Abstract
A new application of fuzzy systems to the processing of materials is presented. The relationships between temperature, time, and the impact strength of an austempered ductile iron (ADI) part are adaptively modeled. Four fuzzy and neuro fuzzy approaches have been used to build predictive models. These are: a fuzzy based model, a backpropagation based neuro fuzzy model, a clustering based model, and a clustering backpropagation based neuro fuzzy model. The clustering approach, using the subclustering method, yielded the best predictive results when all models had been given the same input-output training data. The backpropagation based neuro fuzzy approach suffers from the lack of a higher number of input-output data training sets. All preliminary results obtained suggest the adequacy of the fuzzy based and neuro fuzzy based modeling techniques to tackle those types of problems in the material processing areas.
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