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首页> 外文会议>International Conference on Engineering Tribology and Applied Technology >Use of Artificial Neural Network (ANN) to Determining Surface Parameters, Friction and Wear during Pin-on-Disc Tribotesting
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Use of Artificial Neural Network (ANN) to Determining Surface Parameters, Friction and Wear during Pin-on-Disc Tribotesting

机译：使用人工神经网络（ANN）来确定贴椎上盘摩托车的表面参数，摩擦和磨损

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摘要

In tribological analysis of machine elements (such as gears, ball/roller bearings etc.), surface roughness plays very important role, ultimately it affects the friction coefficient, wear, rolling contact fatigue (micro pitting) and other failure mechanisms. Surface geometry and topography changed with time (number of cycles) during rolling/sliding motion of contacting surfaces. So, it is significant to show the variation of surface topography parameter during wear process. This work presents the evolution of roughness parameters, wear and friction coefficient during pin-on-disc tribotesting under dry condition. The test is performed using pin on disc apparatus under room temperature condition. Pin (25mm long, 6mm diameter) is made of medium carbon steel (AISI 1038) whereas disc (165mm diameter, 8mm thickness) is made of high carbon steel (SAE 52100). This works demonstrates the potential of Artificial Neural Network (ANN) for prediction of roughness parameters, friction coefficient and wear coefficient. Experimental results obtained from wear testing are compared with those obtained using artificial neural network (ANN) analysis. A very good agreement in results is suggested that, a well trained neural network is capable to predict the parameters in wear process.
机译：在机器元件的摩擦学分析（如齿轮，球/滚子轴承等）中，表面粗糙度起到非常重要的作用，最终它会影响摩擦系数，磨损，滚动接触疲劳（微点蚀）和其他故障机制。表面几何形状和地形随时间（循环数）在接触表面的滚动/滑动运动期间发生变化。因此，显着显示磨损过程中表面形貌参数的变化。在干燥条件下，这项工作介绍了粗糙度参数，磨损和摩擦系数的演变。在室温条件下使用销钉在盘装置上进行测试。引脚（长25mm，直径为6mm）由中碳钢（AISI 1038）制成，而盘（165mm直径，8mm厚）由高碳钢（SAE 52100）制成。这作用证明了人工神经网络（ANN）的潜力，用于预测粗糙度参数，摩擦系数和磨损系数。将从磨损测试获得的实验结果与使用人工神经网络（ANN）分析获得的实验结果进行比较。在结果方面存在非常良好的一致性，训练有素的神经网络能够预测磨损过程中的参数。

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