Journal of Advanced Joining Processes最新文献_第6页

Thermo mechanical Finite Element Analysis of the plasma Wire Arc Additive Manufacturing process in DEFORM® 13 DEFORM®13中等离子丝弧增材制造过程的热机械有限元分析

IF 3.8 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY

Journal of Advanced Joining Processes

Pub Date : 2025-06-16 DOI: 10.1016/j.jajp.2025.100321

Marcel Czipin, Alexander Wenda, Karin Hartl, Emre Akalin, Martin Stockinger

This study investigates the potential of Finite Element Analysis in DEFORM® to predict the thermal history, deformation, residual stress state and grain growth in the Wire Arc Additive Manufacturing processes of Ti–6Al–4V. The temperature dependent material model for Ti–6Al–4V was extended and adapted to improve the representation of contact boundary conditions within DEFORM®, focusing on Additive Manufacturing. A single layer quad-mesh approach was employed alongside dummy heat sources to simulate the process by accurate layer wise activation within the arc welding module. The model utilized a normalized double-ellipsoid heat source and introduced a power adaptation strategy to account for differences in volumetric deposition. The extracted thermal history showed very good agreement to corresponding thermocouple measurements. The accuracy of the resulting deformation state was validated using a 3D scan, while the predicted grain size distribution was compared against an as-built micrograph. The simulation showed good overall accuracy, though limitations were noted in the grain size model, which was inadequate in predicting the more complex texture of the mixed

α / β

-microstructure typical for Ti–6Al–4V. Seven heat treatment strategies were evaluated to address mechanical anisotropy. Solution annealing followed by water quenching and subsequent low temperature aging was found to be most effective.

本研究探讨了DEFORM®有限元分析在预测Ti-6Al-4V电弧增材制造过程中的热历史、变形、残余应力状态和晶粒生长方面的潜力。Ti-6Al-4V的温度相关材料模型进行了扩展和调整，以改善DEFORM®中接触边界条件的表示，重点是增材制造。采用单层四网格方法与虚拟热源一起，通过在弧焊模块内精确分层激活来模拟过程。该模型采用归一化双椭球热源，并引入功率自适应策略来考虑体积沉积的差异。提取的热历史与相应的热电偶测量结果非常吻合。通过3D扫描验证了变形状态的准确性，同时将预测的晶粒尺寸分布与构建的显微照片进行了比较。尽管晶粒尺寸模型存在一定的局限性，不足以预测Ti-6Al-4V混合α/β-组织的复杂织构，但模拟结果总体精度较高。评估了7种热处理策略以解决机械各向异性问题。溶液退火后再进行水淬和低温时效是最有效的。

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引用次数: 0

Prediction of weld quality in laser welding of hardmetal and steel using high-speed imaging and machine learning methods 基于高速成像和机器学习方法的硬质合金和钢激光焊接焊缝质量预测

IF 3.8 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY

Journal of Advanced Joining Processes

Pub Date : 2025-06-01 DOI: 10.1016/j.jajp.2025.100318

Mohammadhossein Norouzian , Mahan Khakpour , Marko Orosnjak , Atal Anil Kumar , Slawomir Kedziora

Laser welding of steel and hardmetal presents significant challenges due to their differing material properties. Improper laser welding parameters can result in unstable joints, ultimately leading to reduced mechanical strength of the weld. Therefore, defining an optimal process window is critical to ensuring weld quality. In addition, a continuous process monitoring method like High-Speed Imaging (HSI) is essential in real industrial applications to maintain stability and detect potential defects. Understanding plume dynamics helps identify the most important features of weld quality, but it also provides deeper insight into operational parameters that discriminate different weld types. Analysis of individual image plume frames from HSI reveals distinct statistical features that are identified as unique to each welding condition. Performing systematic feature selection using plume morphology, spatter generation and weld quality, we achieved>95 % leveraging Machine Learning (ML) classifiers. Particularly, Gradient Boosting Classifier (GBC), Linear Discriminant Analysis (LDA), Multinomial Logistic Regression (MNL-LR), Support Vector Machine (SVM), and Random Forest (RF), where the RF obtained >99 % classification accuracy of weld quality. The RF was then used in performing Recursive Feature Elimination (RFE), and with the robustness analysis, we managed to reduce the number of features from forty-nine to nine features while maintaining satisfactory performance (Accuracy = 0.981, F1-score = 0.961, AUROC = 0.997). The position of the weld plume, plume eccentricity and plume width are the most essential features that lead to the improvement of node purity and classification accuracy.

钢和硬质合金的激光焊接由于其不同的材料特性而面临着巨大的挑战。不当的激光焊接参数会导致接头不稳定，最终导致焊缝机械强度降低。因此，确定最佳工艺窗口对于确保焊接质量至关重要。此外，在实际工业应用中，像高速成像（HSI）这样的连续过程监控方法对于保持稳定性和检测潜在缺陷至关重要。了解羽流动力学有助于确定焊接质量的最重要特征，同时也有助于更深入地了解区分不同焊接类型的操作参数。对HSI中单个图像羽流帧的分析揭示了不同的统计特征，这些特征被认为是每个焊接条件所独有的。利用羽流形态、飞溅产生和焊接质量进行系统的特征选择，我们利用机器学习（ML）分类器实现了95%的目标。特别是梯度增强分类器（GBC）、线性判别分析（LDA）、多项逻辑回归（MNL-LR）、支持向量机（SVM）和随机森林（RF），其中RF对焊缝质量的分类准确率达到99%。然后使用RF进行递归特征消除（RFE），通过鲁棒性分析，我们成功地将特征数量从49个减少到9个，同时保持令人满意的性能（精确度= 0.981,F1-score = 0.961, AUROC = 0.997）。焊缝羽流位置、羽流偏心率和羽流宽度是提高节点纯度和分类精度的最基本特征。

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引用次数: 0

Corrigendum to “Exploring Wire-Arc Additive Manufactured Rivets for Joining Hybrid Electrical Busbars” “探索用于连接混合电母线的线弧添加剂制造铆钉”的勘误表

IF 3.8 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY

Journal of Advanced Joining Processes

Pub Date : 2025-06-01 DOI: 10.1016/j.jajp.2025.100291

Pedro M.S. Rosado , Rui F.V. Sampaio , João P.M. Pragana , Nuno M.S. Pereira , Ivo M.F. Bragança , Carlos M.A. Silva , Paulo A.F. Martins

引用次数: 0

Enhancement of mechanical properties of thermite heat assisted friction stir welded aluminium bronze alloy (C95300) by eliminating tunnel defect 消除隧道缺陷提高铝热辅助搅拌摩擦焊铝青铜合金（C95300）的力学性能

IF 3.8 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY

Journal of Advanced Joining Processes

Pub Date : 2025-06-01 DOI: 10.1016/j.jajp.2025.100317

Tamil Prabakaran S , Sudha J , Siva S , Balamurali Duraivel , Vivekananda A S

Thermite Heat-Assisted Friction Stir Welding (THAFSW) is recognized as an efficient welding method for joining aluminium bronze (AB) alloys. The mechanical and metallurgical characteristics of the welded joints were analyzed and compared with those fabricated using the conventional friction stir welding (CFSW) technique. Tensile strength and hardness assessments of the welded specimens were conducted at ambient temperature. The findings revealed that the THAFSW joints exhibited superior mechanical properties, with tensile strength and elongation improving by 11 % and 25 %, respectively, compared to joints produced through the conventional approach. The strengthening mechanism of the welded joints was examined based on images captured through macroscopy, optical microscopy, scanning electron microscopy (SEM), and transmission electron microscopy (TEM). The THAFSW process effectively eliminated tunnel defects and facilitated the development of a uniform α-phase microstructure, which contributed to enhanced mechanical performance.

铝热剂热辅助搅拌摩擦焊（THAFSW）被认为是连接铝青铜（AB）合金的一种有效的焊接方法。对焊接接头的力学和冶金特性进行了分析，并与传统搅拌摩擦焊（CFSW）工艺进行了比较。焊接试样的抗拉强度和硬度评估在室温下进行。研究结果表明，与传统方法生产的接头相比，THAFSW接头具有优越的机械性能，抗拉强度和伸长率分别提高了11%和25%。通过宏观显微镜、光学显微镜、扫描电镜和透射电镜对焊接接头的强化机理进行了研究。THAFSW工艺有效地消除了隧道缺陷，促进了α-相组织的均匀发展，从而提高了材料的力学性能。

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引用次数: 0

A comprehensive review on the integration of artificial intelligence in friction stir welding for monitoring, modelling, and process optimization 人工智能在搅拌摩擦焊监测、建模和工艺优化中的应用综述

IF 3.8 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY

Journal of Advanced Joining Processes

Pub Date : 2025-06-01 DOI: 10.1016/j.jajp.2025.100316

Mostafa Akbari , Ezatollah Hassanzadeh , Yaghuob Dadgar Asl , Amirhossein Moghanian

Recent advancements in artificial intelligence (AI) technologies have expanded their applications across various industrial environments, particularly in the field of Friction Stir Welding (FSW), a relatively modern manufacturing technique. AI techniques are primarily employed for modeling, monitoring, optimization, and management of complex systems influenced by multiple parameters within industrial processes. This study systematically reviews and evaluates commonly utilized AI techniques in FSW, highlighting their effectiveness, accuracy, and comparative advantages. The discussion is organized into three distinct sections, each focusing on the critical roles of AI and machine learning (ML) in FSW. The first section addresses process prediction, showcasing how AI techniques predict welding outcomes using historical data and process parameters, which enhances decision-making prior to actual implementation. The second section examines process control, emphasizing how AI systems enable real-time monitoring and adaptive control of the welding process. This functionality allows for immediate parameter adjustments, thus significantly improving weld consistency and quality by minimizing defects. Lastly, the third section pertains to the optimization of FSW parameters, illustrating how AI-driven algorithms analyze complex interactions among multiple variables to determine the most effective process settings. By adopting this structured approach, the review articulates the comprehensive benefits of integrating AI into the friction stir welding process, ultimately contributing to enhanced joint quality and improved operational efficiency.

人工智能（AI）技术的最新进展已经扩展了其在各种工业环境中的应用，特别是在搅拌摩擦焊接（FSW）领域，这是一种相对现代的制造技术。人工智能技术主要用于工业过程中受多个参数影响的复杂系统的建模、监控、优化和管理。本研究系统地回顾和评估了FSW中常用的人工智能技术，强调了它们的有效性、准确性和比较优势。讨论分为三个不同的部分，每个部分都侧重于人工智能和机器学习（ML）在FSW中的关键作用。第一部分介绍了工艺预测，展示了人工智能技术如何使用历史数据和工艺参数预测焊接结果，从而增强了实际实施之前的决策。第二部分探讨过程控制，强调人工智能系统如何实现焊接过程的实时监控和自适应控制。该功能允许立即调整参数，从而通过最小化缺陷显着提高焊接一致性和质量。最后，第三部分涉及FSW参数的优化，说明了人工智能驱动的算法如何分析多个变量之间的复杂相互作用，以确定最有效的工艺设置。通过采用这种结构化方法，该综述阐明了将人工智能集成到搅拌摩擦焊接过程中的综合效益，最终有助于提高接头质量和提高操作效率。

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引用次数: 0

Regulating intermetallic compound growth and bridging in SnAg solder under electromigration stress through Ni addition and sn crystallographic orientation-grain size 通过添加Ni和sn晶粒取向调节电迁移应力下SnAg焊料中金属间化合物的生长和桥接

IF 3.8 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY

Journal of Advanced Joining Processes

Pub Date : 2025-06-01 DOI: 10.1016/j.jajp.2025.100320

Dinh-Phuc Tran, Ya-Ting Xiao, Mai-Phuong La, Shi-Chi Yang, Chih Chen

As semiconductor devices scale down, electromigration (EM) failures in interconnects become more severe, requiring effective under-bump metallization (UBM) strategies. Herein, we investigated EM failures correlated with the development of intermetallic compounds (IMCs) in two UBM structures (Cu/SnAg/Cu and Cu/SnAg/Ni/Cu). Results showed that the Ni layer resulted in thinner IMCs. It acted as a diffusion barrier, which effectively suppressed IMC growth. We also found that the IMC formation in both solder structures was significantly influenced by the Sn grain orientation. A lower c-axis angle of beta-Sn to EM flow associated with faster IMC formation. Sn grain size also impacted IMC growth, with larger grains resulting in slower IMC formation as a result of the reduced grain boundary density. In addition, the IMC bridging phenomenon was observed in the joints. It was found that IMC bridging occurred less frequently in Ni UBM solder joints compared to Cu/SnAg/Cu counterparts. Such a difference could be attributed to the lower solubility of Ni in Sn compared to Cu. The Ni served as a barrier, which limited the Ni dissolution into the Sn solder. It suppressed the IMC formation/growth, thereby reducing the IMC bridging probability in the Cu/SnAg/Ni/Cu joints.

随着半导体器件的小型化，互连中的电迁移（EM）故障变得更加严重，需要有效的碰撞下金属化（UBM）策略。在此，我们研究了两种UBM结构（Cu/SnAg/Cu和Cu/SnAg/Ni/Cu）中与金属间化合物（IMCs）发展相关的EM失效。结果表明，Ni层导致imc变薄。它起到了扩散屏障的作用，有效地抑制了IMC的生长。我们还发现，两种焊料结构的IMC形成都受到Sn晶粒取向的显著影响。β - sn - EM流的c轴角越小，IMC形成越快。Sn晶粒尺寸也会影响IMC的生长，晶粒越大，由于晶界密度降低，导致IMC形成较慢。此外，在关节中观察到IMC桥接现象。与Cu/SnAg/Cu焊点相比，IMC桥接在Ni UBM焊点中发生的频率更低。这种差异可能是由于Ni在Sn中的溶解度比Cu低。镍作为阻挡层，限制了镍在锡焊料中的溶解。它抑制了IMC的形成/生长，从而降低了Cu/SnAg/Ni/Cu接头中IMC的桥接概率。

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引用次数: 0

Prediction of shrink lines in powder bed fusion of metals using a laser beam by means of a finite element simulation approach 用有限元模拟方法预测金属粉末床熔合过程中的收缩线

IF 3.8 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY

Journal of Advanced Joining Processes

Pub Date : 2025-06-01 DOI: 10.1016/j.jajp.2025.100315

Dominik Rauner, Niklas Eilers, Hannes Panzer, Lukas Frei, Michael F. Zaeh

Powder bed fusion of metals using a laser beam (PBF-LB/M) enables the near-net-shape fabrication of thin-walled parts with a high geometric complexity, thus often featuring structural transitions. Due to high temperature gradients during manufacturing, these structural transitions are subject to localized deformations, which manifest themselves in a shrink line, which is reducing the part lifetime and the dimensional accuracy. In current PBF-LB/M process simulations, however, the shrink line formation cannot be predicted on a physical basis yet. In this study, a finite element approach for efficiently predicting the shrink line formation is presented. The three-stage approach begins with a numerical geometry analysis, which is used to define an appropriate finite element mesh for the subsequent analyses. This is followed by the prediction of the geometry-dependent overheating during the PBF-LB/M process. Using these overheating results and an experimentally calibrated overheating-shrink-line relation, the shrink lines are modeled in a mechanical analysis considering the physics-based effects. The simulation approach was verified on an academic specimen design and was experimentally validated on two parts with different degrees of geometric complexity. The derived overheating-shrink-line relation provided a valid strategy for predicting the resulting shrink line depth. Applying the approach, the deviation between the measurements and the shrink line simulation was determined to be lower than 41 µm. Furthermore, the prediction quality of the dimensional accuracy was increased by 6.9 % for a topology-optimized part. For the approach, necessary extensions were derived to allow for simulating an asymmetric shrink line formation in the future.

使用激光束（PBF-LB/M）进行金属粉末床熔合，可以实现具有高几何复杂性的薄壁零件的近净形状制造，因此通常具有结构转变。由于制造过程中的高温梯度，这些结构转变受到局部变形的影响，这些局部变形表现为收缩线，从而降低了零件的使用寿命和尺寸精度。然而，在目前的PBF-LB/M工艺模拟中，还不能在物理基础上预测收缩线的形成。本文提出了一种有效预测收缩线形成的有限元方法。三阶段方法从数值几何分析开始，用于为后续分析定义适当的有限元网格。接下来是预测PBF-LB/M过程中与几何相关的过热。利用这些过热结果和实验校准的过热-收缩线关系，在考虑物理效应的力学分析中对收缩线进行建模。仿真方法在一个理论试件设计上得到了验证，并在两个几何复杂度不同的零件上进行了实验验证。导出的过热-收缩线关系为预测收缩线深度提供了一种有效的策略。应用该方法，测量值与收缩线模拟值之间的偏差小于41µm。拓扑优化后的零件尺寸精度预测质量提高了6.9%。对于该方法，推导了必要的扩展，以便将来模拟不对称收缩线形成。

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引用次数: 0

Data-driven parameter optimization for bead geometry in wire arc additive manufacturing of 17-4 PH stainless steel 17-4 PH不锈钢丝弧增材制造中焊头几何参数的数据驱动优化

IF 3.8 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY

Journal of Advanced Joining Processes

Pub Date : 2025-05-30 DOI: 10.1016/j.jajp.2025.100319

Muhammad Irfan , Yun-Fei Fu , Shalini Singh , Sajid Ullah Butt , Abul Fazal Arif , Osezua Ibhadode , Ahmed Qureshi

Due to its high strength, corrosion resistance, and toughness, 17-4 Precipitation Hardening (PH) stainless steel is widely used in aerospace, petrochemical, and marine industries. Additive manufacturing (AM) technologies enable the fabrication of complex and/or customized components while offering superior material efficiency and shorter lead times. Because of its high deposition rate, Wire Arc Additive Manufacturing (WAAM) can produce large metal structures. However, consistent bead profiles remain challenging because the process is highly sensitive to variations in thermal input and deposition conditions. Achieving uniform bead geometry during additive manufacturing is essential to avoid defects such as humming, spattering, and distortion, which can compromise the structural integrity of 3D components.

To achieve a uniform bead profile in WAAM, in this study, a full-factorial design of experiments is implemented to optimize the process parameters such as Wire Feed Rate (WFR), Torch Travel Speed (TTS), and Gas Flow Rate (GFR) for 17-4PH stainless steel. A backpropagation neural network (BPNN) is trained to model a non-linear relationship between these process parameters and bead geometry. Moreover, a genetic algorithm (GA) optimizes for bead uniformity and deposition efficiency. With a Pearson Correlation Coefficient (PCC) of 0.85, the optimized parameters exhibited significantly improved uniformity and higher deposition efficiency.

17-4沉淀硬化（PH）不锈钢由于其高强度、耐腐蚀性和韧性，广泛应用于航空航天、石油化工和海洋工业。增材制造（AM）技术能够制造复杂和/或定制组件，同时提供卓越的材料效率和更短的交货时间。电弧增材制造（WAAM）由于其沉积速率高，可以生产大型金属结构。然而，由于该工艺对热输入和沉积条件的变化高度敏感，因此一致的焊头轮廓仍然具有挑战性。在增材制造过程中，实现均匀的焊头几何形状对于避免嗡嗡声、飞溅和变形等缺陷至关重要，这些缺陷可能会损害3D组件的结构完整性。为了在WAAM中获得均匀的头形，本研究对17-4PH不锈钢进行了全因子实验设计，以优化送丝速度（WFR）、火炬行进速度（TTS）和气体流量（GFR）等工艺参数。训练反向传播神经网络（BPNN）来模拟这些工艺参数与焊头几何形状之间的非线性关系。此外，采用遗传算法优化了焊头均匀性和沉积效率。结果表明，优化后的沉积参数均匀性显著提高，沉积效率显著提高，Pearson相关系数为0.85。

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引用次数: 0

Robotized hardfacing on high-strength steels: determination of impact properties with different heat inputs 高强度钢的自动化堆焊：不同热输入下冲击性能的测定

IF 3.8 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY

Journal of Advanced Joining Processes

Pub Date : 2025-05-19 DOI: 10.1016/j.jajp.2025.100314

Ákos Meilinger, Gábor Terdik

The use of high-strength steels as a substrate for hardfacing is becoming increasingly common in the industry (e.g., for demolition shears). In the case of joint welding, the weldability of these steels is limited because welding heat has significant affect to the base material. Both softening and hardening can occur in the different sub-zones of heat-affected zone, leading to changes in impact properties. For demolition shears, impact stresses are the most critical loads. Heat input can alter the microstructure of the heat-affected zone, potentially reducing the load-bearing capacity due to the penetration depth of the hardface layer or the buffer layer. Robotization of hardfacing creates equal layers with high precision, which helps the precise comparison. In this study, S690QL and S960QL substrates were investigated under different heat inputs, and the impact properties of these specimens were tested. Instrumented impact test results were analyzed and supplemented with surface fractography. The impact resistance of the S690QL substrate decreases with higher heat input and penetration depth. In contrast, S960QL exhibits different behavior: the use of lowest heat input causes a 226 % increase in impact energy compared with the base material. The underlying reasons for this were identified through force-time curve analysis, where the positive effect of the heat-affected zone is determined. Additionally, the maximum impact forces display different behavior for the two materials: S960QL shows higher impact force except in case of highest heat input, where the S690QL shows better force. These findings are valuable for selecting the appropriate substrate and hardfacing technology for this application and its specific loading conditions.

使用高强度钢作为堆焊的基材在工业中变得越来越普遍（例如，用于拆卸剪）。在接头焊接的情况下，由于焊接热对母材有显著影响，这些钢的可焊性受到限制。在热影响区的不同亚区均可发生软化和硬化，从而导致冲击性能的变化。对于拆剪来说，冲击应力是最关键的载荷。热输入可以改变热影响区的微观结构，由于硬面层或缓冲层的渗透深度，可能会降低承载能力。堆焊的自动化制造出高精度的等量层，这有助于精确的比较。在本研究中，研究了S690QL和S960QL基板在不同热输入下的冲击性能，并测试了这些试样的冲击性能。对仪器冲击试验结果进行分析，并辅以表面断口分析。S690QL基板的抗冲击性随热输入和穿透深度的增加而降低。相比之下，S960QL表现出不同的行为：与基材相比，使用最低的热输入导致冲击能量增加226%。通过力-时间曲线分析确定了这种情况的根本原因，其中确定了热影响区的积极影响。此外，两种材料的最大冲击力表现出不同的行为：S960QL显示出更高的冲击力，但在最高热量输入的情况下，S690QL显示出更好的冲击力。这些发现是有价值的选择适当的基材和堆焊技术，为这种应用和其特定的加载条件。

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引用次数: 0

The Influence of temperature on the microstructure and properties of Cu/Al tube joints in magnetic pulse-assisted semi-solid brazing 温度对磁脉冲辅助半固态钎焊Cu/Al管接头组织和性能的影响

IF 3.8 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY

Journal of Advanced Joining Processes

Pub Date : 2025-05-16 DOI: 10.1016/j.jajp.2025.100313

Zhenglei Rui , Shangyu Huang , Huajun Wang , Zhenghua Meng

This study addresses the technical challenge of copper/aluminum (Cu/Al) tube joining through the innovative application of magnetic pulse-assisted semi-solid brazing (MPASSB) technology. Through an integrated approach combining finite element simulation with microstructural characterization, this study systematically investigates how brazing temperature (390–440 °C) influences the microstructure and mechanical properties of Cu/Al tube joints. Notably, a novel finite element method-smoothed particle hydrodynamics (FEM-SPH) coupling model has been developed. This model enables precise simulation of fluid-solid interactions between tubes and filler metal during the brazing process, providing fresh insights into oxide layer removal mechanisms. The research reveals that brazing temperature serves as the critical parameter governing the elemental diffusion and microstructural evolution in the joint. As the temperature increases from 390 °C to 440 °C, the viscosity coefficient of the filler metal decreases significantly from 41.6Pa·s to 1.798Pa·s, resulting in enhanced fluidity that promotes interfacial interactions between the tubes and filler metal and effectively removes surface oxide films, thus improving joint quality. However, excessive temperature intensifies the filler metal ejection, increasing the risk of filler metal deficiency at the top of the joint. Mechanical testing demonstrates that joints brazed at 440 °C achieve optimal shear strength of 81.1 MPa, with fracture occurring at the copper-side (Cu-side) interface between the Al_4.2Cu_3.2Zn_0.7 intermetallic phase and the diffusion layer. This work establishes fundamental theoretical guidance for optimizing MPASSB process parameters and facilitates the efficient joining of Cu/Al tubes.

本研究通过磁脉冲辅助半固态钎焊（MPASSB）技术的创新应用，解决了铜/铝（Cu/Al）管连接的技术挑战。本研究采用有限元模拟与微观组织表征相结合的方法，系统研究了钎焊温度（390 ~ 440℃）对Cu/Al管接头微观组织和力学性能的影响。值得注意的是，本文提出了一种新的有限元方法-光滑颗粒流体动力学（FEM-SPH）耦合模型。该模型能够精确模拟钎焊过程中管道和填充金属之间的流固相互作用，为氧化层去除机制提供新的见解。研究表明，钎焊温度是控制接头中元素扩散和组织演变的关键参数。随着温度从390℃升高到440℃，填料金属的粘度系数从41.6Pa·s显著降低到1.798Pa·s，流动性增强，促进了管材与填料金属之间的界面相互作用，有效去除表面氧化膜，提高了接头质量。然而，过高的温度加剧了填充金属的喷出，增加了接头顶部填充金属不足的风险。力学试验结果表明，440℃钎焊接头抗剪强度为81.1 MPa，断裂发生在Al4.2Cu3.2Zn0.7金属间相与扩散层之间的铜侧（cu侧）界面。该工作为优化MPASSB工艺参数提供了基础理论指导，促进了Cu/Al管的高效连接。

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引用次数: 0