Deep-Learning-Coupled Numerical Optimization Method for Designing Geometric Structure and Insertion-Withdrawal Force of Press-Fit Connector
简介：The press-fit connector is a typical plug-and-play solderless connection,and it is widely used in signal transmission in fields such as communication and automotive devices.This paper focuses on inverse designing and optimization of geo-metric structure,as well as insertion-withdrawal forces of press-fit connector using artificial neural network(ANN)-assisted optimization method.The ANN model is established to approximate the relationship between geometric parameters and insertion-withdrawal forces,of which hyper-parameters of neural network are optimized to improve model performance.Two numerical methods are proposed for inverse designing structural parameters(Model-Ⅰ)and multi-objective optimization of insertion-withdrawal forces(Model-Ⅱ)of press-fit connector.In Model-Ⅰ,a method for inverse designing structure parameters is established,of which an ANN model is coupled with single-objective optimization algorithm.The objective function is established,the inverse problem is solved,and effectiveness is verified.In Model-Ⅱ,a multi-objective optimization method is proposed,of which an ANN model is coupled with genetic algorithm.The Pareto solution sets of insertion-withdrawal forces are obtained,and results are analyzed.The established ANN-coupled numerical optimization methods are beneficial for improving the design efficiency,and enhancing the connection reliability of the press-fit connector.展开
学者：MingzhiWangBingyuHouWeidong
关键词：Press-fit connectorCompliant pinInsertion-withdrawal forceOptimization designNeural network model
在线出版日期：2025-04-25 （网站首发日期）
