In aerospace, thermal applications demand compact, lightweight, and efficient heat exchangers. Additive manufacturing processes offer the potential to create highly complex structures that are not achievable through traditional manufacturing methods. This work presents the development of an additively manufactured fluid-fluid heat exchanger that shows the potential to enhance the performance, reduce weight, and increase compactness compared to a conventional plate heat exchanger. A numerical model of the conventional plate heat exchanger was created, and fluid dynamics simulations with heat transfer were performed. Validation of the simulations was done by experiments. Then, a novel heat exchanger was designed using a bottom-up approach and investigated at different levels of complexity using computational fluid dynamics. The internal structure of the final heat exchanger consists of a repeating triply periodic Schwarz diamond minimum surface elongated in the direction of flow. The heat exchanger was manufactured with laser powder bed fusion process using AlSi10Mg. It had a 108% higher compactness and 54% lower weight compared to the plate heat exchanger. Numerical analysis yielded the pressure loss in pascal was reduced by 50%–59% while heat transfer in watts was improved by 3%–5%. Future researches should experimentally investigate the thermal and fluid mechanical characteristics of the novel additively manufactured heat exchanger and increase compactness and heat transfer further by analyzing the minimum partition wall thickness and the impact of wall roughness and deposit formation.

Additive manufacturing (AM) and topology optimization (TO) emerge as vital processes in modern industries, with broad adoption driven by reduced expenses and the desire for lightweight and complex designs. However, iterative topology optimization can be inefficient and time-consuming for individual products with a large set of parameters. To address this shortcoming, machine learning (ML), primarily neural networks, is considered a viable tool to enhance topology optimization and streamline AM processes. In this work, a machine learning (ML) model that generates a parameterized optimized topology is presented, capable of eliminating the conventional iterative steps of TO, which shortens the development cycle and decreases overall development costs. The ML algorithm used, a conditional generative adversarial network (cGAN) known as Pix2Pix-GAN, is adopted to train using a variety of training data pairs consisting of color-coded images and is applied to an example of cantilever optimization, significantly enhancing model accuracy and operational efficiency. The analysis of training data numbers in relation to the model’s accuracy shows that as data volume increases, the accuracy of the model improves. Various ML models are developed and validated in this study; however, some artefacts are still present in the generated designs. Structures that are free from these artefacts achieve 91% reliability successfully. On the other hand, the images generated with artefacts may still serve as suitable design templates with minimal adjustments. Furthermore, this research also assesses compliance with two manufacturing constraints: the limitations on build space and passive elements (voids). Incorporating manufacturing constraints into model design ensures that the generated designs are not only optimized for performance but also feasible for production. By adhering to these constraints, the models can deliver superior performance in future use while maintaining practicality in real-world applications.

Lattice structures in additive manufacturing of 316L stainless steel have gained increasing attention due to their well-suited mechanical properties and lightweight characteristics. Infill structures such as honeycomb, lattice, and gyroid have shown promise in achieving desirable mechanical properties for various applications. However, the design process of these structures is complex and time-consuming. In this study, we propose a machine learning-based approach to optimize the design of honeycomb, lattice, and gyroid infill structures in 316L stainless steel fabricated using laser powder bed fusion (L-PBF) technology under different loading conditions. A dataset of simulated lattice structures with varying geometries, wall thickness, distance, and angle using a computational model that simulates the mechanical behavior of infill structures under different loading conditions was generated. The dataset was then used to train a machine learning model to predict the mechanical properties of infill structures based on their design parameters. Using the trained machine learning model, we then performed a design exploration to identify the optimal infill structure geometry for a given set of mechanical requirements and loading conditions. Finally, we fabricated the optimized infill structures using L-PBF technology and conducted a series of mechanical tests to validate their performance under different loading conditions. Overall, our study demonstrates the potential of machine learning-based approaches for efficient and effective designing of honeycomb, lattice, and gyroid infill structures in 316L stainless steel fabricated using L-PBF technology under different loading conditions. Furthermore, this approach can be used for dynamic loading studies of infill structures.

Proton exchange membrane fuel cells (PEMFCs) represent today one of the most common types of fuel cells for mobility applications due to their comparatively high-power density, low operating temperature, and low costs. A PEMFC regularly consists of a stack of individual cells in which each consists of polar plates and a membrane electrode assembly. To achieve the best possible electric conductivity over the series connection of cells, the contact pressure in between the cells must be uniformly distributed over the cell area. This pressure is usually applied to the stack by end plates, which frame the stack and are clamped together by bolts, which are tightened by a defined torque. Typically, these end plates are made from bulk material with no or limited optimization. Looking at mobility applications, e.g., in aerospace, a fuel cell should ideally provide high efficiency at the lowest weight. Based on this assumption, this paper uses topology optimization varying the material as well as the design space to derive new design concepts for the end plates of a PEMFC. The designs are compared with respect to an even stress distribution to the fuel cell stack, the weight of the plates, and the manufacturability in the laser powder bed fusion process. The most promising design is manufactured and results in a weight decrease of 48% compared to previously used aluminum bulk plates. Finally, the optimized base plates are applied to a test cell and the performance is compared to their conventional counterparts, showing a 1% increase in electric stack power despite the lower mass.

This study introduced a systematic framework to develop practical design guidelines specifically for filament-based material extrusion of metals (MEX/M), an additive manufacturing (AM) process defined by ISO/ASTM 52900. MEX/M provides a cost-efficient alternative to conventional manufacturing methods, which is particularly valuable for rapid prototyping. Although AM offers significant design flexibility, the MEX/M process imposes distinct geometric and process constraints requiring targeted optimization. The research formulates and validates design guidelines tailored for the MEX/M using an austenitic steel 316L (1.4404) alloy filament. The feedstock consists of a uniform blend of 316L stainless steel powder and polymeric binder embedded within a thermoplastic matrix, extruded and deposited layer by layer. Benchmark parts were fabricated to examine geometric feasibility, such as minimum printable wall thickness, feature inclination angles, borehole precision, overhang stability, and achievable resolution of horizontal and vertical gaps. After fabrication, the as-built (green-state) components undergo a two-step thermal post-processing treatment involving binder removal (debinding), followed by sintering at elevated temperatures to reach densification. Geometric accuracy was quantitatively assessed through a 3D scan by comparing the manufactured parts to their original CAD models, allowing the identification of deformation patterns and shrinkage rates. Finally, the practical utility of the developed guidelines was demonstrated by successfully manufacturing an impeller designed according to the established geometric constraints. These design guidelines apply specifically to the machine and filament type utilized in this study.

Improving mechanical topology optimization (TO) results by substituting biomimetic beams is one possibility to achieve designs of mechanical components that are highly sustainable and show good mechanical performance. Because of their geometric complexity, such designs were found to be well-suited for production by laser additive manufacturing. One obstacle of incorporating biomimetics beams in TO designs is the lack of detailed design methodologies. Röver et al. [“Methodology for integrating biomimetic beams in abstracted topology optimization results,” in Proceedings of the ASME 2022 International Mechanical Engineering Congress and Exposition. Volume 4: Biomedical and Biotechnology; Design, Systems, and Complexity Columbus, OH, 30 October–3 November (ASME, New York, 2022)] proposed a corresponding design concept. Building on their concept, we present in this work a detailed methodology for abstraction of TO results to a design consisting of ball nodes and cylindrical beams. Using such an auxiliary design, the internal forces and moments of the beams can be evaluated to allow for the substitution of suitable biomimetic beams to generate biomimetic component designs in a next step. We present a skeletonization algorithm based on the potential field approach. Using the skeletonization and an additional analysis of the dimensions of the beams in the TO result, the algorithm develops an auxiliary design of the original TO result. The final algorithm was applied to three common TO results to obtain one auxiliary component design each. The developed algorithm was found to generate abstractions that were well-suited for use in the methodology proposed in Röver et al. [“Methodology for integrating biomimetic beams in abstracted topology optimization results,” in Proceedings of the ASME 2022 International Mechanical Engineering Congress and Exposition. Volume 4: Biomedical and Biotechnology; Design, Systems, and Complexity Columbus, OH, 30 October–3 November (ASME, New York, 2022)], because internal forces and moments in the abstracted beams could be evaluated with less effort. Therefore, our work contributes to a detailed design methodology for biomimetic mechanical components in the field of design for additive manufacturing.