Stress relaxation of high-density polyethylene is addressed both experimentally and theoretically. Two types of stress relaxation testing are carried out: uniaxial tensile testing at constant test specimen length and compression testing of a 3D structure producing inhomogeneous deformation fields and relaxation. A constitutive model for isotropic, semi-crystalline polymers is also proposed. The model has the ability to model stress relaxation at different time scales. The developed model was implemented as a user subroutine in Abaqus (UMAT). The implicit integration scheme including an algorithmic tangent modulus is described in detail. The material model is calibrated by use of the uniaxial tensile tests, and the model is then validated by simulating the compression tests of the 3D structure. The model is able to describe the uniaxial tension tests well, and the comparison between the simulations and experimental testing of the 3D structure shows very good agreement. © 2024, The Author(s).

The present study investigates ultrasonic metal welding to manufacture 10 mm2 copper (Cu) wire joints with different core diameters. The primary purpose of this study is to explore the influence of wire core diameter on the performance of ultrasonic welded joints. Wire core diameter is positively correlated with the peeling resistance of the joint. Superior mechanical properties of the joint are achieved with an increased diameter of the wire core. The peeling strength of the welded joint of two wires with a wire core diameter of 0.25 mm reaches 306.8 N. Examining the welding temperature and assessing the joint's porosity reveals a significant impact of temperature on porosity. However, relying solely on porosity as a criterion for judging the overall forming quality of joints may be insufficient. Scanning electron microscope and energy-dispersive X-ray elemental analysis revealed that certain wires underwent plastic deformation at elevated temperatures without attaining atomic bonding. Additionally, the welded joint exhibits a compact structure externally and a more relaxed structure internally. The upper side of the joint in contact with the briquette and the lower side in contact with the welding head exhibit minimal gaps, while numerous gaps are evident in the middle of the joint. Furthermore, upon examining the fracture morphology, two distinct failure modes are identified at the joint surface of the conductor. The first involves the fracture of the wire core with a completely separated joint surface, resulting in poor mechanical properties of the joint. The second mode entails the ductile fracture of the wire core at the joint surface, indicating good mechanical properties of the joint.

Ultrasonic wire welding is considered a method of choice for creating reliable interconnects in electronics industry including aerospace, batteries and electric vehicles. In this paper, ultrasonic welding tests between EVR252 copper wire and substrate are carried out. Novel pattern morphologies are machined on substrates to explore its influence on mechanical properties of welded joint. Patterns are divided into three different categories e.g., original surface, vertical and horizontal shapes. Cracks, microstructure strength and tensile properties of welded joint are studied and its joining mechanism is analysed. Compared with the reference substrate (S1), the welded joint performance of the longitudinal patterns (S2, S3, S4) has been improved, among which the longitudinal pattern (S4) has the most significant improvement (+ 15%). Likewise, the performance of transverse pattern (S5) welded joints is relatively poor (− 16%). The microstructural analysis using SEM has revealed predominant joint strength on Cu wire surface while maintaining rock-like and compact properties of S4 substrate. Upper side of wire-harness compactness is frequently observed due to vertical direction of patterns on substrate and also increases the strength of welded joint. Values of failure load, failure displacement and failure energy absorption were increased by 7.9%, 72% and 35% for S2, 6.1%, 75% and 42% for S3 and 15%, 87% and 113% for S4 compared to S1. Failure modes of welded joints are mainly characterized into: 1-poor ductility or rupture (no deformation) failure in vertical 3-line pattern joints 2-cylindrical deep holes failure in vertical 3-line zigzag pattern joints and 3-bulging effect failure in horizontal 3-line zigzag pattern joints. Point and line scans EDS measurement were performed to investigate weaker and stable trends of different locations in welded joints. In S4 substrate, 17.9% carbon content at the position of welded joint was investigated, leading to content of less oxides and fraction impurities. However, S1 weld zone contains 38.7% carbon content which can weaken welded joint and reduce durability. Graphical Abstract: (Figure presented.). © The Author(s) under exclusive licence to The Korean Institute of Metals and Materials 2024.

Ultrasonic metal welding machines are suitable for various complex applications (e.g., battery tabs) through unique mechanical design, special pressure application methods and high-precision welding. This work reports the weldability, forming quality and fractographic analysis of copper multilayered welded joints which were studied by SEM-EDS characterization, micro-hardness testing and tensile testing based on ultrasonic double roller welding (UDRW). Three groups of process parameters (A, B and C) were established to investigate the performance, production quality and welded joint surface interconnections. The tensile testing results of sample under parameter 3 in group A [S-P3(A)] indicate the maximum tensile strength of 69.859 N in T-peel test while the average tensile strength has increased by 58.525 N due to rise in welding time from 2 sec to 5 sec. The results analysis indicates that welding quality features in S-P3(A) joints under 4 bar, 100 mm/s, 45 % have been exploited. The over-welded zone was transformed into good-welded zone. The micro-cracks, fatigue stations and peeling texture in multilayers were reduced. It was found that when the welding energy was 10000 J then the tearing edges and interlayers cracks were minimized while keeping the other parameters constant. Moreover, when the amplitude increased up to 50 %, then numerous micro-cracks and micro-fissure stations were created, which leads to the occurrence of fracture in multi-layer welded joint. The EDS study investigated that the complex features are formed at the interface junction of sample 3 S3(A) in multilayer welds. The complex multilayer microstructures can induce and produce unique hardness properties for battery manufacturing. It leads to high quality and durable welds. Eventually, it is experimentally demonstrated that robust 40 layer welded joints can be obtained by the UDRW process. Data availability: The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request. © 2024 The Authors

Ultrasound welding technology is widely applied in the field of industrial manufacturing. In complex working conditions, various factors such as welding parameters, equipment conditions and operational techniques contribute to the formation of diverse and unpredictable line defects during the welding process. These defects exhibit characteristics such as varied shapes, random positions, and diverse types. Consequently, traditional defect surface detection methods face challenges in achieving efficient and accurate non-destructive testing. To achieve real-time detection of ultrasound welding defects efficiently, we have developed a lightweight network called the Lightweight Attention Detection Network (LAD-Net) based on an attention mechanism. Firstly, this work proposes a Deformable Convolution Feature Extraction Module (DCFE-Module) aimed at addressing the challenge of extracting features from welding defects characterized by variable shapes, random positions, and complex defect types. Additionally, to prevent the loss of critical defect features and enhance the network's capability for feature extraction and integration, this study designs a Lightweight Step Attention Mechanism Module (LSAM-Module) based on the proposed Step Attention Mechanism Convolution (SAM-Conv). Finally, by integrating the Efficient Multi-scale Attention (EMA) module and the Explicit Visual Center (EVC) module into the network, we address the issue of imbalance between global and local information processing, and promote the integration of key defect features. Qualitative and quantitative experimental results conducted on both ultrasound welding defect data and the publicly available NEU-DET dataset demonstrate that the proposed LAD-Net method achieves high performance. On our custom dataset, the F1 score and mAP@0.5 reached 0.954 and 94.2%, respectively. Furthermore, the method exhibits superior detection performance on the public dataset. © 2024 Elsevier B.V.

In the industrial production process, unsupervised visual inspection methods have obvious advantages over supervised visual inspection methods due to the scarcity of defect samples, annotation costs and the uncertainty of defect generation. Currently, unsupervised defect detection and localization methods have demonstrated significant improvements in detection accuracy to find numerous applications in industrial inspection. Nonetheless, the complexity of these methods limits their practical application. In this paper, we integrate the FastFlow model plugin as a probability distribution by introducing a simpler and lightweight CNN pre-trained backbone. Concurrently, various training strategies are employed to optimize the 2D Flow module within the Lightweight unsupervised flow model (LightFlow). Notably, the number of model parameters in the LightFlow model is only 1/4 of the original model size of the typical Vision Transformer (ViT) model CaiT. Thereby, this offers heightened training efficiency and speed. Therefore, extensive experimental results on three challenging anomaly detection datasets (MVTec AD, VisA, and BTAD) using various CNN backbones and multiple current state-of-the-art vision algorithms demonstrate the effectiveness of our approach. Specifically, the existing method can achieve 99.1% and 95.2% image-level AUROC (area under the receiver operating characteristic) in MVTec AD and VisA, respectively. IEEE

Defect inspection of the surface in ultrasonically welded wire terminations is an important inspection procedure to ensure welding quality. However, the detection task of ultrasonic welding defects based on deep learning still faces the challenges of low detection accuracy and slow inference speed. Therefore, to solve the above problems, we propose a fast and effective lightweight detection model based on You Only Look Once v8 (YOLOv8n), named LightYOLO. Specifically, first, to achieve fast feature extraction, a Two-Convolution module with FasterNet block and Efficient multi-scale attention (CTFE) structures is introduced in the backbone network. Secondly, Group-Shuffle Convolution (GSConv) is used to construct the feature fusion structure of the neck, which enhances the fusion efficiency of multi-level features. Finally, an auxiliary head training method is introduced to extract shallow details of the network. To verify the effectiveness of the proposed method, we constructed a surface defect data set of ultrasonic welding wire terminals and conducted a series of experiments. The results of experiments show that the precision of LightYOLO is 93.4%, which is 3.5% higher than YOLOv8n(89.9%). In addition, the model size was reduced to 1/2 of the baseline model. LightYOLO shows the potential for rapid detection on edge computing devices. The source code and dataset for our project is accessible at https://github.com/JianshuXu/LightYOLO. © 2024 The Authors

The ultrasonic metal welding technology is widely promoted as a new connection approach in the field of current energy vehicle wiring harness connection. In the present investigation, low-temperature mechanical properties of slotted and normal terminals were studied. The EVR 25 mm2 copper wires are selected for welding using ultrasonic wire harness welding with two different structures of T2 copper terminals. Then, a more stable joint structure under the same welding parameters is investigated through tensile tests at − 30 °C and 25 °C. The results showed that the ST joint has higher static mechanical properties than the NT joint at 25 °C and the peak load of the joint is increased. In addition, the results investigated that the performance and welded interface texture of ST joints is reliable than NT joints under 25 °C, the maximum joint load is increased by 12.93% under − 30 °C, the joint energy absorption is increased by 87.58%, and ST joint stability is better and safer in actual production applications. At the same welding parameters, the ST joints have less neck contraction at 25 °C and the ligamentous sockets are smaller and densely welded surfaces. The failures of ST joints and NT joints are investigated under the same welding parameters. The energy loss during the ST joint welding process is smaller and the welding effect is better and advantageous. The SEM findings showed that the failure of the ST joint and the NT joint is different and the tensile strength of the ST joint is greater under the same low-temperature conditions. © International Institute of Welding 2024.

Efficient detection of defects in riveted joints during the self-piercing riveting (SPR) process will help improve riveting quality. Due to the complexity of SPR defects under actual working conditions, it is difficult for traditional visual technology to detect the forming quality of SPR joints effectively. To detect SPR defects and improve the efficiency of SPR joint forming quality, we proposed a defect detection model based on a multi-attention mechanism, named Multiple Attention Self-Piercing Riveting Network (MA-SPRNet), for the detection of SPR defects. Specifically, to alleviate problems such as unclear object features in complex environments, a multi-level fusion enhancement network (MFEN) is constructed. It fuses features into each level and improves the fusion effect by adding more levels of features. In addition, to alleviate the information redundancy generated during the feature fusion process, the triple attention module (TRAM) and the efficient multi-scale attention module (EMAM) were introduced to enhance the attention of the network to SPR defective. These modules are designed to refine the attention of the network, ensuring a more targeted analysis of riveting features. In addition, the Wise Intersection over Union (WIoU) loss function is introduced, aiming to guide the network to characterize features within the region of interest and to enhance the accurate positioning of riveting defects by the network. Finally, to verify the performance of the MA-SPRNet, an SPR defect dataset was constructed, and a series of experiments based on this dataset were conducted. The detection mAP0.5 of MA-SPRNet was 82.83%. The results of experiments show that MA-SPRNet effectively realizes the detection of riveted joint defects. © 2024 Elsevier Ltd

In this paper, we present an investigation of ultrasonic welding performance for 25 mm2 copper wire and T2 copper plate across various welding parameters using orthogonal experimentation. The objective of this work was to explore the influence of operational parameters on the resulting welds. A comprehensive study of the mechanical properties and microstructure of the copper wire-to-copper plate joint was carried out using a series of sophisticated instruments. It includes a universal tensile machine, resistance measuring equipment, SEM, EDS and temperature measuring tool. This multifaceted approach enabled a detailed analysis of the joint's integral features and properties. This provides further insight into its performance and durability. Findings indicate that welding pressure has the most significant effect on welded joints. The optimal combination of parameters is achieved with the welding energy set at 6000 J, the welding amplitude at 85% and the welding pressure at 260 kPa. In different sets of welding parameters, joint strength is positively related to welding parameters and increases with increasing welding parameters. Joint resistance decreases with increasing joint tensile load and conductivity can be used to evaluate ultrasonic welding. It has been found that the development of the welded joint is achieved gradually in a direction moving inwards from the welding tool head, exhibiting a methodical forming process. Three distinct failure modes are observed in welded joints such as joint pullout, joint tearing and busbar breakage. The peak temperature during the welding process was recorded at 373 °C which indicates that the ultrasonic welding is a solid state connection. © The Author(s), under exclusive licence to Korean Society for Precision Engineering 2024.