Wire arc additive manufacturing (WAAM) is a direct energy deposition (DED) process with high deposition rates, but deformation and distortion can occur due to the high energy input and resulting strains. Despite great efforts, the prediction of distortion and resulting geometry in additive manufacturing processes using WAAM remains challenging. In this work, an artificial neural network (ANN) is established to predict welding distortion and geometric accuracy for multilayer WAAM structures. For demonstration purposes, the ANN creation process is presented on a smaller scale for multilayer beads on plate welds on a thin substrate sheet. Multiple concepts for the creation of ANNs and the handling of outliers are developed, implemented, and compared. Good results have been achieved by applying an enhanced ANN using deformation and geometry from the previously deposited layer. With further adaptions to this method, a prediction of additive welded structures, geometries, and shapes in defined segments is conceivable, which would enable a multitude of applications for ANNs in the WAAM-Process, especially for applications closer to industrial use cases. It would be feasible to use them as preparatory measures for multi-segmented structures as well as an application during the welding process to continuously adapt parameters for a higher resulting component quality.

Recycling plays a crucial role in achieving a sustainable production chain for lithium-ion batteries (LIBs), as it reduces the demand for primary mineral resources and mitigates environmental pollution caused by improper disposal. Disassembly of the LIBs is typically the preliminary step preceding chemical recovery operations, facilitating early separation of components consisting of different materials. Despite that extensive research has been conducted on the chemical processes involved in the recycling of LIBs, systematic studies on disassembly processes in the recycling process are relatively scarce. In this research, a systematic review was conducted on the publications from major databases, such as Scopus, SpringerLink, and others, to explore the current state of disassembly processes in LIBs’ recycling. The results emphasize disassembly as a crucial process for achieving a high material separation rate and ensuring a high degree of purity of the recycled active material. Moreover, automated disassembly can significantly raise productivity and reduce disassembly costs. Thus, it improves disassembly efficiency and increases economic as well as environmental benefits. Most researchers have focused on disassembly at the pack or module level. Investigation into extending the disassembly depth from cell to individual components is limited, particularly in automated approaches. Therefore, further research is highly recommended to explore the feasibility and potential of novel automated disassembly procedures at the cell level. This can contribute to improving the efficiency and sustainability of the recycling process for LIBs.

The design and development of composite structures requires precise and robust manufacturing processes. Composite materials such as fiber reinforced thermoplastics (FRTP) provide a good balance between manufacturing time, mechanical performance and weight. In this contribution, we investigate the process combination of thermoforming FRTP sheets (organo sheets) and injection overmolding of short FRTP for automotive structures. The limiting factor in those structures is the bond strength between the organo sheet and the overmolded thermoplastic. Within this process chain, even small deviations of the process settings (e.g., temperature) can lead to significant defects in the structure. A cyber physical production system based framework for a digital twin combining simulation and machine learning is presented. Based on parametric Finite-Element-Method (FEM) studies, training data for machine learning methods are generated and a FEM surrogate is developed. A comparison of different data-driven methods yields information on the estimation accuracy of task-specific data-driven methods. Finally, in accordance with experimental cross tension tests, the investigated FEM surrogate model is able to predict the interface bond strength quality in dependence of the process settings. The visualization into different quality domains qualifies the presented approach as decision support.

Additive manufacturing in construction (AMC) enables new design methods and strategies within the construction industry. In particular, Shotcrete 3D Printing (SC3DP) offers a high degree of design freedom by enabling the deposition of concrete at variable layer orientation based on a wet-mix shotcrete process. However, the mechanical properties and geometry of the printed layers are dependent on the material and process parameters used. In this context, the effects of air and concrete flow rates, path planning parameters, and material parameters have been investigated in previous research. The presented study investigates the influence of the nozzle geometry on the resulting strand properties, e.g. strand geometry, layer bond strength, and compressive strength, to evaluate nozzle diameter and length as control parameters for the SC3DP process. Experimental investigations were performed with fixed nozzle diameters between 10 and 30 mm and nozzle lengths ranging from 100 to 200 mm. The results show a significant influence of the nozzle diameter on the resulting strand geometry as well as the mechanical properties. Finally, concepts for a nozzle with a controllable outlet diameter were developed and evaluated.

Electrolyte filling and wetting is a quality-critical and cost-intensive process step of battery cell production. Due to the importance of this process, a steadily increasing number of publications is emerging for its different influences and factors. We conducted a systematic literature review to identify common parameters that influence wetting behavior in experimental settings, specifically focusing on material, processes, and experimental measurement methods but excluding simulation studies. We reduced the initially found 544 records systematically to 39 fully labeled articles. Our profound analysis guided by attributed labelings revealed current research gaps such as the lack of a holistic view on measurement methods for filling and wetting, underrepresented studies relevant to series production, as well as the negligence of research targeting the transferability of results from the material to the cell level, while also examining the measured variables’ interactions. After comparatively illustrating and discussing implications of our findings, we also discussed limitations of our contribution and suggested ideas for potential further research topics.

Demand for lithium-ion battery cells (LIB) for electromobility has risen sharply in recent years. In order to continue to serve this growing market, large-scale production capacities require further expansion and the overall effectiveness of processes must be increased. Effectiveness can be significantly optimized through innovative manufacturing technology and by identifying scrap early in the production chain. To enable these two approaches, it is imperative to quantify safety- and function-critical product features in critical manufacturing steps through appropriate measurement techniques. The overview in this paper on quality control in LIB production illustrates the necessity for improved inspection techniques with X-rays to realize a fast, online measurement of inner features in large-scale cell assembly with short cycle times and to visualize inner product-process interactions for the optimization in electrolyte filling. Therefore, two new inspection techniques are presented that contribute to overcoming the aforementioned challenges through the targeted use of X-rays. First, based on the results of previous experiments in which the X-ray beam directions were deliberately varied, a online coordinate measurement of anode-cathode (AC) overhang was developed using a line detector. Second, a new concept and the results of a continuous 2D visualization of the electrolyte filling process are presented, which can be used in the future to optimize this time-critical process step. By using a X-ray-permeable and portable vacuum chamber it is possible to quantify the influence of process parameters on the distribution of the electrolyte in the LIB.