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首页> 外文会议>International Conference on Engineering Materials, Metallurgy and Manufacturing >Effect of Surface Modification Using GTAW as Heat Source and Cryogenic Treatment on the Surface Hardness and Its Prediction Using Artificial Neural Network
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Effect of Surface Modification Using GTAW as Heat Source and Cryogenic Treatment on the Surface Hardness and Its Prediction Using Artificial Neural Network

机译：表面改性使用GTAA作为热源和低温处理对表面硬度的影响及其预测使用人工神经网络

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High-wear-resisting EN 31 bearing steel has been widely used to make components such as roller bearing, ball bearing, spline shaft, and other components like tiller blades, punches and dies are subjected to severe abrasion to require high surface hardness. To obtain high surface hardness, EN 31 steel is usually surface modified using various methods like conventional heat treatment (591 HV), cryogenic treatment (688 HV) and GMAW. But, there are no studies on surface modification of EN 31 using gas tungsten arc (GTA) heat source followed by cryogenic treatment. To improve the hardness further, surface alloying using gas tungsten arc followed by cryogenic treatment is done in this study. EN 31 steel is surface-hardened by using GTA heat source by varying the welding current, electrode tip angle and shallow and deep cryogenic treatments (SCT & DCT) by varying soaking time and temperature. Microstructures were studied and micro-hardness was measured. It is found that cryogenic treatment leads to formation of carbide particles in martensite matrix with reduced retained austenite which improves the microhardness from 258 to 898 HV after SCT and 1856 HV for DCT. Further, in this work, a back-propagation artificial neural network (ANN) which uses gradient descent learning algorithm is used to predict the microhardness of EN 31 steel for the entire ranges of parameters used in the experiments. The ANN model is trained and tested using 200 experiments done. The input parameters of the ANN model are 4 variables (welding current, electrode tip angle, cryogenic soaking time and temperature). Using MATLAB, a programme was developed and by varying the transfer function (tansig and logsig) different ANN models are constructed for the prediction of microhardness. This study shows that
机译：高耐磨EN 31轴承钢已被广泛用于制造滚子轴承，滚珠轴承，花键轴等部件，如耕地叶片，冲头和模具，以严重磨损，以需要高表面硬度。为了获得高表面硬度，EN 31钢通常使用常规热处理（591 HV），低温处理（688 HV）和GMAW等各种方法进行表面改性。但是，使用钨弧（GTA）热源随后使用气体钨弧（GTA）热源而没有研究EN 31的研究。为了进一步提高硬度，在本研究中进行了使用气体钨弧的表面合金化，然后进行低温处理。通过改变浸泡时间和温度来改变焊接电流，电极尖端角度和浅层和深度低温处理（SCT＆DCT），通过使用GTA热源来表面硬化。研究了微观结构，测量微硬度。发现低温处理导致马氏体基质中形成碳化物颗粒，其残留的奥氏体减少，其在SCT和1856 HV之后将显微硬度从258〜898 HV改善为DCT。此外，在该工作中，使用梯度下降学习算法的反向传播人工神经网络（ANN）用于预测EN 31钢的微硬度，用于实验中使用的整个参数范围。 ANN模型使用200实验进行培训并进行测试。 ANN模型的输入参数是4个变量（焊接电流，电极尖角，低温浸泡时间和温度）。使用MATLAB，开发了一个程序，通过改变传递函数（TANSIG和LOGSIG）不同的ANN模型被构造用于预测显微硬度。这项研究表明

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