双面渐进成形工艺参数优化及减薄率的预测
Optimization of process parameters and prediction of thinning rate for double-sided incremental forming
张澧桐 1田雨 1顾鹏 2张鑫1
作者信息
- 1. 长春理工大学机电工程学院,吉林 长春 130022
- 2. 长春设备工艺研究所,吉林 长春 130000
- 折叠
摘要
渐进成形的减薄率是衡量成形件质量的重要指标.文章采用Box-Behnken设计实验方案进行试验,分析了刀具直径D、层间距Δz、成形角α和板厚t对减薄率的影响,并得到试验最优的参数组合.建立了工艺参数到减薄率的BP神经网络模型,用数据训练集训练网络,计算测试集减薄率预测模型的精度.针对BP神经网络平均误差大(6.42%)的问题,用粒子群算法(PSO)优化了BP神经网络模型参数,使预测误差降低到 2.24%.PSO-BP 神经网络模型可以有效预测工艺参数和减薄率的关系.
Abstract
The rate of thinning in incremental forming is a crucial indicator for assessing the quality of formed parts.In this study,we conducted experiments using a Box-Behnken design experimental scheme to analyze the impact of tool diameter(D),layer spacing(Δz),forming angle(α),and plate thickness(t)on the thinning rate.By obtaining an optimal combination of these parameters,we established a BP neural network model that correlates process parameters with thinning rate.The model was trained using a data training set and its accuracy in predicting the thinning rate for a test set was evaluated.To address the issue of high average error in the BP neural network model(6.42%),we employed particle swarm optimization(PSO)to optimize its parameters,resulting in a reduced prediction error of 2.24%.The PSO-BP neural network model effectively predicts the relationship between process parameters and thinning rate.
关键词
双面渐进成形/减薄率/智能神经网络/粒子群算法/正交试验Key words
double-sided incremental forming/thinning rate/intelligent neural network/particle swarm optimization/orthogonal experiment引用本文复制引用
基金项目
吉林省科技发展计划项目重点研发(JJKH20220732KJ)
出版年
2024
NETL
NSTL
万方数据