传感技术学报2026，Vol.39Issue(2)：295-301,7.DOI:10.3969/j.issn.1004-1699.2026.02.010
基于人工神经网络的超微量液滴分配参数配置规律与体积预测方法研究
Research on the Distribution Parameters and Volume Prediction Method of Ultrafine Droplets Based on Artificial Neural Network
摘要
Abstract
The micro-nano scale ultrafine gel distribution technology is one of the core technologies urgently needed in the field of high performance micro-nano packaging,connection and assembly manufacturing.However,it is the key to accurately control the working pa-rameters to achieve the accurate control of the microdroplet distribution volume by clarifying the action law of process parameters on the microdroplet distribution results and realizing the accurate prediction of the microdroplet size and volume.A method based on artificial neural network is proposed to analyze the dispensing properties of the needle transfer microdroplet distribution system developed.Nu-merical simulation is used to analyze the influence of pipetting needle tip diameter,dispensing distance,contact distance and other pro-cessing parameters on the size and surface quality of the drops.In addition,the neural network prediction model of microdroplet distribu-tion process is established,and the correlation effect of experimental parameters on the diameter of the glue spot is analyzed,and the process parameter matching method is further formed.Through univariate experiment,the theoretical results of working parameter influ-ence law obtained from the neural network model and numerical simulation are verified.Moreover,the validity and reliability of the em-pirical model of micro-dispensing neural network and the feasibility of the process parameter configuration method are further verified through orthogonal experiment.关键词
微量点胶/点胶分析/人工神经网络/正交试验Key words
micro dispensing/dispensing analysis/artificial neural networks/orthogonal test分类
机械制造引用本文复制引用
迟成涛,刘慧芳,王文国,刘小江,许哲名,赵丁睿..基于人工神经网络的超微量液滴分配参数配置规律与体积预测方法研究[J].传感技术学报,2026,39(2):295-301,7.基金项目
国家自然科学基金项目(52175428) （52175428）
辽宁省教育厅重点项目(LJKZZ20220020) （LJKZZ20220020）
辽宁省研究生教育教学改革项目LNYJG2022067 （）
