Laser deep penetration welding of die-cast aluminum presents challenges as the presence of retained gas under high pressure leads to a modified spatial and temporal keyhole behavior, which determines conditions for process pore formation. Currently different investigation with the goal manipulating the keyhole behavior focus on the use of adjustable core-to-ring intensity distributions. In wrought materials, a core-to-ring intensity distribution is referred to advantage a broader melt pool and enhance the keyhole width especially on the top, while only few investigations on welding of aluminum die-cast are reported. In this paper, investigation with in situ high-speed synchrotron X-ray imaging of a deep penetration laser welding process involving die-cast aluminum with different core-to-ring intensity distribution are presented. The results highlight a broadening of the keyhole width in the core-dominated process, while in the ring dominated process the keyhole width on the top is strongly reduced, leading to a pronounced bulging on the bottom of the keyhole. Further in the core-dominated process a second separated channel is formed beginning from the tip of the keyhole growing then in the rear part of it. This is then responsible for formation of a non-spherical cavity in the solidified pool. In the case of a ring-dominated regime bulging is occurring with mainly large bulging pores forming.

In industrial manufacturing, oil contamination on the surface of the workpiece is difficult to completely avoid during laser beam welding (LBW). In this study, LBW experiments were conducted on stainless steel sheets with controlled oil contamination located on the top surface of the upper plate and at the interface between the upper and lower plates in an overlap configuration. The experimental results demonstrate that oil evaporation affects the pressure balance within the keyhole, inducing violent keyhole oscillations and unstable melt pool dynamics. This leads to the formation of various weld defects, including lack of fusion, porosity, and blowout defects. Therefore, in industrial production, contamination of workpieces by oil should be detected. For automatic detection of oil contamination, machine learning based algorithms were deployed using acoustic signals. Airborne and structure-borne Acoustic Emission (AE) signals were recorded simultaneously during the welding process. Feature extraction from the acoustic signal channels was carried out using the Wavelet Scattering Transform (WST). The results show that the identification F1-macro score for oil contaminated regions exceeds 99% with 10 ms temporal resolution. This indicates that contaminated and clean welding regions can be reliably distinguished. The study suggests that even under acoustic interference from cross-jet with 5 bar air pressure, AE provides a promising approach for in-situ detection of oil contamination and process monitoring in LBW.

The growing demand for lightweight and cost-effective conductor materials in the electrical and automotive sectors is accelerating the substitution of copper (Cu) by aluminum (Al). However, aluminum’s native oxide layer can cause high contact resistances, so copper is still used as a terminal conductor at cable ends. Laser-based joining processes offer high flexibility and productivity, but they tend to promote the formation of brittle intermetallic compounds (IMCs), thereby decreasing mechanical strength and electrical performance. Indirect laser welding is a promising alternative, as it generates only a thin interfacial fusion zone and can thus reduce IMC formation. To date, this approach has mainly been studied for simple sheet-to-sheet joints without surface preparation at the interface, leaving open the extent to which surface modifications can be used to improve joint properties. In this study, three optimization strategies were investigated in spot-weld experiments: (i) laser structuring of the copper joining partner to promote mechanical interlocking, (ii) nickel (Ni) coating of copper as a diffusion barrier to mitigate Cu-Al IMCs, and (iii) a combination of both approaches. Joint performance was assessed by lap-shear strength and total electrical resistance. Laser-structured copper surfaces enabled the eutectic melt to enter the structure cavities, creating a combined mechanical and metallurgical bond and increasing lap-shear strength by approximately 40%. Nickel interlayers effectively suppressed intermetallic formation, but joint strength was governed by the adhesion of the coating to the copper substrate. Additional laser structuring of the Ni coating provided mechanical interlocking and strengths comparable to structured copper while further reducing intermetallic formation, at the expense of increased surface-preparation effort.

High welding speeds above 8 m/min during laser beam welding of high-alloy steel lead to spatter formation, resulting in material losses and spatter adhesion that significantly degrade seam quality. Superimposing the main laser intensity with a second laser intensity leads to a reduction of such effects, as melt flow conditions around the keyhole are influenced. In this configuration, the area hit from the superimposed laser must be notably larger than the main laser spot, in order to enlarge the melt pool on the top side. However, the basic interactions of keyhole, melt pool, and metal vapor using a superimposed intensity are largely unknown. In order to identify and understand the related effects with combined laser intensities a welding speed of 12 m/min was chosen, while through high-speed synchrotron radiation imaging visualization of keyhole behavior and melt flow characteristics by tracking tungsten carbide particles was carried out. The superimposed intensity widens the melt pool on the sheet top side, thereby increasing the cross-sectional area of the melt flow, while the keyhole geometry and its fluctuations remain comparable to those observed without superimposed intensity. This reduces the melts kinetic energy of the flow around the keyhole and finally spatter formation. Raising power of the superimposed laser intensity, the keyhole width expands over its entire depth, and a bulge on the keyhole rear wall due to vaporizing material is formed. This bulge causes fluctuations of the melt pool and leads to lateral spatter formation due to an upward directed melt flow at the keyhole walls. Therefore, in order to reduce spatter formation, the power of the superimposed intensity has to be chosen consequently in order to enhance melt pool dimensions and avoid bulging.

Modern solid-state lasers enable a continuous increase in welding speed due to higher possible laser beam powers. However, welding speeds above 8 m/min for high-alloy steels lead to altered melt flow dynamics and increased spatter formation, resulting in spatter adhesion and particularly in undercuts along the weld seam. In contrast to the cost- or time-intensive methods described in the state of the art to reduce these effects, modifying the intensity distributions appears to be a simple and effective approach that is independent of the welding direction. In particular, using a superimposed intensity allows a detailed investigation by adjusting the intensity with varying spot sizes or laser powers. Using a superimposed intensity to widen up the melt pool is especially effective in reducing the velocity of the melt flow around the keyhole and, consequently, spatter formation. However, its effect on other melt pool flows, especially the upward directed melt flow along the keyhole rear wall, remains largely unknown. To investigate these effects, high-speed synchrotron X-ray imaging in combination with tungsten carbide tracer particles was used to visualize the melt flow dynamics. Generally, two melt vortices are observed in the area behind the keyhole, one in the upper and one in the lower part of the melt pool. The backward melt flow at the melt pool bottom is unaffected by the additional energy input due to the limited penetration depth of the second laser. Furthermore, the upward directed flow along the keyhole rear wall remains unchanged. When increasing the second laser power, the keyhole widens and fluctuates at the rear wall. As a result, the upward melt flow at the rear wall is not discernible. In general, the superimposed intensity alters the melt flow behind the keyhole. However, these changes appear to be minor significant for spatter formation compared to the flow around the keyhole.

Pulsed laser beam welding is widely used to weld thin aluminium alloy sheets, for example, in e-mobility components such as prismatic battery casings. However, aluminium alloys are prone to hot cracking phenomena during solidification, which is affected by different factors. In pulsed laser welding, the solidification rate is highlighted as a key factor, while tensile strain development during solidification shrinkage must also be considered a dominant factor leading to hot cracking. Accurate temporal strain measurements preceding hot crack initiation during solidification remain challenging to obtain, especially beneath the welding surface. Therefore, a thermomechanical finite element model is developed in ANSYS software to simulate time-dependent strain evolution at different depths around the melt pool during solidification. The simulation results are compared with experimental results of time-resolved high-speed synchrotron X-ray diffraction (XRD) measurements beneath the welding surface. The finite element model accurately captures the overall strain evolution and peak strain magnitudes observed experimentally, demonstrating good agreement in terms of global strain trends. Additionally, the time-dependent strain measurements reveal distinct strain-drop times corresponding to crack initiation at different time lags and to crack propagation from the melt pool boundary.

Laser welding of high-alloy steels leads to characteristic spatter formation at welding speeds above 8 m min-1 within the Single Wave and Elongated Keyhole regimes, which reduces seam quality due to material loss and adhering spatter. Spatter formation is strongly influenced by the melt flow dynamics, particularly by the local flow velocity and direction. Although current literature provides model-based descriptions of the upward melt flow along the keyhole rear wall and the flow around the keyhole, experimental measurements of these flow velocities are of particular interest. In this study, melt flow directions and velocities are quantified using high-speed synchrotron X-ray imaging combined with tungsten carbide particle tracking. As the welding speed increases and the process shifts from the Rosenthal to the Single Wave regime, the upward melt flow along the keyhole rear wall accelerates, resulting in the detachment of comparatively large spatter from a melt swelling behind the keyhole. At even higher welding speeds within the Elongated Keyhole regime, an enhanced flow around the keyhole promotes the detachment of multiple spatter from a single melt swelling.

Metal mixing during laser beam welding (LBW) of dissimilar materials like aluminum and copper influences the formation of intermetallic phases and affects weld quality in applications such as e-mobility. This paper investigates metal mixing phenomena in keyhole LBW of copper-on-aluminum via experimental and numerical analysis based on computational fluid dynamics (CFD). For this purpose, keyhole LBW of EN CW004A and EN AW-1050A using a short-wavelength green laser is modeled in FLOW-3D by utilizing the volume-of-fluid (VOF) approach. The results reveal characteristic melt flow patterns, including flow upward originating from the bottom of the keyhole region, surface flow along the copper top surface, outward flow from inside the keyhole, and local recirculation adjacent to the keyhole. Predicted melt pool width and penetration depth are validated against metallographic cross-sections with very close consensus. The study provides insight into flow-driven mixing behavior in keyhole LBW of copper-on-aluminum.

Welding thin steel sheets in industrial applications is difficult because joint gaps occur during the process, which can lead to weld interruptions. Such welds are considered a reject and in order to avoid the weld to interrupt it is crucial to hinder the formation of joint gaps. Especially laser beam welding is affected by the emergence of gaps. Due to the narrow laser spot, product quality is highly dependent on the alignment and positioning of the sheets. This is typically done by clamping devices, which hold the workpieces in place. However, these clamps are suited for a specific workpiece geometry and require manual redesign every time the process changes. Adaptive clamping devices instead are designed to realize a time-dependent workpiece adjustment. Modeling the joint gap behavior to realize a controller for adaptive clamps can be difficult as the influence of heating, melting, and cooling on the joint gap formation is unknown and varies due to temperature dependent physical properties. Instead, the control parameters and actions can be derived using data-driven methods. In this paper, we present a novel data-driven approach how deep learning can be utilized to manipulate the sheet position during the weld with two actuators that apply force. A temporal convolution neural network (TCN) analyzes the change of the joint gap and predicts the required force to adapt the workpiece position. The developed method has been integrated into the welding process and improves the length of the average weld seam by 39.5% compared to welds without an active adjustment and 1.4% to welds that have been adapted with a constant force.

Spatter formation is a major issue at welding speeds above 8 m/min for full penetration laser beam welding of high-alloyed steels. In experiments using a local gas flow directed at the keyhole rear wall, a reduction in spatter formation on the specimen top side was observed for welding of AISI 304. However, the interaction between gas flow and keyhole behavior with respect to the mechanisms and locations of spatter detachment, especially on the bottom side, is not yet fully understood. High-speed synchrotron X-ray imaging enables detailed insights into the keyhole behavior and the spatter formation to obtain a deeper understanding of the underlying mechanisms. During the reference experiments welding without shielding gas flow, the spatter detach from a melt pool swelling behind the keyhole aperture on both sides of the sheet. A gas flow with a low flow rate of 4.8 L/min reduces the spatter formation on the top side and the keyhole length due to the absence of oxygen affecting the surface tension. A swelling also forms on the keyhole front on the bottom side and small spatter detach undirected. Increasing the flow rate to 12.8 L/min elongates the keyhole, particularly on the specimen top side. The increased momentum transfer of the gas flow results in a periodic keyhole oscillation on the specimen top side. In combination with an elongated melt pool, the oscillation is directly correlated with the hump formation, caused by melt being pushed over the already solidified weld seam. In addition, spatter does not detach from the top side due to the changed melt flow and only detach from the keyhole front on the bottom side.

This paper examines the impact of spatial power distributions on the time-dependent keyhole behavior during laser beam welding of copper using high-speed synchrotron X-ray imaging. The experimental setup utilized a COHERENT HighLight FL8000-ARM fiber laser with concentric intensity distribution created by an optical fiber cable. The European Synchrotron Radiation Facility (ESRF, beamline ID19) was used to conduct high-speed synchrotron imaging at 20,000 images per second to study the spatio-temporal keyhole behavior. Keyhole geometries were extracted through advanced image processing techniques, allowing quantification of parameters like depth, aperture, bulging, and determination of related oscillation frequencies. The results showed that core-dominated processes exhibit significant variations in keyhole geometry. In contrast, ring-dominated processes exhibited reduced penetration depths but increased melt pool dynamics due to altered absorption conditions and increased temperatures within the melt pool. A stabilized core-ring power distribution minimized fluctuations, resulting in improved process stability. The findings were summarized in a model concept describing three characteristic keyhole regimes: core-dominated, ring-dominated, and stabilized core-ring processes.

Laser beam welding can produce narrow, high-quality welds in various industrial joining processes. The thermal expansion and contraction of the metal during the weld results in the displacement of the sheets. That leads to the formation of joint gaps and subsequent to a process interruption. This behavior has only been analyzed to a limited extent and causes manufacturers to rely on heavy clamping systems rather than using more flexible fixtureless approaches. Due to the time-consuming and costly nature of recording and producing erroneous weld seams, such recordings and datasets are rarely available in this area. This often limits the research towards adaptable fixtureless welding setups. Because of this, we present a multi-modal dataset consisting of 100 recorded welds that tracks the metal sheets movement. The developed setup enables the determination of boundary conditions for fixtureless welding. Two types of sensors record the welding process. First, three inductive probes are applied to record the metal sheets` movement and second, a long-wave infrared (LWIR) camera records changes in the thermal radiation field. Two different welding speeds and laser powers were used to produce a variety of welds. The dataset can be used for data-driven algorithms to predict the metal movement, analyze the thermal radiation field, or develop quality control methodologies.

Laser beam welding with partial gas shielding using local gas flows has been shown to be very effective in reducing spatter, especially when welding high-alloy steels at high processing speeds (>= 8 m/min). This paper examines the gas flow induced mechanical effect on keyhole geometry and correlating melt flow by means of a computational fluid dynamics analysis. Therefore, keyhole mode laser beam welding of AISI304 was modeled by using the volume of fluid method in FLOW-3D. The mechanical effect of the partial shielding was implemented by considering the gas flow induced dynamic pressure. By varying flow rates, a widening, and a reduction in fluctuation amplitude of the keyhole rear wall was detected. Furthermore, the melt flow dynamics were characterized by less flow velocity and melt movement. The results were validated by a visual comparison with high-speed synchrotron X-ray imaging, showing a high degree of agreement in modeling accuracy of the keyhole dynamics. (c) 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0)

Laser beam butt welding is often the technique of choice for a wide range of industrial tasks. To achieve high quality welds, manufacturers often rely on heavy and expensive clamping systems to limit the sheet movement during the welding process, which can affect quality. Jiggless welding offers a cost-effective and highly flexible alternative to common clamping systems. In laser butt welding, the process-induced joint gap has to be monitored in order to counteract the effect by means of an active position control of the sheet metal. Various studies have shown that sheet metal displacement can be detected using inductive probes, allowing the prediction of weld quality by ML-based data analysis. The probes are dependent on the sheet metal geometry and are limited in their applicability to complex geometric structures. Camera systems such as long-wave infrared (LWIR) cameras can instead be mounted directly behind the laser to overcome a geometry dependent limitation of the jiggles system. In this study we will propose a deep learning approach that utilizes LWIR camera recordings to predict the remaining welding process to enable an early detection of weld interruptions. Our approach reaches 93.33% accuracy for time-wise prediction of the point of failure during the weld.

Laser welding zinc-coated steels is of major importance in automotive engineering and other industries to ensure cost-effective corrosion resistance of assemblies without requiring post-weld rework. However, the low evaporation temperature of zinc is causing welding defects, in particular melt ejections and spatter. In this work, in-situ experiments of laser beam welding were performed using high-speed synchrotron Xray imaging (up to 40,000 images/second) to determine the dynamics in the keyhole and in the weld pool and to provide detailed explanations for the formation of weld defects. A simplified sample geometry made it possible to weld zinc-coated steel sheets (DX51D Z275) in a lap joint (sheet thickness 1.25 mm each) with a fiber laser (COHERENT HighLight FL-ARM 8000) to describe fundamental phenomena. The high spatial and temporal resolution of the radiography allowed describing the acting mechanisms and their effect on keyhole and melt pool. (c) 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0)

Welding of high-alloy steels results in spatter formation addressing high welding speeds above 8 m/min, i.e., the seam quality is significantly reduced due to material losses and adhering spatter. A reduction of spatter can be addressed by using concentric intensity distributions consisting of core and ring, by affecting melt and metal vapor flow. In this paper, the understanding of spatter formation on sheet top and bottom side is significantly enhanced for full penetration welds of AISI 304. Therefore, different concentric intensities and tophat distributions were systematically studied and compared. Fundamental interactions between concentric intensity distributions and spatter formation during full penetration welding were determined and summarized in model concepts. In particular, spatter formation can be reduced on both sheet sides using a concentric intensity distribution due to a smaller keyhole geometry with a smaller angle of inclination of the keyhole front. (c) 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0)

With the increasing power and speed of laser welding, in-process monitoring has become even more crucial to ensure process stability and weld quality. Due to its low cost and installation flexibility, acoustic process monitoring is a promising method and has demonstrated its effectiveness. Although its feasibility has been the focus of existing studies, the temporal resolution of acoustic emissions (AE) has not yet been addressed despite its utmost importance for realizing real-time systems. Aiming to provide a benchmark for further development, this study investigates the relationship between duration and informativeness of AE signals during high-power (3.5 kW) and high-speed (12 m/min) laser beam butt welding. Specifically, the informativeness of AE signals is evaluated based on the accuracy of detecting and quantifying joint gaps for various time windows of signals, yielding numerical comparison. The obtained results show that signals can be shortened up to a certain point without sacrificing their informativeness, encouraging the optimization of the signal duration. Our results also suggest that large gaps (>0.3 mm) induce unique signal characteristics in AE, which are clearly identifiable from 1 ms signal segments, equivalent to 0.2 mm weld seam.

This study aimed to explore the feasibility of using airborne acoustic emission in laser beam butt welding for the development of an automated classification system based on neural networks. The focus was on monitoring the formation of joint gaps during the welding process. To simulate various sizes of butt joint gaps, controlled welding experiments were conducted, and the emitted acoustic signals were captured using audible-to-ultrasonic microphones. To implement an automated monitoring system, a method based on short-time Fourier transformation was developed to extract audio features, and a convolutional neural network architecture with data augmentation was utilized. The results demonstrated that this non-destructive and non-invasive approach was highly effective in detecting joint gap formations, achieving an accuracy of 98%. Furthermore, the system exhibited promising potential for the low-latency monitoring of the welding process. The classification accuracy for various gap sizes reached up to 90%, providing valuable insights for characterizing and categorizing joint gaps accurately. Additionally, increasing the quantity of training data with quality annotations could potentially improve the classifier model’s performance further. This suggests that there is room for future enhancements in the study.