Fraunhofer Institute for Production Systems and Design Technology

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.

Abstract Understanding the vaporisation of cutting fluids in machining processes is essential for improving cooling effectiveness and tool life. In order to gain basic knowledge regarding vapour formation, this study presents a combined experimental and numerical investigation of submerged turning of C45 and Inconel 718. High-speed imaging is employed to qualitatively visualise vapour phenomena, while simulations using the Finite Pointset Method (FPM) extend existing models to incorporate a physically realistic vapour phase and fluid–structure interaction. Jet cooling simulations capture the intermittent nature of vapour formation, with shorter time intervals and larger affected areas at higher wall temperatures. Increasing surface velocity shifts vaporisation farther from the stagnation point. Machining simulations indicate that cutting fluids tend to vaporise at a certain distance from the chip root rather than directly at it, while submerged machining experiments reveal a stable vapour region on the chip surface. These results advance the understanding of cooling performance and heat transfer in metal cutting and contribute to the development of more accurate simulation tools for process and tool optimization.

Achieving both safe contact transitions and high-performance motion tracking is essential for robots manipulating in contact-rich environments. Disturbance-observer (DOB) frameworks enable robust motion control, but achieving compliant interaction typically requires additional strategies, and prior work has paid limited attention to how acceleration estimation affects in DOB behavior. In this work, we adopt a second-order sliding-mode observer (SOSML) to obtain noise-robust, low-lag acceleration estimates and integrates them into an Impedance-Aware Disturbance Observer (IADOB). The proposed controller integrates DOB feedback with inner force control to exploit the robot’s natural inertia and rendered stiffness, enabling safe contact transitions without sacrificing motion tracking performance. Experiments on a 7-degree-of-freedom (DoF) Franka Research 3 robot demonstrate improved contact stability, and higher admissible stiffness compared to conventional impedance and momentum observer-based controllers.

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 β-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.

With the introduction of EU Battery Regulation No. 2023/1542, the implementation of digital product passports (DPP) will become mandatory for all electric vehicle, light transport, and industrial batteries from 2027. The planned decentralized system, operated by the European Commission, distributors, and IT service providers, poses significant challenges, especially for small and medium-sized enterprises, due to high system complexity, evolving standards, and tight deadlines. This paper advocates for the development of a freely accessible test environment to support battery distributors and other stakeholders in verifying the conformity of their data, interfaces, and system functions with regulatory and technical requirements. The proposed test platform should enable the definition and execution of modular test procedures, provide emulation of central components, and facilitate robust validation of data structures, interaction protocols, and security mechanisms. By drawing on best practices from standardization projects and industry initiatives, the paper outlines the necessary functionalities, process models, and test types required to ensure compliance and reduce implementation risks. The test environment is intended to aid companies in efficiently meeting regulatory requirements and adapting to future changes, thereby supporting the successful rollout of the DPP system.

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.

How to estimate the economic returns of public science is a longstanding but equally challenging topic in quantitative science studies. In this paper, we exploit the staggered foundation of the SENAI Innovation Institutes (ISI) in Brazil since 2012 to estimate their effects on GDP using a difference-in-differences (DiD) approach. Building on historical and institutional insights from interviews on the foundation process, we unravel the conditions under which the parallel trends assumption is likely to hold. Our analysis reveals that these institutes significantly contribute to GDP per capita, with an average treatment effect of 985 BRL (approximately €160). Moreover, by relying on detailed project-level data, we were able to show that the effects come almost exclusively from genuine research projects and not from the provision of scientific services, such as metrology. Finally, tentative calculations suggest that the SENAI ISI institutes may account for about 0.66% of Brazil’s overall GDP, emphasising the importance of applied science in regional economic development and providing insights into effective collaboration between research and industry. • The paper addresses the economic returns of public science, focusing on the SENAI Innovation Institutes (ISI) in Brazil. • Utilizes a difference-in-differences (DiD) approach to estimate the institutes' impact on GDP since their establishment in 2012. • Highlights the importance of historical and institutional insights in validating the parallel trends assumption. • Findings indicate a significant contribution to GDP per capita, with an average treatment effect of 985 BRL (around €160). • The positive effects are primarily attributed to genuine research projects rather than scientific service provision. • Following macroeconomic projections, SENAI ISI institutes represent approximately 0.66% of Brazil’s overall GDP, underscoring the role of applied science in regional economic development.