面曝光快速成形系统制件的强度模型研究  

Research on strength model for mask exposal rapid prototyping system components

作　　者：巨孔亮 JU Kongliang(Xi′an Polytechnic University,Xi′an 710048,CHN)

机构地区：[1]西安工程大学,陕西西安710048

出　　处：《制造技术与机床》2021年第8期131-136,共6页Manufacturing Technology & Machine Tool

基　　金：国家自然科学基金面上项目(22078253);陕西省科技厅重点研发计划项目(2020GY-276)。

摘　　要：为了快速准确地获得面曝光快速成形制件的强度,利用传统多项式和BP神经网络建立了制件强度模型,建模结果显示二次多项式模型的最大偏差为9.5632 MPa,平均偏差为2.3812 MPa,BP神经网络模型的最大偏差为4.997 MPa,平均偏差为0.8435 MPa。研究结果表明:BP神经网络模型计算结果优于二次多项式模型,并具有一定的预测能力,可用于面曝光快速成形系统制件强度模型的建立。In order to quickly and accurately obtain the strength of mask exposal rapid prototyping system components,traditional polynomial and BP neural network were deployed to establish components strength models in this study.The modeling results reveal that the maximum deviation and average deviation of the quadratic polynomial model are confirmed to be 9.5632 and 2.3812 MPa,respectively.Meanwhile,the maximum deviation and average deviation of the BP neural network model are determined to be 4.997 and 0.8435 MPa,respectively.Comparison between the two models demonstrates that the BP neural network model is superior to the quadratic polynomial model in calculation results and performs good predictive ability.Therefore,BP neural network can be used to establish the strength model of mask exposal rapid prototyping system components.

关 键 词：面曝光 快速成形 强度模型 BP神经网络 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]