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Optimization of Manufacturing ProcessesModelling and Optimization of Alpha-set Sand Moulding System Using Statistical Design of Experiments and Evolutionary Algorithms

Optimization of Manufacturing Processes: Modelling and Optimization of Alpha-set Sand Moulding... [The traditional trial-and error method applied to derive empirical relation and optimize the process is time consuming and results in reduced productivity, high rejection and cost. Hence, current research in foundries focussed towards development of statistical modelling and optimization tools. The present research work is focused on modelling and optimization of Alpha-set moulding sand system. The variables such as percent of resin and hardener, and curing time will influence the sand mould properties, namely, compression strength, permeability, mould hardness, gas evolution and collapsibility. Experimental data is collected as per CCD design matrix and non-linear models have been developed for all responses. The behaviour of all responses is studied by utilizing surface plots. The statistical adequacy of all models is tested with help of ANOVAAnalysis of Variance (ANOVA). All responses are tested for their prediction capacity with the help of test cases. The predictive non-linear models, developed for the process resulted in average deviation of less than 5%. TheParticle swarm optimizationoptimizationGenetic algorithm (GA, PSO, DFA and TLBO) tools are applied to optimize the process for conflicting requirements in sand mould properties. Six case studies with different combination of weight fractions assigned to sand mould properties are considered. The optimum solution correspond to highest composite desirabilityDesirability value is selected. TLBO outperformed other optimization tools (i.e. GA, PSO, and DFA) while determining the highest desirability value and resulted in optimized sand mould properties. Experiments are conducted for the optimized and normal (i.e. lowest desirability) sand mould conditions. Castings are prepared by pouring molten LM20 alloy to the prepared moulds. The casting obtained for the optimized sand mould condition resulted in a better casting qualityQuality.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Optimization of Manufacturing ProcessesModelling and Optimization of Alpha-set Sand Moulding System Using Statistical Design of Experiments and Evolutionary Algorithms

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Publisher
Springer International Publishing
Copyright
© Springer Nature Switzerland AG 2020
ISBN
978-3-030-19637-0
Pages
1 –28
DOI
10.1007/978-3-030-19638-7_1
Publisher site
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Abstract

[The traditional trial-and error method applied to derive empirical relation and optimize the process is time consuming and results in reduced productivity, high rejection and cost. Hence, current research in foundries focussed towards development of statistical modelling and optimization tools. The present research work is focused on modelling and optimization of Alpha-set moulding sand system. The variables such as percent of resin and hardener, and curing time will influence the sand mould properties, namely, compression strength, permeability, mould hardness, gas evolution and collapsibility. Experimental data is collected as per CCD design matrix and non-linear models have been developed for all responses. The behaviour of all responses is studied by utilizing surface plots. The statistical adequacy of all models is tested with help of ANOVAAnalysis of Variance (ANOVA). All responses are tested for their prediction capacity with the help of test cases. The predictive non-linear models, developed for the process resulted in average deviation of less than 5%. TheParticle swarm optimizationoptimizationGenetic algorithm (GA, PSO, DFA and TLBO) tools are applied to optimize the process for conflicting requirements in sand mould properties. Six case studies with different combination of weight fractions assigned to sand mould properties are considered. The optimum solution correspond to highest composite desirabilityDesirability value is selected. TLBO outperformed other optimization tools (i.e. GA, PSO, and DFA) while determining the highest desirability value and resulted in optimized sand mould properties. Experiments are conducted for the optimized and normal (i.e. lowest desirability) sand mould conditions. Castings are prepared by pouring molten LM20 alloy to the prepared moulds. The casting obtained for the optimized sand mould condition resulted in a better casting qualityQuality.]

Published: Jun 26, 2019

Keywords: Casting; Moulding; Design of experiments; Genetic algorithm; Teaching learning-based optimization; Particle swarm optimization

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