Shear strength prediction of Ni–Ti alloys manufactured by powder metallurgy using fuzzy rule-based model

Abstract: Powder metallurgy is an important manufacturing method and developing models that can predict the characteristics of the products is remarkable for researchers. There are many models discussed in the literature for prediction of the product properties however, nonlinear modeling methods including artificial neural networks (ANNs) and fuzzy models have shown better performance. In the present work, a rule-based fuzzy logic model is developed to predict the shear strength of Ni–Ti alloys specimens manufactured by powder metallurgy method. The processing time and temperature are selected as the input variables and a fuzzy model is designed with two inputs and one output variable. Four statistical parameters are used for assessment of the model accuracy. The comparison of this model result and the result of the ANN model that have been reported by previous researchers, shows that the fuzzy model is more accurate and actually better than ANN model for predicting the shear strength of Ni–Ti alloys specimens manufactured by powder metallurgy. [Copyright &y& Elsevier]