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Item type:Publication, Access status: with files , Design guidelines for laser powder bed fusion of triply periodic minimal surface structures for applications in smart reactors(Springer, 2026-01-20); ; ; ; Additive Manufacturing (AM), particularly Laser Powder Bed Fusion (PBF-LB/M), has transformed the production of complex metallic structures, enabling applications in smart reactors where enhanced heat and mass transfer at minimal pressure drop are critical. Triply Periodic Minimal Surface (TPMS) structures, such as Gyroid-TPSf and Schwarz-Diamond-TPSf geometries, offer unique advantages due to their high surface area-to-volume ratios, tunable porosity, and zero mean curvature. However, their manufacturability using PBF-LB/M remains underexplored, especially for demanding applications in process engineering that require structural integrity under extreme conditions. This study investigates the design and manufacturability of TPMS structures using 316L stainless steel via PBF-LB/M, focusing on the interaction of the key parameters porosity, unit cell size, and sheet thickness, of which two are independent variables while the third is a dependent variable. Through numerical simulations, experimental validation, and process optimization, practical design guidelines are developed. In this study, the design parameters of Gyroid-TPSf and Schwarz-Diamond-TPSf samples include porosities ranging from 70 to 90% and unit cell sizes from 2 to 20 mm. The results indicate that specifically, at large unit cell sizes (e.g., 20 mm), the decreased curvature radius reduces self-supporting effects, leading to insufficient mechanical stability during printing and resulting in local deformation. Conversely, at small unit cell sizes combined with high porosity levels (e.g., 2 mm and 90%), the sheet thickness becomes critically thin, often below the printable resolution, resulting in incomplete or fragile structures. CFD simulations were validated against experimental data across various volume flow rates. This work enables a knowledge-based selection of a suitable type of TPMS and its design parameters depending on the required flow characteristics in a given process engineering task while maintaining manufacturability. In conclusion, the study underscores the need for further refinement of design and manufacturing processes to fully exploit their benefits.Publicationtype: Journal Article95 101 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Mechanical properties of additively manufactured AlSi7Mg0.6 and IN718 thin-walled structuresAdditively manufactured heat exchanger cores offer enhanced compactness, thermomechanical robustness, and improved integration within confined assembly spaces. However, their complex thin-walled lattice structures pose significant challenges for mechanical characterization and conventional finite element analysis, due to high computational costs. This study investigates the tensile mechanical properties of grid-like structures produced by powder bed fusion with a laser beam in aluminum alloy AlSi7Mg0.6 and nickel–chromium-based superalloy Inconel 718 (IN718). Samples with varying wall thicknesses were fabricated, heat treated, and some were surface treated to reduce surface roughness. Tensile tests combined with digital image correlation and non-linear finite element modeling were conducted to evaluate mechanical behavior. Experimental results reveal that a minimum solid material proportion (Ps ≈ 0.09 for AlSi7Mg0.6; Ps ≈ 0.06 for IN718) is required to achieve reliable elastoplastic deformation; below these thresholds, premature brittle failure dominates. Equivalent Young’s moduli and yield strength scale linearly with solid material proportion, the latter reaching approximately 59% and 52% of bulk material values for AlSi7Mg0.6 and IN718, respectively. Surface treatment contributed mainly to wall thickness reduction without significantly altering mechanical performance. Homogenized porous material models based on the Gibson–Ashby framework and a modified Hockett–Sherby constitutive law were developed, enabling efficient and realistic simulation of these complex structures. These findings provide critical experimental data and modeling tools essential for the design and optimization of next-generation additively manufactured heat exchangers in both alloys.Publicationtype: Journal Article50 17 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Design guidelines for metal binder jettingIn addition to the general advantages of additive manufacturing technologies, such as the resource-efficient production of highly complex components, the metal binder jetting (MBJ) process enables a more cost-effective manufacturing of metal additive components. However, the design freedoms gained by additive manufacturing, and MBJ in particular, are also accompanied by new design restrictions for component design. While a large number of design guidelines are already available for the established additive manufacturing processes, the metal binder jetting process imposes additional requirements on component design, which are currently only inadequately covered in the literature. Therefore, this paper presents the development and derivation of first design rules for the metal binder jetting process using martensitic stainless steel (17–4 PH). In this way, the paper also provides a methodology for deriving guidelines regardless of the material. In total, 13 guidelines have emerged from this, which in part both confirm existing guidelines, but also extend them.Publicationtype: Journal ArticleScopus© Citations 3 61 191 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Numerical and experimental investigation of the geometry dependent layer-wise evolution of temperature during laser powder bed fusion of Ti–6Al–4VLaser powder bed fusion (L-PBF) is currently the additive manufacturing process with the widest industrial use for metal parts. Yet some hurdles persist on the way to a widespread industrial serial production, with reproducibility of the process and the resulting part properties being a major concern. As the geometry changes, so do the local boundary conditions for heat dissipation. Consequently, the use of global, geometry-independent processing parameters, which are today’s state of the art, may result in varying part properties or even defects. This paper presents a numerical simulation as a method to predict the geometry-dependent temperature evolution during the build. For demonstration, an overhang structure with varying angles towards the build platform was manufactured using Ti–6Al–4V. A calibrated infrared camera was integrated into a commercial L-PBF system to measure the temperature evolution over time for a total build height of 10 mm, and the results are used for validation of the simulation. It is shown that the simulation is capable of predicting the temperature between layers. The deviations between simulation and measurement remain in single digit range for smaller overhang structures (90°, 60° and 45°). For large overhang structures (30°), the simulation tends to over-predict the temperatures up to 15 °C. Experiments with varying process parameters showed the feasibility of energy reduction as compensation of the heat accumulation produced by overhang structures.Publicationtype: Journal ArticleScopus© Citations 1 41 258 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Predicting melt track geometry and part density in laser powder bed fusion of metals using machine learningLaser powder bed fusion of metals (PBF-LB/M) is a process widely used in additive manufacturing (AM). It is highly sensitive to its process parameters directly determining the quality of the components. Hence, optimal parameters are needed to ensure the highest part quality. However, current approaches such as experimental investigation and the numerical simulation of the process are time-consuming and costly, requiring more efficient ways for parameter optimization. In this work, the use of machine learning (ML) for parameter search is investigated based on the influence of laser power and speed on simulated melt pool dimensions and experimentally determined part density. In total, four machine learning algorithms are considered. The models are trained to predict the melt pool size and part density based on the process parameters. The accuracy is evaluated based on the deviation of the prediction from the actual value. The models are implemented in python using the scikit-learn library. The results show that ML models provide generalized predictions with small errors for both the melt pool dimensions and the part density, demonstrating the potential of ML in AM. The main limitation is data collection, which is still done experimentally or simulatively. However, the results show that ML provides an opportunity for more efficient parameter optimization in PBF-LB/M.Publicationtype: Journal ArticleScopus© Citations 4 102 185