In this paper an attempt has been made for linear and non linear modeling of resin bonded sand mould system using full factorial design of experiments and response surface methodology, respectively. It is important to note that the quality of castings produced using the resin bonded sand mould system depends largely on properties of moulds, which are influenced by the characteristics of sand, like type of sand, grain fineness number, grain size distribution and quantity and type of resin, catalyst, curing time etc. In the present study, percentage of resin, percentage of hardener, number of strokes and curing time are considered as input parameters and the mould properties, such as compression strength, shear strength, tensile strength and permeability are treated as responses. In the present work, phenol formaldehyde is used as the resin whereas tetrahydrophthalic anhydride as the hardener. A two level full factorial and three level central composite designs are utilized to develop input-output relationships. Surface plots and main effects plots are used to study the effects of amount of resign, amount of hardener, number of strokes and curing time on the responses, namely, compression strength, tensile strength, shear strength and permeability. Moreover, the adequacies of the developed models have been tested using analysis of variance. The prediction accuracy of the developed models have been tested with the help of twenty test cases and found reasonably good accuracy.
An expert system is developed in the present paper to predict the mechanical properties of chromite-based resin bonded sand core system. The various properties of sand cores, such as hardness, compression strength, collapsibility, tensile strength, hot strength, shear strength and permeability depends upon numerous process parameters, viz. percentage of resin, percentage of hardener, additives, number of strokes and curing time. In this paper, Mamdani-based fuzzy logic (FL) approach has been used to develop the knowledge base scheme(that is, forward and reverse modeling) for chromite-based resin bonded sand core system. However, the prediction efficiency of FL system depends on the knowledge base (KB), which consists of rule base and data base. Three varied approaches have been implemented in the suggested work. Manually constructed FL system is developed in the first Approach, whereas in Approach 2, genetic algorithm (GA) is exploited to optimize the data base and rule base of FL system developed in Approach 1. On the other hand in Approach 3, automatic evolution of rules is well advised along with the use of GA to optimize data base and rule base. The developed FL system annihilates the need of extensive experimental work in selecting the most potent process parameters. The performances of all three approaches have been examined with the help of twenty experimental test cases. It is to be stated that all the three developed approaches can be effectively used in foundry for making prediction. The results showed that the Approach 3 has outperformed the remaining two, in terms of prediction accuracy.
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