Tribological behavior prediction of friction materials for ultrasonic motors using Monte Carlo‐based artificial neural network

Abstract

In this article, the relationship of complexity, diversity, and uncertainty between components and tribological properties of friction materials based on a Monte Carlo-based artificial neural network (MC-ANN) model was predicted precisely. Meanwhile, the grey relational analysis was applied to figure out weight of factors, optimize formulation design, and calculate nonlinear dependency of ingredients. The accuracy of model was studied by comparing experimental and simulated values on the basis of statistical methods (root-mean-squared error). It was found that the model exhibited an excellent performance in predicting and fitting effect. Moreover, comprehensive analysis of weight indicated that nano-SiO 2 and mica exerted a significant role in improving the friction stability and wear resistance. According to different contents of each ingredient, the corresponding friction coefficient and specific wear rate could be obtained by virtue of a well-trained MC-ANN model without experiments, which saved a lot of time and money. It can be expected that the results of this work will extend the current research and pave a route for further in-depth studies of friction materials.

The effect of different layered materials on the tribological properties of PTFE composites

“…The formulations of PTFE composites are listed in Table 1 and obtained with our previous method [25][26][27][28].…”

A review of recent advances and applications of machine learning in tribology

“…Therefore, MC-ANN, introduced by Li et al exercises the MC approach to estimate the weights of each neuron in a probabilistic manner. 188 A faster and more accurate MC-ANN aids in better non-linear mapping ability and improved fitting effect of samples. The comparison of the workflow of MC-ANN that is featured in the work of Li et al is shown in Fig.…”

“…In the context of variation and volatility of the data, MC-ANN performs better than conventional ANN because it uses repeated random sampling and a variety of transfer functions like sigmoid, polynomial, tanh, and Gauss functions. 188 (ii) Improved bat algorithm (IBA). A meta-heuristic global optimization algorithm, improved bat algorithm (IBA) is based on the echolocation characteristics of the bat.…”

Current Trends and Applications of Machine Learning in Tribology—A Review

“…A network trained by gradient search and consisting of three hidden layers (15, 10, and 5 neurons) and tan-sigmoid transfer functions between the input and the hidden layers as well as pure linear transfer functions to the output layer was found to deliver the least mean square errors. Li et al [51] applied a Monte Carlo-based ANN to predict the tribological behavior of PTFE resin with aramid pulp, potassium titanate whisker (PTW), mica, copper (Cu) as well as silicon dioxide (SiO 2 ) for ultrasonic motors and compared the performance to a back propagation ANN. The database, an orthogonal table by variation of the composition, was generated from experiments conducted in triplicate on a quasi-static test rig where the specimens were fixed on a dynamic rotor and slid against a phosphor bronze stator at constant speed and load.…”