Author : Ganesh G. Patil and K. H. Inamdar

Casting process is the most widely used process in manufacturing industries especially in automobile products. Systematic analysis and identification of sources of product defects are essential for successful manufacturing. Since the quality of casting parts are mostly influenced by process conditions, how to determine the optimum process condition becomes the key to improving part quality. The industry generally tries to eliminate the defects by trial and error method, which is an expensive and error-prone process. This paper presents review on a use of Artificial neural network (ANN) for the casting processes better than the other techniques such as design of experiment (DOE), inspection method, casting simulation, cause-effect diagram, genetic algorithm, fuzzy logic. ANN has challenges in the eve of prediction, optimization, control, monitor, identification, classification, modeling and so on particularly in the field of manufacturing. We discuss number of key issues, which must be addressed when applying neural network to practical problems, and steps followed for the development of such models are outlined. Artificial neural network also found that, the trained network has great forecast ability. Furthermore, the trained neural network is employed as an objective function to optimize the processes.

Keywords: Artificial Neural Network (ANN), Casting Process, Optimization, Product defects