The drying of lithium-ion battery electrodes is one of the most energy- and cost-intensive processes within the battery production chain. Recently, laser drying has emerged as a promising energy-efficient alternative to the conventional convective and infrared drying methods due to its direct energy input, high energy density and superior controllability. However, the underlying process mechanism remain incompletely understood, and its full potential has yet to be reached. In this work, a numerical process model was developed to simulate the changes in evaporation rates, film thickness as well as the film tempera-tures, while also estimating the process duration and energy consumption for both laser and convective drying methods. Comparative analysis was made between two drying methods in terms of their process durations and energy consumptions. Furthermore, the process disturbance in the coating and drying processes was addressed, and a concept of model-based process control was developed.

The most reported approaches for controlling the laser welding process are the application of data-driven proportional-integral-derivative (PID) controllers. These methods allow controlling the desired weld quality using direct or indirect measurements of the melt pool as feedback signals. However, these approaches fail to generalise away from this training data. With the raise of scientific machine learning, physics-informed neural networks (PINNs) for control systems is gaining popularity. The fundamental idea is to embed the governing equations of the laser welding process into the machine learning model, i.e., a specified neural network. This paper aims to critically review current developments of PINNs for real-time control of the laser welding process. The discussion will highlight the accuracy of prediction, generalisation to un-seen events and computational latency. A series of use cases with a moving Gaussian beam and a set of shaped beams will be presented to support the findings.

The growth of e-mobility has driven a rising demand for copper welding, a process made difficult by copper’s high reflectivity and thermal conductivity, which often lead to defects like porosity and spattering. Beam shaping enhances weld quality by optimizing energy distribution. Multi-Plane Light Conversion technology offers exceptional flexibility in beam shaping, making it essential to determine the most effective beam configurations.

This study explores multi-physics simulations of various beam shapes, including asymmetric designs, and their impact on key weld seam characteristics such as capillary stability and surface finish. We will outline the process for identifying an optimal beam shape, examine how to adapt it into a manufacturable design, and discuss criteria for evaluating its performance against other configurations, including symmetric shapes and unshaped beams.

During laser welding, droplets can detach from the melt pool, which may impair the surface quality or the mechanical properties of the weld due to lack of material. Many techniques to reduce spatter are either increasing the melt pool size to reduce melt flow velocities, or increasing the capillary aperture size to reduce the vapor flow velocity. However, these strategies add complexity to the welding setup and reduce its flexibility. This work applies coherent beam combining technology in order to optimize the intensity distribution during laser welding of AISI 304 stainless steel at 12 m/min welding speeds with respect to the successfully deployed optimization presented in literature. The formation of spatter during the welding process was captured by means of high-speed video recordings. The results show a reduction of spatter by 85%, when welding with the optimized beam shape, compared to a static circular beam shape.

Beam shaping presents promising new horizons for laser materials processing. In particular, the potential to manipulate heat and mass flow during welding and additive manufacturing processes. In this work, we explore whether static beam shaping can be used to control solidification conditions by modulation of local heat and mass flows computed by fluid dynamics. By manipulating beam profile, we explore the interplay of simultaneous beam shaping and process parameter optimization to influence the thermal field and solidification parameters, G and R. The CIVAN laser system offers the ability to control both static and dynamic beam shape properties and thus offers an enormous parameter space to explore. We present a simple taxonomy of beam shape topologies and symmetries and then use it to constrain and systematize our shape space exploration. We control the shape space exploration using a Monte Carlo based trial shapes and test them in a fluid dynamics simulation.

A novel measurement method was developed to indirectly measure the absorptivity of liquid metals, including its dependency on the angle of incidence and the temperature. For the method a small metal piece is molten. Surface tension forms the liquid metal to a spheroid droplet. The thermal emission of the droplet is recorded at the desired wavelength after a linear polarizer with a camera without any probe beam. The refractive index n and extinction coefficient k can be determined by analyzing the intensity of the p-polarized thermal emission over the curvature of the sphere. The temperature was determined via quotient pyrometry, which was included in the same optical setup. In the talk we show the successful application of the method for the determination of the absorptivity characteristics for liquid copper at a wavelength of 850 nm.