中国激光, 2020, 47 (8): 0802007, 网络出版: 2020-08-17
基于支持向量回归的定向能量沉积熔道尺寸预测 下载: 833次
Size Prediction of Directed Energy Deposited Cladding Tracks Based on Support Vector Regression
激光技术 定向能量沉积 熔道尺寸预测 支持向量回归 核函数 粒子群优化 laser technique directed energy deposition size prediction of cladding tracks support vector regression kernel function particle swarm optimization
摘要
定向能量沉积过程的智能化建模有助于解决沉积制造精度低的问题。以沉积工艺参数(激光功率、送粉速率、扫描速率、喷嘴高度)为输入、熔道宽度和高度为输出设计实验,建立基于高斯径向(RBF)核函数的支持向量回归(SVR)模型,采用该模型对熔道尺寸进行预测,并采用改进的粒子群优化(PSO)算法对RBF-SVR的超参数进行自动全局寻优。结果表明:RBF-SVR预测熔道宽度和高度的平均相对误差分别为4.58%和5.33%,小于反向传播(BP)神经网络预测的平均相对误差(6.72%和7.96%);所建模型适用于定向能量沉积熔道尺寸的预测,并能对沉积成型工艺参数的选取提供帮助。
Abstract
Intelligent modeling of directed energy deposition contributes in improving the manufacturing accuracy of deposition. We designed an experiment with the deposition process parameters (laser power, powder feeding rate, scanning speed, and distance of the nozzle) as inputs, and the width and height of the cladding tracks as ouputs. A support vector regression (SVR) model based on radial basis function (RBF) was established to predict the size of the cladding tracks. Then, an improved particle swarm optimization algorithm was used to determine suitable values for the hyperparameters of SVR. Results indicated that the average relative errors of RBF-SVR for predicting the width and height of the cladding tracks were 4.58% and 5.33%, respectively, which were better than the results obtained using the back propagation (BP) neural network where the average relative errors were 6.72% and 7.96%, respectively. The RBF-SVR is suitable for predicting the size of cladding tracks and provides a reference for selecting process parameters in a directed energy deposition.
姚望, 黄延禄, 杨永强. 基于支持向量回归的定向能量沉积熔道尺寸预测[J]. 中国激光, 2020, 47(8): 0802007. Yao Wang, Huang Yanlu, Yang Yongqiang. Size Prediction of Directed Energy Deposited Cladding Tracks Based on Support Vector Regression[J]. Chinese Journal of Lasers, 2020, 47(8): 0802007.