Solidification cracking in laser beam welding (LBW) is governed by local thermal conditions at the weld-pool boundary, yet the sub-micron microstructural response across the full mushy zone remains poorly understood. This study employs an automated phase-field (PF) simulation workflow, integrated within the Kadi4Mat research data management platform, to conduct a systematic, FAIR-compliant parametric study of dendritic solidification in the quaternary EN 1.4301 (Fe–Cr–Ni–C) alloy. Thermal conditions—thermal gradient (G), solidification velocity (Vs), and grain misorientation angle (θR)—are extracted from a thermocouple-validated ANSYS Fluent weld-pool model and used as inputs to 2D and 3D PACE3D phase-field simulations spanning G from 100 to 900 K/mm, Vs from 5 to 40 mm/s, and θR from 0∘ to 45∘. Key findings demonstrate that θR is a critical parameter—alongside G and Vs—governing the cellular-to-dendritic morphological transition, secondary dendrite arm formation, and the topology of inter-dendritic liquid (continuous films versus isolated pockets), each carrying distinct solidification cracking risk pathways. The Kadi4Mat workflow reduces manual pre-processing effort substantially while ensuring data reproducibility and reuse. Quantitative validation against electron probe micro-analysis confirms primary dendrite arm spacing predictions within 12% at the upper weld surface and within 4% at the mid-section.

Laser beam welding (LBW) is a non-contact joining technique that has gained significant importance in modern industrial manufacturing. One potential problem, however, is the formation of solidification cracks, which particularly affects alloys with a pronounced melting range. The aim of the present work is the development of computational methods and software tools to numerically simulate LBW. In order to obtain a sufficiently accurate solution, a large number of finite elements has to be used. Therefore, a highly parallel scalable solver framework, based on the software library PETSc, was used to solve this computationally challenging problem on a high-performance computing architecture. Finally, the experimental results and the numerical simulations are compared. They are found to be in good qualitative agreement, which confirms the validity of the numerical simulations and allows for a better interpretation of the experimentally observed strain distribution.

This paper presents the results of developing a cost-effective, robust process for welding thick steel plates. Welding trials were performed on S355ML structural steel plates with a thickness of 80 mm. A specially designed U-shaped joint preparation with a 45 mm root face was proposed to enable thick welds to be welded using a combined technique. In the developed process, a hybrid laser arc weld (HLAW) is performed as the first pass. Subsequently, narrow-gap submerged arc welding (NG-SAW) is applied to the opposite side using a multi-layer technique. The weld cross-section is completed using a reliable overlap of both the HLAW and NG-SAW welds. This method achieves a 2.9-fold reduction in weld volume and filler material consumption, as well as shorter production times for thick-walled welds. Further advantages of the process combination include eliminating the need to form the root of the HLAW weld and the absence of a backing support. The applied process parameters ensure that the maximum heat input does not exceed 5 kJ/mm, leading to uniform hardness across the weld metal and heat-affected zone (HAZ). Impact toughness testing at -40 degrees C demonstrated excellent performance, with Charpy V-notch energies of 138 f 45 J in the arc-dominated region and 65 f 12 J in the critical laserdominated zone of the HLAW weld. In addition, the NG-SAW weld exhibited an average Charpy V-notch energy of 274 f 5 J, confirming excellent low-temperature toughness of the fill passes. Owing to its high process stability and practical applicability, the proposed welding approach shows high potential for integration into the fabrication of thick-walled offshore constructions.

Laser beam welding (LBW) in keyhole mode enables high-productivity joining for modern manufacturing processes, yet its industrial deployment is hindered by porosity defects that degrade weld quality and process reliability. This work presents a physics-informed optimization framework designed to systematically mitigate porosity in aluminum LBW by integrating multi-physics modeling, experimental data, and machine-learningbased predictive analytics. The framework couples a series of predictive physics-informed machine learning models (penetration predictor, porosity predictor, and physics estimator) with evolutionary and Bayesian optimization strategies to identify optimal process parameters across a wide operating space of laser power, welding speed, beam diameter, and focal position. High-fidelity thermal-fluid simulations and comprehensive experiments were used to train and validate the predictive models. The framework consistently converged toward parameter sets that achieve target penetration depths while suppressing porosity, revealing the inherent trade-off between weld penetration and defect formation. Beyond accurate prediction and optimization, the approach provides clear interpretability by quantifying key physical factors, such as keyhole stability, weld pool morphology, and local solidification rates, that govern porosity formation. The results demonstrate the potential of physics-informed machine learning as a scalable tool for quality-driven process control and intelligent optimization in advanced manufacturing processes.

Laser beam welding (LBW) of metallic components is a knowledge-intensive manufacturing process whose quality depends on the complex multi-physics. However, its engineering application is often hindered by the occurrence of porosity defects. Achieving a thorough understanding and reliable prediction of porosity defects remains difficult because it demands robust representation and reasoning over nonlinear and hard-to-observe physical information. In this study, we propose an integrated multimodal physics-informed machine learning (PIML) framework with the help of multi-physical modelling and experimental data to predict the porosity defects in laser beam welding of aluminum alloys. The whole framework contains a multimodal PIML model for predicting the porosity ratio and an ML-based estimator for relevant physical information. By utilizing the scalar welding parameters and high-dimensional physical information (probability of keyhole collapses, cumulative existing time of collapses, and molten pool geometry) as inputs, the multimodal PIML model shows great superiority in predicting the porosity ratio, with a reduction of the mean square error by 45%, compared with the ML model trained only with welding parameters. The ML-based estimator constructed with an encoder-decoder architecture can accurately reproduce the critical physical information within a timeframe of seconds. By integrating these two ML models, the proposed framework advances engineering informatics by offering a scalable, physics-knowledge-centric solution for fast and accurate porosity prediction in LBW manufacturing.

Keyhole instability is a critical challenge in high-power laser beam welding as it can induce defects such as porosity, spatter, and spiking. However, conventional methods for evaluating keyhole stability, based on the transient keyhole geometry or keyhole depth variation, are limited in accuracy and statistical significance. To address this, a novel evaluation framework from a statistical perspective is proposed in this paper. Oscillating magnetic fields were employed as an active control strategy to generate different levels of keyhole stability, thereby validating the applicability and effectiveness of the proposed framework under different conditions. This method is developed based on a transient three-dimensional multi-physics coupled model incorporating with oscillating magnetic fields. By calculating the equivalent keyhole diameter based on the gas phase area at each discrete layer, the two dimensional keyhole morphology on each layer is reduced to a one dimensional diameter, which is then used to quantify keyhole stability. The spatial average of the keyhole diameter standard deviation is proposed as a metric to quantify keyhole stability, providing a multi-dimensional and statistically robust assessment. Using this novel approach, it is demonstrated that the application of oscillating magnetic fields can significantly enhance keyhole stability, with an improvement of up to 17.5% at 280 mT compared to the reference case. This provides direct statistical evidence that magnetic fields can stabilize the keyhole. Furthermore, a clear and statistically meaningful time-averaged keyhole morphology has not yet been obtained. A time-averaged keyhole reconstruction method is proposed to investigate the time-averaged keyhole morphology. The reconstructed profiles, over a 250 ms time span, exhibit banana-like shapes and capture trailing tails at the keyhole bottom.

Optimization for large industrial parts manufactured with directed energy deposition using a thermomechanical simulation is time-intensive, due to the complex thermomechanical interactions and high deposition rates necessitating small time steps and element sizes. This work develops a layer-dependent inherent strain method using the minimum thermal strain extracted from a thermomechanical simulation for distortion prediction, employing the thermal shrinkage as the predominant driver of distortion in welding processes. The method has been tested on a wall with 40 mm height and a cylinder, achieving 94.1% accuracy, improving predictions by 15% compared to simulations using a constant inherent strain tensor. The approach enables fast distortion prediction, while the thermal strain correlation enables future parameter estimations without prior thermomechanical simulation.

Abstract This paper showcases how a holistic approach to digitalisation enables data-driven welding applications, exemplarily for a gas metal arc welding (GMAW) laboratory. The workflow integrates advanced process monitoring, synchronised multi-sensor data acquisition and tools for data analytics. A welding domain-specific data exchange format weldx is presented that unifies and aggregates the data sets acquired during process monitoring with final component quality metrics, supporting reuse, traceability, and reproducibility. Two case studies illustrate the approach. First, GMAW parameters are adaptively adjusted according to local seam geometry to compensate joint-preparation deviations from nominal values typical for large-scale steel fabrication. Second, the seamless data aggregation along the welding production chain enables an automatic life-cycle assessment (LCA), quantifying the environmental impacts of additive manufacturing with DED Arc/M and attributing the dominant contributors to the carbon footprint. Collectively, the results indicate that a fully integrated experimental set-up together with standardised data structures and scalable analytics can couple monitoring, control, and sustainability, thereby realising the potential of digitalisation for high-quality and environmentally informed welding production.

Nickel–Titanium (NiTi) alloys exhibit high thermal sensitivity during Powder-based Laser Directed Energy Deposition (DED-LB/p), while quantitative process–geometry relationships remain insufficiently understood. This study employed Response Surface Methodology (RSM) to investigate the combined influence of laser power and preheating temperature, representing the interlayer temperature during layer-wise deposition, on the morphology and Powder Catchment Efficiency (PCE) of deposited single tracks. The results show that laser power is the dominant factor governing dilution and PCE, whereas the interaction between laser power and preheating temperature controls the final track morphology. Increasing laser power generally increased track width, dilution, and PCE; however, non-linear geometric behavior was observed at intermediate preheating temperatures, indicating complex melt pool dynamics. Consequently, it was shown that achieving high component quality and geometric precision necessitates strict control of the chamber atmosphere to avoid oxidation and the integration of comprehensive control models capable of modulating laser power in response to dynamic interlayer thermal states.

The weld pool and keyhole geometries are critical characteristics in evaluating the stability of the high-power laser beam welding (LBW) process and determining the resultant weld quality. However, obtaining these data through experimental or numerical methods remains challenging due to the difficulties in experimental measurements and the high computational demands of numerical modelling. This paper presents a physics-informed generative approach for predicting weld pool and keyhole geometries in the LBW process. With the help of a well experimentally validated numerical model considering the underlying physics in the LBW, the geometries of the weld pool and keyhole under various welding conditions are calculated, serving as the dataset of the generative model. A conditional variational autoencoder model is employed to generate realistic 2D weld pool and keyhole geometries from the welding parameters. We utilize a beta-variational autoencoder model with the evidence lower bound loss function and include Kullback-Leibler divergence annealing to better optimize model performance and stability during training. The generated results show a good agreement with the ground truth from the numerical simulation. The proposed approach exhibits the potential of physics-informed generative models for a rapid and accurate prediction of the weld pool geometries across a diverse range of process parameters, offering a computationally efficient alternative to full numerical simulations for process optimization and control in laser beam welding processes.

Powder properties are considered a key factor in mechanical properties in laser additive manufacturing, although few studies have investigated the effects in laser beam directed energy deposition for metal materials (DED-LB/M). Water atomized (WA), and gas atomized (GA) powders are frequently used but may result in different part properties due to powder properties. To examine their qualification for DED-LB/M, this work examines powders and mechanical properties of AISI 316 L. Also, examination techniques are compared. The results show that the powder production has no relevant influence on porosity and Archimedian density of built parts. WA powders show good processability in the process, despite unfavorable morphology. In contrast, WA specimen reach only 10% fracture elongation in tensile testing whereas GA-based specimen achieve 30%. Tensile strength of both is above 500 MPa. The reason for the lower mechanical property values can be attributed to defects and oxides. Yet WA powders may provide a cost-effective alternative for DED-LB/M when a reduced fracture elongation is acceptable.

Laser-based wire directed energy deposition (DED-L/W) is a promising process for copper components, but its environmental performance is not yet well understood. This study presents a gate-to-gate life cycle assessment (LCA) of DED-L/W for two copper alloys, CuSi3 and CuSn, using a 0.21 kg cylindrical part as the functional unit. CuSi3 achieved a higher deposition rate and shorter build time compared to CuSn resulting in lower environmental impacts. Across all categories, alloy production is a major contributor. For GWP100, the shielding gas argon plays a major role. The environmental impact of CuSn could be reduced by using a hypothetical 6.4 kW laser, shortening the process time and lowering gas usage, while the specific energy demand per gram remained nearly constant. The results indicate that for DED-L/W with copper alloys, increasing deposition rate and optimizing shielding gas consumption are more effective levers for reducing environmental impacts than minimizing nominal electrical power.

Tandem gas metal arc welding (T-GMAW) utilizes simultaneous deposition from two wires to enhance the productivity for joining thick sections. Current knowledge on the actual energy consumption vis-à-vis filler wire deposition rate in T-GMAW is limited. We present here a detailed investigation on multi-pass single V-groove T-GMAW of a 30 mm thick structural steel plate with real-time monitoring of current, voltage and metal transfer modes for both filler wires. A novel electrical deposition efficiency (EDE) metric is realized using measured current and voltage transients to correlate electrical energy usage with the deposition rate. For a constant wire feed rate, the short-circuiting metal transfer mode resulted in much lesser energy input and 50% higher EDE in comparison to the pulsed mode of metal transfer.

In high-power laser beam welding, a common phenomenon is the formation of a keyhole caused by the rapid evaporation of the material. Under atmospheric pressure, this evaporation generates a vapor plume that interacts with the laser beam, leading to energy attenuation and scattering of the laser radiation along its path. These interactions affect the stability of the process and the overall weld quality. This study investigates the influence of the vapor plume on the weld pool and keyhole dynamics during high-power laser beam welding of AlMg3 aluminum alloy through experimental and numerical approaches. The primary goal is to identify key vapor plume characteristics, particularly its length fluctuations, and to improve the accuracy of the numerical models. To achieve this, an algorithm was developed for the automated measurement of the vapor plume length using high-speed imaging and advanced data processing techniques. The measured plume length is then used to estimate additional vapor heating and laser energy attenuation using the Beer–Lambert law. A refined numerical CFD model, incorporating 3D transient heat transfer, fluid flow, and ray tracing, was developed to evaluate the vapor plume's impact. Results show that already the time-averaged plume length effectively captures its transient influence and aligns well with experimental weld seam geometries. Additionally, energy scattering and absorption caused by the vapor plume led to a wider weld pool at the top surface. The study also shows an increased percentage of keyhole collapses due to the reduced laser power absorption at the keyhole bottom, further highlighting the importance of accurately modeling vapor plume effects.

Accurate prediction of the weld pool and keyhole geometries in laser beam welding (LBW) is crucial for ensuring high weld quality and optimizing process parameters. However, experimental and numerical approaches face significant challenges because of the complexity of multiphysics interactions and the associated high computational cost. In this study, we propose a novel conditional latent diffusion model (CLDM) to efficiently generate high-fidelity 2D profiles of weld pool and keyhole geometries based on welding parameters. The proposed CLDM combines a variational autoencoder for dimensionality reduction with a conditional diffusion model operating in an eight-dimensional latent space, addressing limitations such as the unstable training and mode collapse of generative adversarial networks and the over-smoothed geometries produced by conventional variational autoencoders. The model is trained using two-dimensional weld pool and keyhole profiles obtained from an experimentally validated multiphysics numerical model. The generated profiles agree well with the numerical ground truths, with correlation coefficients for weld pool length and width of 0.98 and 0.97, respectively. Once trained, the CLDM can generate multiple weld pool and keyhole profiles within minutes, demonstrating its potential as an efficient surrogate for rapid parameter screening, process optimization, and weld quality control.

During the production of ship propellers, considerable quantities of grinding chips from nickel aluminium bronze are produced. This paper examines the mechanical comminution of such chips via impact whirl milling and utilization of two chip-powder batches as feedstock for a laser-based directed energy deposition process. The materials are characterized via digital image analysis, standardized flowability tests, scanning electron microscopy and energy dispersive X-ray spectroscopy and are compared to conventional, gas atomized powder. The specimens deposited via directed energy deposition are analyzed for density, hardness and microstructure and tensile properties for vertical and horizontal build up directions are compared. At elevated mill rotation speeds, the comminution with impact whirl milling produced rounded particles, favorable flow properties and particle size distribution, making them suitable to deposit additive specimens. The microstructure exhibited characteristic martensitic phases due to the high cooling rates of the additive manufacturing process. The presence ceramic inclusions was observed in both the powder and on the tensile fracture surfaces, partly impairing mechanical properties. However, specimens in the vertical build-up direction (Z) showed competitive tensile results, with 775 MPa in tensile strength, 455 MPa in yield strength and 12.6 % elongation at break. The findings of this study indicate that recycling of machining chips to additive manufacturing feedstock can be a viable option for reducing material costs and environmental impact.

The laser welding process is an important manufacturing technology for metallic materials. However, its application is often hindered by the occurrence of porosity defects. By far, an accurate prediction of the porosity defects and an insight into its formation mechanism are still challenging due to the highly nonlinear physics involved. In this paper, we propose a physics-informed deep learning (PIDL) framework by utilizing mechanistic modeling and experimental data to predict the porosity level during laser beam welding of aluminum alloys. With a proper selection of the physical variables (features) concerning the solidification, liquid metal flow, keyhole stability, and weld pool geometry, the PIDL model shows great superiority in predicting the porosity ratio, with a reduction of mean square error by 41 %, in comparison with the conventional DL model trained with welding parameters. Furthermore, the selected variables are fused into dimensionless features with explicit physical meanings to improve the interpretability and extendibility of the PIDL model. Based on a well-trained PIDL model, the hierarchical importance of the physical variables/procedures on the porosity formation is for the first time revealed with the help of the Shapley Additive Explanations analysis. The keyhole ratio is identified as the most influential factor in the porosity formation, followed by the downward flow-driven drag force, which offers a valuable guideline for process optimization and porosity minimization.

With the advancement of machine learning, many predictions and measurements in visual tasks can be achieved by convolutional neural networks (CNNs). Solidification hot cracking is a significant defect in laser beam welding, commonly encountered in practical applications. Existing theories indicate that the formation of cracks is closely related to strain accumulation near the solidification front. In this paper, we first leverage supervised regression networks to design CNNs that achieve real-time average strain estimation for each frame in the collected welding videos. Two different architectures are proposed and compared: the first model stacks two frames at a set interval and feeds them into the network, while the second model extracts image features individually and predicts the results by calculating the correlation between them. Each network has its own advantages in terms of computational efficiency and accuracy. Finally, we further train a multilayer perceptron (MLP) classification model that can detect the occurrence of cracks based on the predicted strain behaviors.

Laser hybrid welding presents several challenges when used to weld thick steels. A typical weld is divided into the arc-dominated and laser-dominated zone. These zones lead to variations in the mechanical properties of the weld. The laser-dominated zone is of particular importance regarding mechanical properties, notably Charpy impact toughness, due to the high cooling rates and the absence of filler wire. The low heat input of the laser can lead to martensitic microstructure causing hardening and deterioration of impact toughness. The high heat input of the arc can lead to grain coarsening and even loss of impact toughness. This study examines the influence of heat input on the cooling rates, microstructure and mechanical properties of single-pass laser hybrid welded steels of S355J2 and EH36 with thicknesses up to 30 mm. The experiments were performed with a 20-kW fibre laser and a contactless electromagnetic weld backing in the butt-joint configuration in 1G welding position. The cooling time was measured in three different locations near the fusion lines corresponding to different heights of the seam using a special configuration with pyrometers, collimators and optical fibres. The test specimens for the Charpy impact testing and tensile testing were extracted in three different depths. The experiments indicated that a heat input of 1.6 kJ/mm–2 kJ/mm, 2 kJ/mm–2.4 kJ/mm and 3.7 kJ/mm were recommended when single-pass laser hybrid welding of 20-, 25-, and 30-mm-thick structural steels regarding the minimum requirements of the mechanical properties, respectively.

A phase-field model including magnetic field induced dendrite fragmentation was established and applied to the cases with different initial crystal nuclear positions for AA5754 aluminum alloy electromagnetic laser beam welding. Compare the calculated results that include dendrite fragmentation caused by the thermal electromagnetic Lorentz force with the results that consider only the thermal electromagnetic Lorentz force, without fragmentation, at the characteristic time instants. Both in the early and late stages, the small fragmentation at the dendrite tip promotes the number of higher-order branches and their growth, especially in the direction perpendicular to the solidification. The later stage fragmentation has the possibility of breaking one grain into several, which verifies the possibility of grain refinement caused by dendrite fragmentation. The fracture surface caused by fragmentation also makes more solid-liquid interfaces and their growth. In addition, the cases with different initial nuclear positions were compared. The grain growth in the low-temperature zone can be inhibited by the equiaxed grains' fragmentation at the high-temperature area (179.8 μm² and 14.7% start at the center, 115.4 μm² and 9.4% start at the high-temperature corner, 134.3 μm² and 10.9% start at the low-temperature corner), which is another kind of grain refinement by the dendrite fragmentation. This kind of inhibition effect on grain growth in the low-temperature region will be enhanced with the increasing time interval between the two crystal nuclei’ appearance (179.8 μm² and 14.7% when virtual grains appear at t = 4.3803s and t = 4.3803s, 134.3 μm² and 10.9% at t = 4.0977s and t = 3.9564s, and 115.4 μm² and 9.4% at t = 3.8151s and t = 3.5325s).