摘要
为了改善矫直机工艺参数与板材矫直后板形的关系,并提升板材零件的矫直精度和矫直系统的智能化程度,提出基于Sine-SSA-BP算法来构建智能辊式矫直系统工艺参数选取模型。该算法通过Sine混沌映射初始化麻雀位置,并采用Sine-SSA算法优化BP神经网络的权值和阈值来构建模型。同时,使用MATLAB软件进行仿真,仿真结果与实验结果进行对比,研究结果表明基于Sine-SSA-BP算法建立的模型可有效提升矫直工艺参数预测精度,系统预测模型的均方根误差为0.246,平均绝对百分误差为8.1%,拟合度为0.983,且模型具有更好的鲁棒性,可以为生产中矫直工艺参数选取提供指导。
To improve the relationship between the straightening machine process parameters and the shape of the plates after straighte-ning,as well as to enhance the straightening accuracy of plate components and the intelligence level of the straightening system,a model for selecting process parameters for intelligent roller straightening systems based on the Sine-SSA-BP algorithm was proposed.The sparrow positions were initialized by using Sine chaotic mapping,and the model was constructed by optimizing the weights and thresholds of the BP neural network with the Sine-SSA algorithm.Simulations were conducted using MATLAB software and the simulation results were com-pared with experimental results.The research results indicate that the model established based on the Sine-SSA-BP algorithm can effec-tively improve the prediction accuracy of straightening process parameters.The root mean square error of the system prediction model is 0.246,the average absolute percentage error is 8.1%,and the goodness of fit is 0.983.Furthermore,the model exhibites better robust-ness,providing guidance for the selection of straightening process parameters in production.
作者
南无疆
王礼先
NAN Wu-jiang;WANG Li-xian(Institute of High-end Heavy Machinery and Equipment,Taiyuan University of Science and Technology,Taiyuan 030024,China;Qingdao Hongjie Intelligent Technology Co.,Ltd.,Qingdao 266111,China)
出处
《塑性工程学报》
CAS
CSCD
北大核心
2024年第12期81-89,共9页
Journal of Plasticity Engineering
关键词
智能辊式矫直系统
Sine混沌映射
麻雀算法
BP神经网络
工艺参数
intelligent roller straightening system
Sine chaotic mapping
sparrow algorithm
BP neural network
process parameters
