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.

Plastic flow is an essential component of a material model describing material behaviors under external loads. Conventionally, a standard uniaxial tensile test is performed to determine the flow curve of sheet metals. This study implements two advanced methods: data-driven and inverse FE methods to identify the plastic flow of a VDA239-100 CR4 sheet using a notch-tensile sample. In the former method, a simulated database is generated to train a neural network, which is able to predict the plastic flow of a sheet metal using the experimental data. The latter adopts an optimization algorithm to minimize the difference between the strain distribution observed during experiment and that of simulations. The derived results are compared with the results obtained from a standard uniaxial tensile test. The benefits of each calibration method are discussed based on the comparisons. 

With the increased focus on smart manufacturing and Industry 4.0, the use of simulations for the creation of cyber-physical manufacturing systems is increasing. The sheet metal forming manufacturing process, commonly used for production of automotive body components, is one of the processes that currently benefits from the use of simulations without exploiting them in a cyber-physical system setup. This study set out to initially identify the key controllable and uncontrollable parameters of the sheet metal forming manufacturing process for the design of an intelligent quality controller. Subsequently, the study investigates the possibility of using data points from a stochastic numerical analysis as training data for an Artificial Neural Network. The stochastic numerical model used is based on the existing Finite Element simulation standard at Volvo Cars to allow for a seamless integration of the methodology into the standard workflow of CAE departments. Lastly, the study will present a validation of the trained Artificial Neural Network using the Volvo XC90 inner front door component as an industrial demonstrator.

The automotive industry is currently facing increasing sustainability demands in order to reduce the environmental impact of their businesses and products. As a part of these demands, reduced amount of scrapped parts in current production is favourable since it contributes to both an increased productivity as well as improved environmental sustainability. Furthermore, in the near future, more sustainable sheet metals will be introduced in the production which could have a larger variation in properties which could increase the number of scrapped parts. These new demands and sheet materials have been the starting point for the study presented in this paper. It is based on results from a Volvo Cars stamping plant for a part in production that has experienced production disturbances. The information from the press shop stated which combinations of sheet metal coatings and lubricants that gave a robust production and which combinations that generated an unacceptable number of scrapped parts. These different tribological systems have then been simulated using the AutoForm R12 Sigma software with TriboForm models of the used tribological systems in the press shop. The simulations are also using the Cold Forming with Temperature Effects functionality in AutoForm R12 which makes it possible to also include the effects of temperature increase in the stamping die during the production of the part.

The hole expansion ratio (HER) observed in a standardized hole expansion test (HET) is commonly used to determine the edge fracture of steel sheets. A large variation of the measured HER restricts the practical application of the method. This study presents a systematic investigation on uncertainties in the HER of DP800 sheet material, including the hole-edge quality, pre-strain due to the hole-punching process, the friction coefficient, and the determination of fracture. An artificial neural network was trained to develop a surrogate model using a database gained from a thousand finite element simulations of the HET. Monte-Carlo simulations were performed using the trained surrogate model to characterize the distribution of the HER. Sensitivity analysis via Sobol's indices is calculated to determine the influence of the input variables on the output. It is found that the pre-strain and pre-damage generated during the hole punching process in the shear-affected zone dominate the variation of the HER. Discussions on reducing the output's variation are detailed. In general, these findings provide valuable insights for the determination of HER as well as the edge crack behavior of the investigated DP800 steel sheet.

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).

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