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Mahesh B Parappagoudar - One of the best experts on this subject based on the ideXlab platform.
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Optimization of Squeeze Cast Process Parameters Using Taguchi and Grey Relational Analysis
Procedia Technology, 2020Co-Authors: G C Manjunath Patel, Prasad Krishna, Mahesh B ParappagoudarAbstract:Abstract The near-net shape manufacturing capabilities of Squeeze Casting Process have greater potential to achieve smooth uniform surface and internal soundness in the cast components. In Squeeze Casting Process, Casting density and surface finish is influenced majorly by Process variables. Proper control of the Process variables is essential to achieve better results. Hence in the present work an attempt made using taguchi method to analyze the Squeeze cast Process variables such as Squeeze pressure, die and pouring temperature considering at three different levels using L9 orthogonal array. Pareto analysis of variance performed on each response to find out optimum Process parameter levels and significant contribution of each individual Process parameter towards surface roughness and density of LM20 alloy. Grey relation analysis used as a multi-response optimization technique to obtain the single optimal Process parameter setting for both the responses surface roughness and Casting density.
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a systematic approach to model and optimize wear behaviour of Castings produced by Squeeze Casting Process
Journal of Manufacturing Processes, 2018Co-Authors: Manjunath Patel G C, Arun Kumar Shettigar, Mahesh B ParappagoudarAbstract:Abstract The present work is an attempt to produce Squeeze cast component with excellent wear resistance property. The material wear rate in Squeeze Casting depends on appropriate selection of pressure duration, Squeeze pressure, die temperature and pouring temperature. Experiments are conducted and data is collected as per central composite and box-behnken design approaches. The input-output relationship developed by utilizing central composite design is found to be statistically adequate and yielded better prediction accuracy. Recurrent and back propagation neural networks are trained by using data generated from best response model. The huge training data in batch mode helps to capture fully the dynamics of Squeeze Casting Process. The recurrent neural network outperformed both, the back propagation neural network and central composite design. Genetic algorithm, desirability function approach, and particle swarm optimization are used to determine best set of Squeeze Casting conditions that locate the extreme values and will result in minimum wear rate. Particle swarm optimization and genetic algorithm outperformed desirability function approach, as the former carried out search in many directions at multi dimensional space, simultaneously. The results of non-linear regression, neural network based models, the performance of different optimization techniques are compared and some concluding remarks are made.
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back propagation genetic and recurrent neural network applications in modelling and analysis of Squeeze Casting Process
Applied Soft Computing, 2017Co-Authors: Manjunath Patel G C, Prasad Krishna, Arun Kumar Shettigar, Mahesh B ParappagoudarAbstract:Abstract Today, in competitive manufacturing environment reducing Casting defects with improved mechanical properties is of industrial relevance. This led the present work to deal with developing the input-output relationship in Squeeze Casting Process utilizing the neural network based forward and reverse mapping. Forward mapping is aimed to predict the Casting quality (such as density, hardness and secondary dendrite arm spacing) for the known combination of Casting variables (that is, Squeeze pressure, pressure duration, die and pouring temperature). Conversely, attempt is also made to determine the appropriate set of Casting variables for the required Casting quality (that is, reverse mapping). Forward and reverse mapping tasks are carried out utilizing back propagation, recurrent and genetic algorithm tuned neural networks. Parameter study has been conducted to adjust and optimize the neural network parameters utilizing the batch mode of training. Since, batch mode of training requires huge data, the training data is generated artificially using response equations. Furthermore, neural network prediction performances are compared among themselves (reverse mapping) and with those of statistical regression models (forward mapping) with the help of test cases. The results shown all developed neural network models in both forward and reverse mappings are capable of making effective predictions. The results obtained will help the foundry personnel to automate and precised control of Squeeze Casting Process.
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Multi-Objective Optimization of Squeeze Casting Process using Genetic Algorithm and Particle Swarm Optimization
Archives of Foundry Engineering, 2016Co-Authors: G. C. M. Patel, Prasad Krishna, Pandu R Vundavilli, Mahesh B ParappagoudarAbstract:Abstract 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.
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Squeeze Casting Process modeling by a conventional statistical regression analysis approach
Applied Mathematical Modelling, 2016Co-Authors: G Manjunath C Patel, Prasad Krishna, Mahesh B ParappagoudarAbstract:Abstract 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.
Prasad Krishna - One of the best experts on this subject based on the ideXlab platform.
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Optimization of Squeeze Cast Process Parameters Using Taguchi and Grey Relational Analysis
Procedia Technology, 2020Co-Authors: G C Manjunath Patel, Prasad Krishna, Mahesh B ParappagoudarAbstract:Abstract The near-net shape manufacturing capabilities of Squeeze Casting Process have greater potential to achieve smooth uniform surface and internal soundness in the cast components. In Squeeze Casting Process, Casting density and surface finish is influenced majorly by Process variables. Proper control of the Process variables is essential to achieve better results. Hence in the present work an attempt made using taguchi method to analyze the Squeeze cast Process variables such as Squeeze pressure, die and pouring temperature considering at three different levels using L9 orthogonal array. Pareto analysis of variance performed on each response to find out optimum Process parameter levels and significant contribution of each individual Process parameter towards surface roughness and density of LM20 alloy. Grey relation analysis used as a multi-response optimization technique to obtain the single optimal Process parameter setting for both the responses surface roughness and Casting density.
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back propagation genetic and recurrent neural network applications in modelling and analysis of Squeeze Casting Process
Applied Soft Computing, 2017Co-Authors: Manjunath Patel G C, Prasad Krishna, Arun Kumar Shettigar, Mahesh B ParappagoudarAbstract:Abstract Today, in competitive manufacturing environment reducing Casting defects with improved mechanical properties is of industrial relevance. This led the present work to deal with developing the input-output relationship in Squeeze Casting Process utilizing the neural network based forward and reverse mapping. Forward mapping is aimed to predict the Casting quality (such as density, hardness and secondary dendrite arm spacing) for the known combination of Casting variables (that is, Squeeze pressure, pressure duration, die and pouring temperature). Conversely, attempt is also made to determine the appropriate set of Casting variables for the required Casting quality (that is, reverse mapping). Forward and reverse mapping tasks are carried out utilizing back propagation, recurrent and genetic algorithm tuned neural networks. Parameter study has been conducted to adjust and optimize the neural network parameters utilizing the batch mode of training. Since, batch mode of training requires huge data, the training data is generated artificially using response equations. Furthermore, neural network prediction performances are compared among themselves (reverse mapping) and with those of statistical regression models (forward mapping) with the help of test cases. The results shown all developed neural network models in both forward and reverse mappings are capable of making effective predictions. The results obtained will help the foundry personnel to automate and precised control of Squeeze Casting Process.
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Multi-Objective Optimization of Squeeze Casting Process using Genetic Algorithm and Particle Swarm Optimization
Archives of Foundry Engineering, 2016Co-Authors: G. C. M. Patel, Prasad Krishna, Pandu R Vundavilli, Mahesh B ParappagoudarAbstract:Abstract 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.
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Squeeze Casting Process modeling by a conventional statistical regression analysis approach
Applied Mathematical Modelling, 2016Co-Authors: G Manjunath C Patel, Prasad Krishna, Mahesh B ParappagoudarAbstract:Abstract 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.
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an intelligent system for Squeeze Casting Process soft computing based approach
The International Journal of Advanced Manufacturing Technology, 2016Co-Authors: G Manjunath C Patel, Prasad Krishna, Mahesh B ParappagoudarAbstract: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.
Peter J Uggowitzer - One of the best experts on this subject based on the ideXlab platform.
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strength and fracture toughness of interpenetrating graphite aluminium composites produced by the indirect Squeeze Casting Process
Materials Science and Engineering A-structural Materials Properties Microstructure and Processing, 2004Co-Authors: T Etter, J Kuebler, T Frey, Peter Schulz, Jorg F Loffler, Peter J UggowitzerAbstract:Abstract Graphite/aluminium composites with an interpenetrating network microstructure were produced by the indirect Squeeze Casting Process. Porous isotropic graphite preforms with about 14.5 vol.% porosity were infiltrated either with AlSi7Ba or AlSi12. Flexural strength and fracture toughness were determined at room temperature and 300 °C before and after thermal cycling. The composites exhibit a significantly higher mechanical strength and fracture toughness than the graphite preforms. The infiltration with aluminium alloys enhances the flexural strength by a factor of two up to about 120 MPa and increases the fracture toughness from 0.94 to 1.93 MPam 1/2 . At 300 °C, no decrease in flexural strength of the composites is observed. Thermal cycling results in a slight reduction of the mechanical properties for graphite/AlSi7Ba composites, while graphite/AlSi12 composites show no decline in strength and toughness, respectively. The effect of aluminium composition and thermal cycling on the mechanical properties are explained by means of a crack bridging model.
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Strength and fracture toughness of interpenetrating graphite/aluminium composites produced by the indirect Squeeze Casting Process
Materials Science and Engineering A-structural Materials Properties Microstructure and Processing, 2004Co-Authors: T Etter, J Kuebler, T Frey, Peter Schulz, Jorg F Loffler, Peter J UggowitzerAbstract:Abstract Graphite/aluminium composites with an interpenetrating network microstructure were produced by the indirect Squeeze Casting Process. Porous isotropic graphite preforms with about 14.5 vol.% porosity were infiltrated either with AlSi7Ba or AlSi12. Flexural strength and fracture toughness were determined at room temperature and 300 °C before and after thermal cycling. The composites exhibit a significantly higher mechanical strength and fracture toughness than the graphite preforms. The infiltration with aluminium alloys enhances the flexural strength by a factor of two up to about 120 MPa and increases the fracture toughness from 0.94 to 1.93 MPam 1/2 . At 300 °C, no decrease in flexural strength of the composites is observed. Thermal cycling results in a slight reduction of the mechanical properties for graphite/AlSi7Ba composites, while graphite/AlSi12 composites show no decline in strength and toughness, respectively. The effect of aluminium composition and thermal cycling on the mechanical properties are explained by means of a crack bridging model.
C G Kang - One of the best experts on this subject based on the ideXlab platform.
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thermal fluid solidification analysis of automobile part by horizontal Squeeze Casting Process and experimental evaluation
Journal of Materials Processing Technology, 2004Co-Authors: S W Youn, C G KangAbstract:Abstract The Squeeze Casting Process of molten metal is very attractive for producing near net shape components with an arbitrary complicated shape. It is particularly attractive for Processing engine mounting brackets with high strength at desired elongation and hardness, since the porosity of dispersed defects under the action of the pressure is remarkably reduced during Squeeze Casting. Therefore, to develop suspension parts, such as the knuckle, arm, and structure parts to support the automobile engine, the Squeeze Casting Process was developed with die design and Process parameter control.
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Thermal fluid/solidification analysis of automobile part by horizontal Squeeze Casting Process and experimental evaluation
Journal of Materials Processing Technology, 2004Co-Authors: S W Youn, C G KangAbstract:Abstract The Squeeze Casting Process of molten metal is very attractive for producing near net shape components with an arbitrary complicated shape. It is particularly attractive for Processing engine mounting brackets with high strength at desired elongation and hardness, since the porosity of dispersed defects under the action of the pressure is remarkably reduced during Squeeze Casting. Therefore, to develop suspension parts, such as the knuckle, arm, and structure parts to support the automobile engine, the Squeeze Casting Process was developed with die design and Process parameter control.
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Microstructure and Mechanical Properties of $SiC_p/6061$ Al Composites Fabricated by Indirect Squeeze Casting
Journal of Korea Foundry Society, 1998Co-Authors: C G KangAbstract:Particulate reinforced aluminum alloys produced by indirect Squeeze Casting are difficult to shape by cutting or milling. Therefore near net shape forming of complex shapes is of high economic and technical interest. The complex shape products of Al composites are fabricated by the melt-stirring and indirect Squeeze Casting Process. The mold temperatures are and and applied pressures are 70, 100, and 130 MPa. The volume fractions of the reinforcements are in the range of 5 vol% to 15 vol%. The reinforcement dispersion state are observed using on optical microscope. By employing observed results systematically a correlation is demonstrated among the microstructure, particles behavior, mechanical properties and Processing parameters for an optimum melt-stirring(compoCasting) and indirect Squeeze Casting Process of MMCs. A procedure to establish the optimum Squeeze Casting of Al-MMCs is proposed.
Jianxin Deng - One of the best experts on this subject based on the ideXlab platform.
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FSKD (3) - Squeeze Casting Process Knowledge System Using Minimum Subjection Degree Fuzzy Reasoning
2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery, 2009Co-Authors: Jianxin DengAbstract:Process parameters are crucial to produce Squeeze Casting components. This paper presented an innovative approach to obtain Squeeze Casting Process parameters based on knowledge management. The Squeeze Casting Process knowledge was analyzed, namely, component design features, Process parameters and component quality indexes, which are structural data with casual relations. A five-layered Squeeze Casting Process knowledge management system was designed, which used relational database to store Squeeze Casting Process knowledge. To obtain Process parameters of new components by selecting the most appropriate options from the system, a fuzzy reasoning method using minimum subjection degree of material and geometry was introduced; the paper constructed the subjection degree calculation equations. It is indicated that this saves time of getting Process parameters and helps use experiment results efficiently.
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Squeeze Casting Process Knowledge System Using Minimum Subjection Degree Fuzzy Reasoning
2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery, 2009Co-Authors: Jianxin DengAbstract:Process parameters are crucial to produce Squeeze Casting components. This paper presented an innovative approach to obtain Squeeze Casting Process parameters based on knowledge management. The Squeeze Casting Process knowledge was analyzed, namely, component design features, Process parameters and component quality indexes, which are structural data with casual relations. A five-layered Squeeze Casting Process knowledge management system was designed, which used relational database to store Squeeze Casting Process knowledge. To obtain Process parameters of new components by selecting the most appropriate options from the system, a fuzzy reasoning method using minimum subjection degree of material and geometry was introduced; the paper constructed the subjection degree calculation equations. It is indicated that this saves time of getting Process parameters and helps use experiment results efficiently.