For training artificial neural network (ANN), big data either generated by machine or measured from experiments are used as input to “learn” the unspecified functions defining the ANN. The experimental data are fed directly into the optimizer allowing training to be performed according to a predefined loss function. To predict sliding friction and wear at mixed lubrication conditions, in this study a specific ANN structure was so designed that deep learning algorithms and data-driven optimization models can be used. Experimental ball-on-plate friction and wear data were analyzed using the specific training procedure to optimize the weights and biases incorporated into the neural layers of the ANN, and only two independent experimental data sets were used during the ANN optimization procedure. After the training procedure, the ANN is capable to predict the contact and hydrodynamic pressure by adapting the output data according to the tribological condition implemented in the optimization algorithm.

为了训练人工神经网络 (ANN)，机器生成的或实验测量的大数据被用作输入来“学习”定义 ANN 的未指定函数。实验数据直接输入优化器，允许根据预定义的损失函数进行训练。为了预测混合润滑条件下的滑动摩擦和磨损，本研究设计了特定的 ANN 结构，可以使用深度学习算法和数据驱动的优化模型。使用特定的训练程序分析实验球板摩擦和磨损数据，以优化纳入 ANN 神经层的权重和偏差，并且在 ANN 优化过程中仅使用两个独立的实验数据集。训练过程结束后，人工神经网络能够根据优化算法中实现的摩擦条件调整输出数据，从而预测接触和流体动力压力。