文献类型：期刊文献

英文题名：Multi-objective optimization of sawblade multi-spot pressure tensioning process based on backpropagation neural network and genetic algorithm

作者：Yu, Mingyang[1] Wang, Bin[1] Ji, Pengliang[1] Li, Bo[2,4] Zhang, Luo[3] Zhang, Qingdong[1]

第一作者：Yu, Mingyang

通信作者：Li, B[1]

机构：[1]Univ Sci & Technol Beijing, Sch Mech Engn, Beijing, Peoples R China;[2]Chinese Acad Forestry, Res Inst Wood Ind, Beijing, Peoples R China;[3]AVIC Changhe Aircraft Ind Grp Corp Ltd, Jingdezhen, Jiangxi, Peoples R China;[4]Chinese Acad Forestry, Res Inst Wood Ind, Beijing 100091, Peoples R China

年份：2023

外文期刊名：WOOD MATERIAL SCIENCE & ENGINEERING

收录：;EI(收录号：20232914420389);WOS:【SCI-EXPANDED(收录号:WOS:001024910800001)】；

语种：英文

外文关键词：Intelligence algorithms; Finite element method; Critical rotation speed; Lateral stiffness; >

摘要：Resources scarcity has created strict requirements for efficient wood processing. Multi-spot pressure tensioning can effectively stabilize the sawblade and reduce cutting losses, but it is difficult to apply in practice due to the complexity of the process. In this study, the elastic-plastic solid model of multi-spot pressure tensioning was established, which was simplified to a thermal expansion shell model, and the mapping relationship between the two models was determined. The feasibility of the mapping method was verified by experiments. The backpropagation neural network (BPNN) was trained on a database composed of 8160 working conditions and the relative error was found to be less than 5%. Indenter displacement, pressing point quantity, and indenter radius were shown to change the degree of tension to varying extent. The pressing-point-distribution radius determines the development direction of sawblade performance. Increasing the number of pressing point circles can expand the adjustment range for sawblade performance. Both sawblade performance and tensioning energy consumption can be optimized by the genetic algorithm (GA). The optimal process parameters for different applications can be obtained. The combination of finite element method, BPNN, and GA can effectively optimize the multi-spot pressure tensioning process to improve the sawblade performance.