Search Results (8)

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. Full article

Additive manufacturing processes such as the material extrusion of metals (MEX/M) enable the production of complex and functional parts that are not feasible to create through traditional manufacturing methods. However, achieving high-quality MEX/M parts requires significant experimental and financial investments for suitable parameter development. In response, this study explores the application of machine learning (ML) to predict the surface roughness and density in MEX/M components. The various models are trained with experimental data using input parameters such as layer thickness, print velocity, infill, overhang angle, and sinter profile enabling precise predictions of surface roughness and density. The various ML models demonstrate an accuracy of up to 97% after training. In conclusion, this research showcases the potential of ML in enhancing the efficiency in control over component quality during the design phase, addressing challenges in metallic additive manufacturing, and facilitating exact control and optimization of the MEX/M process, especially for complex geometrical structures. Full article

Additive manufacturing (AM) technologies have witnessed remarkable advancements, offering opportunities to produce complex components across various industries. This paper explores the potential of AM for fabricating bipolar plates (BPPs) in fuel cell or electrolysis cell applications. BPPs play a critical role in the performance and efficiency of such cells, and conventional manufacturing methods often face limitations, particularly concerning the complexity and customization of geometries. The focus here lies in two specific AM methods: the laser powder bed fusion of metals (PBF-LB/M) and material extrusion of metals (MEX/M). PBF-LB/M, tailored for high-performance applications, enables the creation of highly complex geometries, albeit at increased costs. On the other hand, MEX/M excels in rapid prototyping, facilitating the swift production of diverse geometries for real-world testing. This approach can facilitate the evaluation of geometries suitable for mass production via sinter-based manufacturing processes. The geometric deviations of different BPPs were identified by evaluating 3D scans. The PBF-LB/M method is more suitable for small features, while the MEX/M method has lower deviations for geometrically less complex BPPs. Through this investigation, the limits of the capabilities of these AM methods became clear, knowledge that can potentially enhance the design and production of BPPs, revolutionizing the energy conversion and storage landscape and contributing to the design of additive manufacturing technologies. Full article

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. Full article

Additively manufactured mechanical components show great lightweight characteristics and can often be enhanced by integrating biomimetic geometrical features. This study focuses on one specific subcase, namely the substitution of solid cylindrical beams that are under bending with geometrically more complex biomimetic beams. Based on the pseudo-stem of the banana plant as a role model, six geometric beam designs were derived. Given the manufacturing constraints of the PBF-LB/M process, two abstractions were selected for detailed investigation in the main part of this study. The beam lengths were set to 100 mm. Based on parametric optimization simulations, optimal design parameters were identified for the two biomimetic abstractions for 26 different bending load cases ranging from 14 to 350 Nm. Analogous parameter optimizations were performed for a solid cylindrical beam design, which was used as a reference. The results provide detailed design solutions within the investigated intervals for biomimetic beams that can be substituted into more complex mechanical component designs with ease. The analysis provides information on which structures to use for the investigated loads. With the help of the developed numerical models, designers can easily generate biomimetic beam designs for specific bending load values. Full article

Metal binder jetting shows great potential for medical technology. This potential can be exploited by integrating binder jetting into existing process routes known from metal injection molding. The biggest challenge here is the flowability and packing behavior of the powders used, due to their low size distributions. This paper investigates different powder-drying strategies to improve flowability using a statistical experimental design. Because of its relevance for medical applications, spherical Ti-6Al-4V powder with a size distribution under 25 µm is dried under various parameters using vacuum and gas purging. The investigated parameters, time and temperature, are selected in a central-composite-circumscribed test plan with eleven tests and three center points. The target parameters—water content, flowability and impurity levels (oxygen, nitrogen)—of the powder are analyzed. For validation, practical test trials are carried out on an industrial binder jetting system with unconditioned powder and conditioning with optimized parameters, comparing the manufactured parts and the powder bed. An optimized drying cycle with a duration of 6 h at 200 °C was determined for the investigated powder. Significant improvements in the dimensional accuracy (from ±1.5 to 0.3%) of the components and the visual impression of the powder bed are demonstrated. Full article

Piston-based material extrusion enables cost savings for metal injection molding users when it is utilized as a complementary shaping process for green parts in small batch sizes. This, however, requires the use of series feedstock and the production of sufficiently dense green parts in order to ensure metal injection molding-like material properties. In this paper, a methodological approach is presented to identify material-specific process parameters for an industrially used Ti-6Al-4V metal injection molding feedstock based on the extrusion force. It was found that for an optimum extrusion temperature of 95 °C and printing speed of 8 mm/s an extrusion force of 1300 N ensures high-density green parts without under-extrusion. The resulting sintered part properties exhibit values comparable to metal injection molding in terms of part density (max. 99.1%) and tensile properties (max. yield strength: 933 MPa, max. ultimate tensile strength: 1000 MPa, max. elongation at break: 18.5%) depending on the selected build orientation. Thus, a complementary use could be demonstrated in principle for the Ti-6Al-4V feedstock. Full article

Today’s smart devices come equipped with powerful hard- and software-enabling professional use cases. The latest hardware by Apple utilizes LiDAR and TrueDepth, which offer the capability of 3D scanning. Devices equipped with these camera systems allow manufacturers to obtain 3D data from their customers at low costs, which potentially enables time-efficient mass customization and product differentiation strategies. However, the utilization is limited by the scanning accuracy. To determine the potential application of LiDAR and TrueDepth as a 3D scanning solution, in this paper an evaluation was performed. For this purpose, different Lego bricks were scanned with the technologies and an industrial 3D scanner. The results were compared according to shape and position tolerances. Even though the industrial 3D scanner consistently delivered more accurate results, the accuracy of the smart device technologies may already be sufficient, depending on the application. Full article