequency and heat treatment conditions(Elsevier B.V., 2025) Nanjundaiah, R.S.; Rao, S.S.; Praveenkumar, K.; Selvan, C.P.; Ram Prabhu, T.; Sahay, S.; Manivasagam, G.; Shettigar, A.K.; Gowdru Chandrashekarappa, G.C.This study examines the fretting wear behavior of AlSi10Mg alloy processed via Laser Powder Bed Fusion (LPBF) under different oscillation frequencies (5 Hz, 10 Hz, and 15 Hz) and heat treatment conditions (as-built, stress-relieved, T5, and T6) under a consistent load of 100 N. The fabricated and heat-treated samples were analyzed using X-ray diffraction, hardness testing, and residual stress measurements to evaluate dislocation density, hardenability, and the nature of residual stress. Fretting wear behavior was further assessed through evaluations of the coefficient of friction (COF), worn surface morphology, and wear volume loss using scanning electron microscopy (SEM) and 3D profilometry to understand the mechanism. Results indicated that the as-built samples exhibited superior resistance against fretting wear across all tested frequencies, a phenomenon attributed to their refined microstructure and higher dislocation density (FWHM: 0.213). The results show that lower frequencies primarily result in adhesive wear, with the oxide layer providing some protection, but higher frequencies accelerate abrasive and fatigue wear due to enhanced crack propagation and thermal softening. © 2025 The Authors