In the present research work, the effects of Titanium (Ti) and Tungsten (W) addition on tribological behavior of powder metallurgy (P/M) Fe-1%C steel have been investigated. The test specimens of plain carbon steel and 1%Ti, 1%W and 1%Ti+1%W added plain carbon steels were used to conduct the wear tests and wear behavior analyses. The optical and SEM images of wear tracks and microstructures of the alloys were obtained and analysed with wear behavior of the alloy steels. Artificial Neural Network (ANN) software was used to check the degree of agreement of test results with predicted values. The experimental results show that Ti and W added alloy steel exhibits excellent wear resistance. The carbides formation due to alloying elements pronounces the wear resistance of the alloy steel. It has been proven that ANN could be used as a tool to predict the wear behavior of the P/M alloy steels by agreement between the predicted and experimental values.

中文翻译：

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利用人工神经网络预测粉末冶金Ti和W添加低合金钢的摩擦学特性

在目前的研究工作中，已经研究了钛（Ti）和钨（W）的添加对粉末冶金（P / M）Fe-1％C钢的摩擦学行为的影响。用普通碳素钢和添加了1％Ti，1％W和1％Ti + 1％W的普通碳素钢的试样进行了磨损测试和磨损性能分析。获得了合金的磨损轨迹和显微组织的光学和SEM图像，并通过合金钢的磨损行为对其进行了分析。人工神经网络（ANN）软件用于检查测试结果与预测值的一致性程度。实验结果表明，添加Ti和W的合金钢具有优异的耐磨性。由于合金元素而形成的碳化物表明了合金钢的耐磨性。