LAUSR

In this study, artificial neural networks (ANNs) technique was used in the prediction of abrasive wear rate of high-Cr white cast irons (HCCIs) after subcritical heat treatment at different temperatures. High Cr WCI alloys with different compositions were tested at sliding speed of 1.04 m s−1 under the normal load of 30 N and different sliding distances of 500, 1000 and 1500 m. The abrasive wear rates obtained from wear tests were used in the formation of the data sets of the ANN. A multilayer perceptron model has been constructed with back-propagation algorithm using the input parameters of load, tempering temperature, and Cr/C ratio. The output parameter of the model is abrasive wear rate. Experimental results showed that abrasive wear rate of high-Cr WCI was significantly increased with the increasing of Cr/C ratio. High-Cr WCI alloys with higher volume fraction of carbides and structures with martensitic matrix at lower Cr/C ratio exhibited lower abrasive weight losses. The increasing of both sliding distance and load increases the abrasive weight losses. The HCCI-2 alloy exhibited the lower abrasive weight losses as compared with the other alloys in both as-cast and heat-treated conditions. In addition, the abrasive weight losses for all investigated alloys with different Cr/C ratios after destabilization heat treatment are lower than alloys in the as-cast state. This is may be due to the presences of stronger martensitic matrix structure rather than austenitic or pearlitic matrix structures. Correlation coefficients between the experimental data and outputs from the ANN established the feasibility of ANNs to effectively model and predict the wear rate of high Cr WCI. From the sensitivity analysis, it is concluded that the tempering temperature had the most influence on the wear rate, while the applied load and Cr/C ratio had a small influence on the wear rate of high-Cr WCI.