Ultrasonic Metal Welding Research Articles

Abnormal dislocation substructure in the ultrasonically welded joints of Cu single crystals

Ultrasonic metal welding is a rapid bonding technology that combines ultrasonic softening and high-frequency friction. However, the massive dislocation proliferation in high-strain-rate deformation is contradictory to rapid dynamic recovery induced by the ultrasonic vibration. To investigate the contribution of ultrasonic to the joint microstructure, the Cu single crystals with [001], [101], [111] orientations were selected to trace the abnormal deformation microstructure. It was indicated that the ultrasonic excitation broke the correlation between geometrically necessary boundaries and grain orientation, which resulted in a similar micro-hardness in the range of 112.5–116.0 HV near the interface in three single crystal joints. An analytical model of interfacial friction deformation was proposed to prove the abnormal enlargement of dislocation cells under ultrasonic excitation.

Improving process monitoring of ultrasonic metal welding using classical machine learning methods and process-informed time series evaluation

Ultrasonic metal welding (USMW) is an industrially applied joining technology that is highly complex since the weld quality is influenced by numerous factors. The relationships between these factors and the quality remain partly unavailable resulting in a need for improvement in process monitoring and quality management. This work focuses on exploring the relationships between tensile shear strength (TSS) of Cu-sheet welds and process curves from welding machine and additional vibration sensors at sonotrode and anvil. Discovered relationships would enable an improved process monitoring, when valid for a broad parameter range. To ensure the latter, examinations are carried out on a central composite design of experiments (DoE) data set. For the whole data set as well as for single data points, the process curves are examined in detail comprising visualizations and discussions of revealed trends. These trends are related to process physics to clarify their relevance for the TSS. Based on physical process knowledge, more than 700 features are extracted from the curves. The extraction approach is not limited to the present setup and enables a quantitative evaluation of the relation between TSS and process curves. Most important features are derived from the generator power and the anvil vibration. Finally, linear regression as well as multi-layer perceptron regression are used to predict the TSS based on the most relevant features. Comparing the obtained regression results with the reference model, that is the polynomial regression model from standard DoE evaluation, a prediction improvement of nearly 50% is achieved. These results suggest the employed signals as a suited basis for an improved USMW process monitoring.

Quality prediction of disturbed ultrasonic metal welds

Ultrasonic Metal Welding is an industrially established welding process. Applications such as wiring harness assemblies, battery cell production and joints in power electronics show its relevance for current production and future technologies, but also allow zero failures. Still the mechanisms and influences involved in the joint formation process are not fully understood yet, and non-destructive testing methods to validate the joint quality are not available. Strict quality control of unprocessed material and destructive testing of welded sample parts are established methods to ensure statistical validation of sufficient joint quality.This study evaluates the influences of selected, pre-defined disturbances on the welding process of a statistically relevant number of welds. These disturbances include changes of material hardness, thickness, surface quality, rolling direction and welding position. The joint quality of these disturbed welds as well as a reference data set was determined by destructive shear testing. During welding, measurements of process variables like horn and anvil vibrations and the penetration depth of the horn were taken at high frequencies. Internal, statistical data provided by the welding machine is labelled with its shear strength. Subsequently, process characteristics are derived from this dataset, allowing for the estimation of the achieved weld quality. In this paper, the underlying robust estimation method to predict the weld quality based on these measurements will be presented and compared to current limit values and linear regression used in the industry to monitor the welding process.

Analysis of the oscillation behavior during ultrasonic welding of EN AW-1070 wire strands and EN CW004A terminals

For fulfilling the demand of durable yet lightweight electrical connections in transportation industries, ultrasonic metal welding (USMW) sees widespread use in these branches. As the ultrasound oscillations utilized in the welding procedure occur at a range of only a few micrometers at frequencies of 20–100 kHz for an overall duration of only 50–1500 ms, it is not possible to observe the compaction behavior with the bare eye. This paper focusses on investigating the oscillation behavior of the horn, the anvil, and the joining partners during the welding procedure by utilizing an array of synchronized laser vibrometers and performing welds with incrementing time stages. The oscillation data is correlated with temperature measurements in the welding zone as well as tensile testing results. Inter alia the formation of sidebands at the fundamental frequency as well as 2nd- and 3rd-order harmonics has been observed for the anvil, terminal, and wire front face when exceeding optimal weld time which would lead to maximum joint strength. Following the assumption of other research groups, the cause of these sidebands could be a change in relative motion of these components. As the terminal is slipping with increasing weld time, it could be assumed that the reason for the sidebands is low-frequency movement of the anvil, modulated onto the fundamental frequency, additionally indicating successful bonding of the stranded wire and the terminal. Furthermore, this slipping of the terminal on the anvil could lead to increased wear of the anvil knurls.

Process Monitoring in Ultrasonic Metal Welding of Lithium Batteries by Power Signals

Abstract Ultrasonic metal welding is one of the key technologies in manufacturing lithium batteries, and the welding quality directly determines the battery performance. Therefore, an online welding process monitoring system is critical in identifying abnormal welding processes, detecting defects, and improving battery quality. Traditionally, the peak welding power is used to indicate abnormal process signals in welding process monitoring systems. However, since various factors have complex impacts on the electric power signals of ultrasonic welding processes, the peak power is inadequate to detect different types of welding defects. Therefore, a signal pattern matching method is proposed in this study, which is based on the electric power signal during the entire welding process and thus is capable of identifying abnormal welding processes in various conditions. The proposed method adopts isometric transformation and homogenization as signal pretreatment methods, and Euclidean distance is used to calculate the similarity metric for signal matching. The effectiveness and robustness of the proposed method are experimentally validated under different abnormal welding conditions.

Quality prediction of ultrasonically welded joints using a hybrid machine learning model

Ultrasonic metal welding has advantages over other joining technologies due to its low energy consumption, rapid cycle time and the ease of process automation. The ultrasonic welding (USW) process is very sensitive to process parameters, and thus can be difficult to consistently produce strong joints. There is significant interest from the manufacturing community to understand these variable interactions. Machine learning is one such method which can be exploited to better understand the complex interactions of USW input parameters. In this paper, the lap shear strength (LSS) of USW Al 5754 joints is investigated using an off-the-shelf Branson Ultraweld L20. Firstly, a 33 full factorial parametric study using ANOVA is carried out to examine the effects of three USW input parameters (weld energy, vibration amplitude & clamping pressure) on LSS. Following this, a high-fidelity predictive hybrid GA-ANN model is then trained using the input parameters and the addition of process data recorded during welding (peak power). Once trained, the predictive model is tested against seven unseen parameter combinations specimens. Analysing the experimental data shows that the LSS performance envelop is non-linear with respect to the process variables of clamping pressure, vibration amplitude and welding energy. Vibration amplitude is the dominant input parameter affecting the LSS of the joints. At a fixed welding energy, the LSS can be increased by increasing vibration amplitude. However, the effect of clamping pressure on LSS is dependent on the level of welding energy. The resultant GA-ANN model accurately predicts the LSS of unseen test data producing a mean absolute percentage error of 7.51% with a Pearson's correlation coefficient of 0.96 for all data. It is demonstrated that including process data in a closed loop reduces the mean prediction error from 13.17% to 7.51%.

Anvil state identification based on acceleration signals in ultrasonic metal welding of lithium batteries

Ultrasonic metal welding is a high-frequency vibration process that creates joints for components and has attracted extensive research attentions in recent years. Although this technology has been applied in many areas such as electronics, automotive, and aerospace, its wide adoption is still hindered by unsatisfactory welding quality. In such cases, performing process monitoring to enhance the welding quality is of great significance. In current literature, the exploration in monitoring and inspection of the ultrasonic welding process is still limited due to the ineffective offline monitoring and the lack of investigation of the anvil state impacts on welding results. To address this problem, an acceleration sensor monitoring system is proposed in this study aiming for enhancing the welding quality. In the established system, the acceleration signals are collected and decomposed by variational modal decomposition (VMD) to capture the characteristics of the intrinsic mode functions (IMFs) energy distributions. In addition, the particle swarm optimization (PSO) algorithm is adopted for penalty term selection while the system frequency and sampling rate are used for mode number determination. The proposed method is experimentally validated and suggests high effectiveness and robustness for anvil state identification in ultrasonic metal welding.

Evaluation of Weldability based on Alignment Condition of Horns and Anvils used in Multilayered Battery Cell Welding Process in Ultrasonic Metal Welding

A battery cell has a foil-to-tab structure that is connected with multi-layer foils and a tab, and it is important to secure the weld quality to improve the battery performance. Ultrasonic metal welding (UMW) is widely applied as a technology suitable for battery cell welding because it provides a relative low heat input to welds owing to solid-state welding. In this study, 40 layers of 8 μm Cu foil and one layer of nickel-plated 0.2 mm Cu strip applied to a multilayered battery cell were used to evaluate the weldability of the materials for the UMW process. An experiment was conducted under four alignment conditions of a horn and an anvil to investigate the effect of the alignment on the weldability. The weld quality was classified based on the weld strength, discoloration, and failure mode of the test specimen to obtain a suitable welding range. The effect of the alignment condition on the deformation and weld shape was investigated by observing the cross-section of welds made under the four alignment conditions.

Weld Strength of Longitudinal and Longitudinal-Torsional Mode Horns in Ultrasonic Metal Welding

The ultrasonic metal welding technique has been widely used because of the need to weld different materials for meeting high quality performance requirements. The key part in this type of welding is the horn, which plays an important role in the weld quality. Longitudinal vibration has so far been the most popular vibration mode for ultrasonic horns, but the longitudinal mode coupled with torsional mode is gaining a lot of attention these days owing to its better performance compared to the pure longitudinal mode. Although there are many studies on the performance of these two mode horns, comparative studies based on the performance of these two modes, particularly in ultrasonic metal welding, are very rare. This study focuses on the welding performance comparison of these two horns with 20 kHz resonant frequency. Experimental results show that the performance of the longitudinal-torsional horn is better than that of the longitudinal horn in terms of welding strength.

Effect of Ultrasonic Techniques on Welding Technologies

The article discusses selected physical properties of industrial ultrasonic equipment utilising the magnetostrictive or electrostrictive effect. Particular attention was paid to equipment enabling the ultrasonic welding of various metals and thermoplastics. The research involved the comparison of various designs and operation of technological equipment, taking into account selected energy, control and environmental aspects. Based on reference publications it was possible to determine and categorise general features concerning the application of ultrasonic technologies as well as to indicate factors responsible for the formation of imperfections during the ultrasonic welding process.

Ultrasonic Metal Welding of Multilayered Copper Foils to Nickel-Plated Copper Sheet in Lithium-Ion Battery Cell

The battery performance of electric vehicles depends on the density and capacity of the battery; thus, the battery cells must be assembled in as many layers as possible. Electric vehicle batteries are typically composed of several cells which form modules connected by busbars, with dozens of modules manufactured as battery packs. The ultrasonic metal welding (UMW) technology is applied to such multilayered foil welding. This study analyzed UMW to ensure the weldability of multilayered Cu foils and a Ni-plated Cu strip in lithium-ion battery cells through various approaches. In UMW, the effect of the alignment on weld production and quality were examined through the energy and mechanical performance of the weld by conducting comparative experiments on the alignment of the horn and anvil. Additionally, the effects of UMW process parameters, such as the welding pressure, amplitude, and welding time, were statistically analyzed. The weldability evaluation and characteristic analysis were performed based on these variables. Furthermore, the cross-sectional shapes and microstructure behavior of the Ni layers were analyzed based on the weld quality.

Dynamic Weld evolution during ultrasonic welding of Cu–Al joints

Although ultrasonic welding of dissimilar metals has been widely applied in the automotive and electronic industries, the dynamic weld evolution, including deformation, breakage, and attachment, is still in a conjecture stage because of the short welding time, thin reaction layer and complex thermomechanical behavior. In this study, dissimilar welds of aluminum (Al) alloy 1060 to T2 copper (Cu) were comprehensively investigated via microstructural characterization, molecular dynamics simulations, and mechanical tests. It was found that micro-welds first formed at high asperities. Subsequently, the continuous relative friction at the interface induced severe plastic deformation on the Al side, resulting in the alternating fracture and formation of micro-welds, flattening of asperities, and attachment of Al on the Cu surface. The lap shear failure loads for the joints gradually increased with increasing welding time. The failure loads reached the maximum values of 0.9 ± 0.03 kN after a welding time of 0.29 s. Furthermore, the Cu asperities completely sunk into the Al matrix, presenting a macro-interlocking morphology at the interface. A swirl-like grain morphology with no deformation texture appeared simultaneously on the Al side. Analysis of the weld formation indicated that microstructural evolution of the joint, including material flow, grain morphology, and grain orientation, was mainly concentrated on the softer material side, providing a new understanding of the ultrasonic welding of dissimilar metals.

Evaluation of Vibration Amplitude Stepping and Welding Performance of 20 kHz and 40 kHz Ultrasonic Power of Metal Welding

Today ultrasonic power technique is consider a mandatory technique which is always entered in many processes such as in metal and plastic welding to overcomes many issues, with aided of applying force (pressure) and supplied high frequency vibration, a solid-state weld can be generated by ultrasonic metal welding technique. That give a technique the ability to join not only a small components, whereas also to join thicker specimens, depend on a proper control of matching welding conditions. Therefore a welding performance can be study and compared after designed welding horn to resonance at frequencies of 20 kHz and 40 kHz. The analyses of the designed horn are completed through use a vibration mathematical expressions, modal and harmonic analyses to ensure the weldability due to applying ultrasonic power to the working area and also to compare the performance of joint at using two resonance frequencies of 20 kHz and 40 kHz. The dimensions of the horns were determined to match the selected resonance frequencies, which the lengths were calculated as 132 mm and 66 mm respectively. The analysis of the exciting modal indicates that the axial vibration modes of 19,584Hz and 39,794Hz are obtained in 10th mode, while the two frequency values are recorded 19,600 Hz and 39,800 Hz from the frequency response of the two horns. The weld strength between Al and Cu specimens with a thickness 0.5 mm were evaluated using tensile test, which the analyses were obtained under using different welding pressure and varied amplitudes. The results were recorded within exciting a horn with two different resonance frequencies, show the enhancement of weld strength and quality through control of stepping amplitude, the enhancement means obtain good strength of the weld, reduce sticking horn to specimen, and lower specimen marking.

Multi-Spot Ultrasonic Welding of Aluminum to Steel Sheets: Process and Fracture Analysis

Ultrasonic metal welding is an energy-efficient, fast and clean joining technology without the need of additional filler materials. Single spot ultrasonic metal welding of aluminum to steel sheets using automotive materials has already been investigated. Up to now, further studies to close the gap to application-relevant multi-metal structures with multiple weld spots generated are still missed. In this work, two different spot arrangements are presented, each consisting of two weld spots, joined 0.9 mm thick sheets of wrought aluminum alloy AA6005A-T4 with 1 mm sheets of galvannealed (galvanized and annealed) dual-phase steel HCT980X. An anvil equipped with variable additional clamping punches was used for the first time. The tensile shear forces reached 4076 ± 277 N for parallel connection and 3888 ± 308 N for series connection. Temperature measurements by thermocouples at the interface and through thermal imaging presented peak temperatures above 400 °C at the multi-metal interface. Microscopic investigations of fractured surfaces identified the Zn layer of the steel sheets as the strength-limiting factor. Energy-dispersive X-ray spectroscopy (EDX) indicated intermetallic phases of Fe and Zn in the border areas of the weld spots as well as the separation of the zinc layer from the steel within these areas.

Evaluation of Vibration Amplitude Stepping and Welding Performance of 20 kHz and 40 kHz Ultrasonic Power of Metal Welding

Today ultrasonic power technique is consider a mandatory technique which is always entered in many processes such as in metal and plastic welding to overcomes many issues, with aided of applying force (pressure) and supplied high frequency vibration, a solid-state weld can be generated by ultrasonic metal welding technique. That give a technique the ability to join not only a small components, whereas also to join thicker specimens, depend on a proper control of matching welding conditions. Therefore a welding performance can be study and compared after designed welding horn to resonance at frequencies of 20 kHz and 40 kHz. The analyses of the designed horn are completed through use a vibration mathematical expressions, modal and harmonic analyses to ensure the weldability due to applying ultrasonic power to the working area and also to compare the performance of joint at using two resonance frequencies of 20 kHz and 40 kHz. The dimensions of the horns were determined to match the selected resonance frequencies, which the lengths were calculated as 132 mm and 66 mm respectively. The analysis of the exciting modal indicates that the axial vibration modes of 19,584Hz and 39,794Hz are obtained in 10th mode, while the two frequency values are recorded 19,600 Hz and 39,800 Hz from the frequency response of the two horns. The weld strength between Al and Cu specimens with a thickness 0.5 mm were evaluated using tensile test, which the analyses were obtained under using different welding pressure and varied amplitudes. The results were recorded within exciting a horn with two different resonance frequencies, show the enhancement of weld strength and quality through control of stepping amplitude, the enhancement means obtain good strength of the weld, reduce sticking horn to specimen, and lower specimen marking.

Orbital Ultrasonic Welding of Ti-Fittings to CFRP-Tubes

Hybrid structures are important for the automotive and aeronautical industry as they have the potential to reduce vehicle or aircraft weight and to improve fuel efficiency. Continuous ultrasonic metal welding is a promising technique for hydraulic applications in aircraft to realise tubular metal/fiber reinforced polymer (FRP) hybrids. Fluid proof connections between dissimilar components can be joined by continuous welding seams. Tubular metal/FRP hybrids, produced by a new advanced variant of ultrasonic metal welding, are investigated as a potential substitute for metallic hydraulic tubes. The oscillating welding system moves around the tubular joining partners to generate a sealed orbital connection. Homogeneous joint quality is required to assure the requested component strength. Therefore, the amplitude of sonotrode displacement and the welding force are controlled to keep the induced welding energy constant and the joint quality uniform. High mechanical strength is required for a safe application in the 5000 psi hydraulic system of current and future aircraft concepts. For this study metal injection molded (MIM) titanium fittings (TiAl6V4) and carbon fiber reinforced PEEK (CF-PEEK) tubes were investigated. Process parameters for metal/FRP hybrid joining were evaluated considering their mechanical and technological properties, as well as the microstructure of the hybrid interfacial area. The entire joining area of tubular joining partners has to be in close contact before welding to assure a continuous tight joint. Hence, the titanium fitting is thermally shrunk onto the CFRP tube before ultrasonic welding. The presented orbital ultrasonic welding technology was developed for prospective industrial use and future applications of ultrasonically welded tubular multi-material-components.

Hybrid multi-task learning-based response surface modeling in manufacturing

Response surface modeling is an essential technique for identifying the optimal input parameters in a process, especially when the physical knowledge about the process is limited. It explores the relationships between the process input variables and the response variables through a sequence of designed experiments. Conventional response surface models typically rely on a large number of experiments to achieve reliable modeling performance, which can be cost prohibitive and time-consuming. Furthermore, nonlinear input-output relationships in some processes may not be sufficiently accounted for by existing modeling methods. To address these challenges, this paper develops a new response surface modeling approach based on hybrid multi-task learning (H-MTL). This approach decomposes the variability in process responses into two components–a global trend and a residual term, which are estimated through self-learning and MTL of Gaussian process (GP), respectively. MTL leverages the similarities between multiple similar-but-not-identical GPs, thus achieving superior modeling performance without increasing experimental cost. The effectiveness of the proposed method is demonstrated by a case study using experimental data collected from real-world ultrasonic metal welding processes with different material combinations. In addition, the hyperparameter selection, the effects of the number of tasks, and the determination of the stopping criterion are discussed in detail.

Online tool condition monitoring for ultrasonic metal welding via sensor fusion and machine learning

In ultrasonic metal welding (UMW), tool wear significantly affects the weld quality and tool maintenance constitutes a substantial part of production cost. Thus, tool condition monitoring (TCM) is crucial for UMW. Despite extensive literature focusing on TCM for other manufacturing processes, limited studies are available on TCM for UMW. Existing TCM methods for UMW require offline high-resolution measurement of tool surface profiles, which leads to undesirable production downtime and delayed decision-making. This paper proposes a completely online TCM system for UMW using sensor fusion and machine learning (ML) techniques. A data acquisition (DAQ) system is designed and implemented to obtain in-situ sensing signals during welding processes. A large feature pool is then extracted from the sensing signals. A subset of features are selected and subsequently used by ML-based classification models. A variety of classification models are trained, validated, and tested using experimental data. The best-performing classification models can achieve close to 100% classification accuracy for both training and test datasets. The proposed TCM system not only provides real-time TCM for UMW but also can support optimal decision-making in tool maintenance. The TCM system can be extended to predict remaining useful life (RUL) of tools and integrated with a controller to adjust welding parameters accordingly.

Multi-task learning for data-efficient spatiotemporal modeling of tool surface progression in ultrasonic metal welding

Spatiotemporal processes commonly exist in manufacturing. Modeling and monitoring such processes are crucial for ensuring high-quality production. For example, ultrasonic metal welding is an important industrial-scale joining technique with wide applications. The surfaces of ultrasonic welding tools evolve in both spatial and temporal domains, resulting in a spatiotemporal process. Close monitoring of tool surface progression is imperative since degraded tools often lead to low-quality joints. However, it is generally expensive and time-consuming to acquire fine-scale surface measurement data, which is not economically viable. This paper develops a multi-task learning method to enable data-efficient spatiotemporal modeling. A Gaussian process-based hierarchical Bayesian inference structure is constructed to transfer knowledge among multiple similar-but-not-identical measurement tasks. Meanwhile, a spatiotemporal kernel is developed based on squared sine exponential damping (SSED) function to characterize the periodic trend of anvil surfaces. The proposed method is able to improve interpolation accuracy using limited measurement data compared with state-of-the-art techniques. Data collected from lithium-ion battery production are employed to demonstrate the effectiveness of the proposed method. Additionally, the influence of training data size and hyperparameter selection on the modeling performance is systematically investigated.

3D finite element model of dynamic material behaviors for multilayer ultrasonic metal welding

Ultrasonic metal welding (UMW) has been widely applied as a high throughput solid-state joining technology for multilayers of sheet metal. During a typical UMW process, multilayer work materials are mechanically compressed by a knurl-patterned horn (also known as a sonotrode) onto an anvil tool, and a simultaneous in-plane sliding is applied to the horn at an ultrasonic frequency (20 kHz or higher) to help form the weld at the material interfaces. There is a great challenge in modeling and simulating the dynamic behavior of the work material and the whole weld formation process is subject to ultrasonic mechanical loadings imposed by the knurl-patterned horn tool. In this work, finite element (FE) models are developed to simulate the multilayer UMW process using knurl-patterned tools by directly applying the ultrasonic vibration as a model input. For a short weld duration of 0.1∼0.5 s, a high-fidelity FE modeling approach is developed using ABAQUS/Explicit to simulate the dynamic material response under the 20 kHz horn vibration. For an extended long welding duration of approximately 1.0 s, a computationally efficient hybrid approach is developed using both ABAQUS/Explicit and DEFORM-3D in order to leverage the strengths of each software package. The developed models are validated using experimental data of dynamic welding force, temperature, and weld geometry from in-situ process measurements of UMW. The 3D FE models developed in this study are the most comprehensive solution to date to simulate the complex material response subject to UMW process conditions and provide engineering guidance for the design of UMW applications.