Predicting the Abrasion Resistance of Tool Steels by Means of Neurofuzzy Model

This work considers use neurofuzzy set theory for estimate abrasion wear resistance of steels based on chemical composition, heat treatment (austenitising temperature, quenchant and tempering temperature), hardness after hardening and different tempering temperature and volume loss of materials according to ASTM G 65-94. Testing of volume loss for the following group of materials as fuzzy data set was taken: carbon tool steels, cold work tool steels, hot work tools steels, high-speed steels. Modelled adaptive neuro fuzzy inference system (ANFIS) is compared to statistical model of multivariable non-linear regression (MNLR). From the results it could be concluded that it is possible well estimate abrasion wear resistance for steel whose volume loss is unknown and thus eliminate unnecessary testing.