The laser welding of Cu–Al alloys for battery applications in the automotive industry presents significant challenges due to the high reflectivity of copper. Inadequate bonding and low mechanical strength may occur when the laser radiation is directed toward the copper side in an overlap configuration welding. To tackle these challenges, a laser surface treatment technique is implemented to enhance the absorption characteristics and overcome the reflective nature of the copper material. However, elevating the surface roughness and heat-energy input over threshold values leads to heightened temperature and extreme weld. This phenomenon escalates the formation of detrimental intermetallic compounds (IMC), creating defects like cracks and porosity. Metallurgical analysis, which is time-consuming and expensive, is usually used in studies to detect these phases and defects. However, to comprehensively evaluate the weld quality and discern the impact of surface structure, adopting a more innovative approach that replaces conventional cross-sectional metallography is essential. This article proposes a model based on the image feature extraction of the welds to study the effect of the laser-based structure and the other laser parameters. It can detect defects and identify the weld quality by weld classification. However, due to the complexity of the photo features, the system requires image processing and a convolutional neural network (CNN). Results show that the predictive model based on trained data can detect different weld categories and recognize unstable welds. The project aims to use a monitoring model to guarantee optimized and high-quality weld series production. To achieve this, a deeper study of the parameters and the microstructure of the weld is utilized, and the CNN model analyzes the features of 1310 pieces of weld photos with different weld parameters.

中文翻译：

---

表面粗糙度对铜铝激光焊接焊接质量和缺陷形成影响的数据驱动分析

由于铜的高反射率，用于汽车行业电池应用的铜铝合金激光焊接面临着巨大的挑战。当激光辐射在搭接结构焊接中指向铜侧时，可能会出现粘合不足和机械强度低的情况。为了应对这些挑战，采用激光表面处理技术来增强吸收特性并克服铜材料的反射特性。然而，提高表面粗糙度和热能输入超过阈值会导致温度升高和极端焊接。这种现象会加剧有害金属间化合物 (IMC) 的形成，从而产生裂纹和孔隙等缺陷。冶金分析既耗时又昂贵，通常用于检测这些相和缺陷的研究。然而，为了全面评估焊缝质量并辨别表面结构的影响，采用更具创新性的方法来取代传统的横截面金相学是至关重要的。本文提出了一种基于焊缝图像特征提取的模型，以研究基于激光的结构和其他激光参数的影响。它可以检测缺陷并通过焊缝分类来识别焊缝质量。然而，由于照片特征的复杂性，系统需要图像处理和卷积神经网络（CNN）。结果表明，基于训练数据的预测模型可以检测不同的焊缝类别并识别不稳定的焊缝。该项目旨在使用监控模型来保证优化和高质量的焊接系列生产。为了实现这一目标，对焊缝的参数和微观结构进行了更深入的研究，CNN模型分析了1310张不同焊缝参数的焊缝照片的特征。