铜铝激光焊接中表面粗糙度对焊接质量和缺陷形成影响的数据驱动分析

IF 2.5 4区 材料科学 Q3 MATERIALS SCIENCE, MULTIDISCIPLINARY
Mohammadhossein Norouzian, Mahdi Amne Elahi, Marcus Koch, Reza Mahin Zaeem, Slawomir Kedziora
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引用次数: 0

摘要

由于铜的高反射率,激光焊接汽车行业电池用铜铝合金面临着巨大挑战。在重叠配置焊接中,当激光辐射指向铜侧时,可能会出现结合力不足和机械强度低的问题。为了应对这些挑战,需要采用激光表面处理技术来增强铜材料的吸收特性并克服其反射特性。然而,提高表面粗糙度和热能输入超过临界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,产生裂纹和气孔等缺陷。研究中通常使用耗时且昂贵的金相分析来检测这些相位和缺陷。然而,要全面评估焊接质量并分辨表面结构的影响,必须采用更创新的方法来取代传统的横截面金相分析。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。它可以通过焊缝分类检测缺陷并识别焊缝质量。然而,由于照片特征的复杂性,该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出不同的焊缝类别,并识别出不稳定的焊缝。该项目的目标是使用监测模型来保证优化和高质量的焊缝系列生产。为此,对焊缝的参数和微观结构进行了深入研究,并利用 CNN 模型分析了 1310 张具有不同焊缝参数的焊缝照片的特征。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Data-driven analysis of surface roughness influence on weld quality and defect formation in laser welding of Cu–Al
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.
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来源期刊
CiteScore
4.70
自引率
8.30%
发文量
166
审稿时长
3 months
期刊介绍: The Journal of Materials: Design and Applications covers the usage and design of materials for application in an engineering context. The materials covered include metals, ceramics, and composites, as well as engineering polymers. "The Journal of Materials Design and Applications is dedicated to publishing papers of the highest quality, in a timely fashion, covering a variety of important areas in materials technology. The Journal''s publishers have a wealth of publishing expertise and ensure that authors are given exemplary service. Every attention is given to publishing the papers as quickly as possible. The Journal has an excellent international reputation, with a corresponding international Editorial Board from a large number of different materials areas and disciplines advising the Editor." Professor Bill Banks - University of Strathclyde, UK This journal is a member of the Committee on Publication Ethics (COPE).
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