Prediction for Geometric Characteristics of Single Track of Deposition Layer and Surface Roughness in Thin Wire-Based Metal Additive Manufacturing Process  

细丝基金属增材制造沉积层单道几何特征及粗糙度的预测

作　　者：Liu Haitao Wang Lei Zhao Zhenlong Wang Linxin Tang Yongkai 刘海涛;王磊;赵振龙;王林鑫;汤永凯(西安工业大学机电工程学院,陕西西安710021;西安交通大学高端制造装备协同创新中心,陕西西安710054;国家增材制造创新中心,陕西西安710600)

机构地区：[1]School of Mechatronic Engineering,Xi'an Technological University,Xi'an 710021,China [2]Collaborative Innovation Center of High-End Manufacturing Equipment,Xi'an Jiaotong University,Xi'an 710054,China [3]National Innovation Institute of Additive Manufacturing,Xi'an 710600,China

出　　处：《稀有金属材料与工程》2024年第11期3026-3034,共9页Rare Metal Materials and Engineering

基　　金：173 Basic Strengthening Program;Xi'an Science and Technology Plan(21ZCZZHXJS-QCY6-0002)。

摘　　要：Machine learning prediction models for thin wire-based metal additive manufacturing(MAM)process were proposed,aiming at the complex relationship between the process parameters and the geometric characteristics of single track of the deposition layer and surface roughness.The effects of laser power,wire feeding speed and scanning speed on the width and height of the single track and surface roughness were experimentally studied.The results show that laser power has a significant impact on the width of the single track but little effect on the height.As the wire feeding speed increases,the width and height of the single track increase,especially the height.The faster the scanning speed,the smaller the width of the single track,while the height does not change much.Then,support vector regression(SVR)and artificial neural network(ANN)regression methods were employed to set up prediction models.The SVR and ANN regression models perform well in predicting the width,with a smaller root mean square error and a higher correlation coefficient R2.Compared with the ANN model,the SVR model performs better both in predicting geometric characteristics of single track and surface roughness.Multi-layer thin-walled parts were manufactured to verify the accuracy of the models.针对细丝基金属增材制造(MAM)工艺参数与沉积层单道几何特征和表面粗糙度之间的关系,提出了基于MAM工艺的机器学习预测模型。实验研究了激光功率、送丝速度和扫描速度对单道轨宽度、高度和表面粗糙度的影响规律。结果表明,激光功率对单道宽度影响显著,对高度影响不大。随着送丝速度的增加,单道的宽度和高度增加,特别是高度。扫描速度越快,单道宽度越小,而高度变化不大。采用支持向量回归(SVR)和人工神经网络回归(ANN)方法建立预测模型。SVR和ANN回归模型均具有较好的预测效果,均方根误差较小,相关系数R2较高。与ANN模型相比,SVR模型在预测单道几何特性和表面粗糙度方面都有更好的效果。在此基础上制造了多层薄壁零件,验证了模型的准确性。

关 键 词：thin wire-based metal additive manufacturing machine learning SVR ANN 

分 类 号：TG665[金属学及工艺—金属切削加工及机床] TP18[自动化与计算机技术—控制理论与控制工程] TG84[自动化与计算机技术—控制科学与工程]