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In the production of hybrid composites made of thermoplast and metal sheets, certain limitations in the linear forming process can be overcome through a free kinematic forming process. This process involves distributing the thermoplast press mass using a tumbling or cyclic rolling movement of the forming tool. Due to this special kinematics, only a small part of the tool is ever in engagement with the workpiece, so the component is formed incrementally. Therefore, the final geometry does not have to be created in one stroke but can be carried out step by step in several partial strokes. In order to determine the optimal trajectory of the forming tool, the kinematic interplay between the workpiece, forming tool, and toolpath should be numerically captured in a mathematical model. The specific toolpath depends on the component being formed, as well as the actual geometry of the forming tool. In this research, the free kinematic forming process is modeled and the optimal toolpath is determined using a genetic algorithm. The optimal toolpath enables a uniform distribution of the thermoplast to a specified thickness and collisions between the upper and lower tool are avoided. This enables key insights into the free kinematic forming process and opens up further avenues of research for this innovative and future‐oriented manufacturing concept. [ABSTRACT FROM AUTHOR]

Hybrid structures are increasingly relevant for lightweight design and functional components. Various manufacturing techniques for creating hybrid components exist, often focusing on combining metal and polymer components. The present study examines a novel approach for manufacturing hybrid functional structures by directly printing thermoplastic onto aluminum profiles using closed‐cell aluminum foam, with the mechanical interlocking resulting in a structural bond. The filling of pores in the aluminum foam with the polymer affects the bond strength of this hybrid compound. Within a simulation‐based process design the additive manufacturing process parameters are investigated using a computational fluid dynamics (CFD) model with Fluid‐Structure Interaction for the pore‐filling process and a finite element (FE) model to evaluate the resulting bond strength. The material throughput, the nozzle distance, and the polymer temperature are the key process parameters taken into account. A parameterized, virtual foam model is used within this framework, where a linear movement of the extruder nozzle and the resulting polymer strand is investigated. This method includes the mesh conversion from the CFD results to the FE mesh for structural analysis by exporting the iso‐surface for the polymer geometry embedded in the pores of the aluminum foam structure. This approach made it possible to investigate the phenomena that occur in the process in more detail, particularly to quantify the influence of air inclusions. Thus, a decisive contribution to the hybridization of aluminum foams has been made, which opens up further potential for research and application in the long term. [ABSTRACT FROM AUTHOR]

In the design and production of press part components, the tool development process is an essential step to fulfil the required quality criteria. Conventionally, this is achieved based on expert knowledge to design the effective surface with respect to the multitude of physical, procedural, and human influences. Thus, several iterations in the tool development process are usually required, which are costly and can lead to bottlenecks within product design cycles. To accelerate this tool design, we propose a diffusion model architecture to inversely design the necessary effective tool surface given a desired geometry of the press part. This diffusion model is able to reduce the generalization issues of classical machine learning approaches by leveraging the attention mechanism both in the spatial and temporal dimension of the underlying forming process. The applicability of a similar diffusion model has already been shown in previous applications for the inverse‐design of metamaterials and this work further demonstrates diffusion models as a suitable model candidate for the inverse‐design of 3D‐geometries. For model training, finite element simulations containing the time series of deformation states during the forming process were used. Furthermore, different geometry variations of part and tool as well as relevant press process parameters were used in the training. With the procedure demonstrated in this study, a future‐oriented support for the tool development process has been shown, enabling further developments towards a time‐ and cost‐efficient production of press tools. [ABSTRACT FROM AUTHOR]

Car manufacturers are currently challenged with increasing the sustainability of their products and production to comply with sustainability requirements and legislation. One way to enhance product sustainability is by reducing the carbon footprint of fossil-based plastic parts. Particle foams are a promising solution to achieve the goal of using lightweight parts with minimal material input. Ongoing developments involve the use of expanded particle foam beads made from engineering plastics such as polyamide (EPA). To achieve this, a simulated life cycle was carried out on virgin EPA, including mechanical recycling. The virgin material was processed into specimens using a steam-free method. One series was artificially aged to replicate automotive life cycle stresses, while the other series was not. The mechanical recycling and re-foaming of the minipellets were then carried out, resulting in an EPA particle foam with 100% recycled content. Finally, the thermal and chemical material properties were comparatively analysed. The study shows that the recycled EPA beads underwent polymer degradation during the simulated life cycle, as evidenced by their material properties. For instance, the recycled beads showed a more heterogeneous molecular weight distribution (an increase in PDI from two to three), contained carbonyl groups, and exhibited an increase in the degree of crystallization from approximately 24% to 36%. [ABSTRACT FROM AUTHOR]