Affiliation:
1. School of Mechanical Engineering, Shandong University of Technology, Zibo 255049, China
2. Shandong Provincial Key Laboratory of Precision Manufacturing and Non-Traditional Machining, Shandong University of Technology, Zibo 255049, China
Abstract
Many efforts have been devoted to the forecasting of the capillary force generated by capillary adsorption between solids, which is fundamental and essential in the fields of micro-object manipulation and particle wetting. In this paper, an artificial neural network (ANN) model optimized by a genetic algorithm (GA-ANN) was proposed to predict the capillary force and contact diameter of the liquid bridge between two plates. The mean square error (MSE) and correlation coefficient (R2) were employed to evaluate the prediction accuracy of the GA-ANN model, theoretical solution method of the Young–Laplace equation and simulation approach based on the minimum energy method. The results showed that the values of MSE of capillary force and contact diameter using GA-ANN were 10.3 and 0.0001, respectively. The values of R2 were 0.9989 and 0.9977 for capillary force and contact diameter in regression analysis, respectively, demonstrating the accuracy of the proposed predictive model. The sensitivity analysis was conducted to investigate the influence of input parameters, including liquid volume and separation distance, on the capillary force and contact diameter. The liquid volume and separation distance played dominant roles in affecting the capillary force and contact diameter.
Funder
Shandong Provincial Postdoctoral Science Foundation
National Natural Science Foundation of China
Shandong Provincial Key Laboratory of Precision Manufacturing and Non-traditional Machining of China
Shandong Provincial Natural Science Foundation of China
Subject
Electrical and Electronic Engineering,Mechanical Engineering,Control and Systems Engineering
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