Yin Yisheng, Zhang Chengrui, Zhu Tieshuang
Key Laboratory of High Efficiency and Clean Mechanical Manufacture (Ministry of Education), School of Mechanical Engineering, Shandong University, Jinan 250061, China.
National Demonstration Center for Experimental Mechanical Engineering Education, Shandong University, Jinan 250061, China.
Materials (Basel). 2021 Oct 12;14(20):5984. doi: 10.3390/ma14205984.
This paper builds an infinity shaped ("∞"-shaped) laser scanning welding test platform based on a self-developed motion controller and galvanometer scanner control gateway, takes the autogenous bead-on-plate welding of 304SS with 3 mm thick specimens as the experimental objects, designs the experimental parameters by the Latin hypercube sampling method for obtaining different penetration depth welded joints, and presents a methodology based on the neuroevolution of augmenting topologies for predicting the penetration depth of "∞"-shaped laser scanning welding. Laser power, welding speed, scanning frequency, and scanning amplitude are set as the input parameters of the model, and welding depth (WD) as the output parameter of the model. The model can accurately reflect the nonlinear relationship between the main welding parameters and WD by validation. Moreover, the normalized root mean square error (NRMSE) of the welding depth is about 6.2%. On the whole, the proposed methodology and model can be employed for guiding the actual work in the main process parameters' preliminary selection and lay the foundation for the study of penetration morphology control of "∞"-shaped laser scanning welding.
本文基于自主研发的运动控制器和振镜扫描控制网关搭建了一个∞形激光扫描焊接试验平台，以3mm厚的304不锈钢平板堆焊为实验对象，采用拉丁超立方抽样法设计实验参数以获得不同熔深的焊接接头，并提出一种基于拓扑增强神经进化的方法来预测∞形激光扫描焊接的熔深。将激光功率、焊接速度、扫描频率和扫描幅度设置为模型的输入参数，焊接深度（WD）作为模型的输出参数。经验证，该模型能准确反映主要焊接参数与焊接深度之间的非线性关系。此外，焊接深度的归一化均方根误差（NRMSE）约为6.2%。总体而言，所提出的方法和模型可用于指导实际工作中主要工艺参数的初步选择，并为∞形激光扫描焊接熔深形态控制的研究奠定基础。
