Abstract
The growing demand in today’s competitive manufacturing environment has encouraged the researchers to develop and apply modelling tools. The development and application of modelling tools help the casting industries to considerably increase productivity and casting quality. Till date there is no universal standard available to model and optimize any of the manufacturing processes. However the present work discusses the advantages and limitations of some conventional and non-conventional modelling tools applied for various casting processes. In addition the research effort made by various authors till date in modelling and optimization of the squeeze casting process has been reported. Furthermore the necessary steps for prediction and optimization are high lightened by identifying the trends in the literature. Ultimately this research paper explores the scope for future research in online control of the process by automatically adjusting the squeeze cast process parameters through reverse prediction by utilizing the soft computing tools namely, Neural Network, Genetic Algorithms, Fuzzy-logic Controllers and their different combinations. The present work also proposed a detailed methodology, starting from the selection of process variables till the best process variable combinations for extreme values of the outputs responsible for better product quality using experimental, prediction and optimization methodology.
Highlights
In today’s competitive world industries are searching for light weight materials possess high strength to weight ratio with less defective processing methods
The optimized parameter setting suggested by the particle swarm optimization (PSO) and Genetic algorithm (GA) are compared with the experimental cases and the results shown PSO outperforms GA in terms of for extreme values prediction of all the responses and computational efficiency [97]
The present work describes the current state of art and the future scope for improving the squeeze cast component quality through the applications of conventional and unconventional modelling tools
Summary
In today’s competitive world industries are searching for light weight materials possess high strength to weight ratio with less defective processing methods. The limitations of conventional modelling tools such as only one response can be determined at a time and the practical requirement is to obtain the input variable combinations that will produce the desired output through reverse prediction might be difficult using statistical tools These problems can be effectively tackled through soft computing tools like GA, NN, FL, PSO and their different combinations. To accurately control the quality of the moulding sands [85] and to predict the presence/absence of the casting defects [86] such as hot crack, misrun, scab blow hole and air lock in the sand mould system, NN is used It is to be note from the above literatures authors successfully implemented to predict the outputs (responses) from the known set of inputs (process parameters) via forward modelling. The optimized parameter setting suggested by the PSO and GA are compared with the experimental cases and the results shown PSO outperforms GA in terms of for extreme values prediction of all the responses and computational efficiency [97]
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