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Item type:Research Data, Access status: with files , Design and Simulation Files for a Novel Lightweight Design of Additively Manufactured Structural Components through Biomimetics(2025-03-19); ; ; The data is based on the paper with title “Novel Lightweight Design of Additively Manufactured Structural Components through Biomimetics” that was presented at the 2024 Powder Metallurgy World Congress & Exhibition (World PM2024), 13 - 17 October 2024, Yokohama, Japan. Additional information about the context and generation of this data can be found in the paper that was published in the Journal of the Japan Society of Powder and Powder Metallurgy (2025, Volume 72, Issue Supplement, Pages S1241-S1248). Link to article: https://doi.org/10.2497/jjspm.16C-T11-10.Data Type: Simulation Data45 179 - Some of the metrics are blocked by yourconsent settings
Item type:Research Data, Access status: with files , Automatic Infill Generation with Honeycomb and Grid Structures in MIM Mold Design for Metallic Bipolar Plates(2025-03-10); ; ; ; This dataset highlights a specialized application of automation and optimization of infill structures using the Synera platform. By developing a Synera code to automatically apply infill structures to any model, the design process becomes significantly more efficient, allowing engineers to expedite the transition from concept to production. The practical relevance of this technology is demonstrated in its application to injection molds for bipolar plates within the Metal Injection Molding (MIM) process. Bipolar plates are critical components in fuel cells that require precisely manufactured parts to ensure structural integrity and optimal material usage. Furthermore, the use of Material Extrusion of Metals (MEX/M) processes emphasizes the interdisciplinary integration of additive and traditional manufacturing technologies. Insights from automated infill generation can thus be directly applied to the physical production of prototypes or end-use parts, showcasing the potential of Synera in design automation and the synergy between digital design and conventional manufacturing methods.Data Type: Compiled Data ; Data Type: Experimental Data42 89 - Some of the metrics are blocked by yourconsent settings
Item type:Research Data, Access status: with files , Machine Learning Models and Data for the Application of Machine Learning in Predicting Quality Parameters in Metal Material Extrusion (MEX/M)(2025-03-10); The supplementary data includes detailed information on machine learning (ML) models, specifically MLP and Bagging, and the datasets used to predict surface roughness and density in metal extrusion additive manufacturing (MEX/M) components. Leveraging experimental data, these models incorporate input parameters like layer thickness, print velocity, infill, overhang angle, and sinter profile. Demonstrating a prediction accuracy ranging from 39% to 97%, the data underscores the models' effectiveness in optimizing MEX/M processes, enhancing quality control, and improving design efficiency, particularly for complex geometrical structures.Data Type: Compiled Data46 81 - Some of the metrics are blocked by yourconsent settings
Item type:Research Data, Access status: with files , Supplementary Data: Design Guidelines for Material Extrusion of Metals (MEX/M)Context: This data is supplementary material to the publication with the title ‘Design guidelines for Material Extrusion of Metals (MEX/M)’ The corresponding paper investigates systematic framework for developing design guidelines for filament-based Material Extrusion of Metals (MEX/M), an additive manufacturing (AM) process classified under ISO/ASTM 52900. MEX/M presents a cost-efficient and sustainable alternative to conventional manufacturing techniques, particularly in the context of rapid prototyping. While AM inherently provides extensive design freedom, the MEX/M process introduces unique geometrical and process-related constraints that must be addressed for optimized component fabrication. This research formulates and validates design principles for the MEX/M process utilizing an austenitic steel 316L (1.4404) alloy filament. The feedstock comprises a homogeneous mixture of 316L stainless steel powder and a polymeric binder within a thermoplastic matrix, which is extruded and deposited in successive layers. To systematically evaluate the geometric feasibility of MEX/M, benchmark specimens were fabricated, analysing critical parameters such as minimum printable wall thickness, feature inclination, borehole formation, overhang stability, and the resolution of horizontal and vertical gaps. The green-state components, post-fabrication, undergo a two-stage thermal treatment comprising de-binding and sintering at elevated temperatures to achieve near-full densification. The process parameters for fabrication and sintering were adapted from prior empirical studies. A quantitative assessment of geometric deviations was conducted through 3D scanning, correlating the fabricated components with their corresponding CAD models to determine deformation (mm) and shrinkage rates (%). To further validate the practical applicability of the developed guidelines, an impeller was manufactured, incorporating key geometrical constraints intrinsic to MEX/M. As an outcome, this study proposes ten design guidelines that not only reinforce existing best practices but also extend their applicability, contributing to the enhancement of process reliability and structural integrity in MEX/M-based additive manufacturingData Type: Experimental Data68 161 - Some of the metrics are blocked by yourconsent settings
Item type:Research Data, Access status: with files , Metallic Bipolar Plate Production Through Additive Manufacturing: Contrasting MEX/M and PBF-LB/M Approaches(2025-02-05); ; ; ; The corresponding paper investigates the production of metallic bipolar plates using MEX/M and PBF-LB/M additive manufacturing technologies. Six design variations were fabricated, and the geometric accuracy of both methods was evaluated using 3D scanning technology. To account for dimensional deviations, the shrinkage of MEX/M samples was measured and compensated in the digital models before the comparison. A comparative analysis was conducted based on 20 measurement points per sample, calculating mean values and standard deviations. The results indicate that PBF-LB/M is well-suited for manufacturing complex structures with intricate geometric details, while MEX/M provides a balanced trade-off between accuracy and production efficiency for simpler bipolar plate designs. Consequently, PBF-LB/M is recommended for prototyping and experimental validation of complex geometries, whereas MEX/M is more suitable for prototype production as a preparatory step for mass manufacturing via the MIM process.Data Type: Dataset50 136 - Some of the metrics are blocked by yourconsent settings
Item type:Research Data, Access status: with files , Cost Application for the Publication: Methodology for Cost Estimation Using Characteristic Factors in Additive Manufacturing(2024-08-27); ; ; ; A cost tool was created from the mathematical equations and relationships, which determines the costs for the additive manufacturing processes depending on the number and dimensions of the component. Furthermore, weight reductions are specified in percent for known conventional costs that are required to achieve cost parity to the conventional manufacturing method. Therfore, this application allow an easy and fast estimation of suitable additive manufacturing technologies denpending on the selected material, dimension and quantity of a component. With the knowledge of the manufacturing restriction of the each technology this tool deliver a great estimation in finding the right application. It is also possible to adjust the costs and add different machines to this tool, which enables the comparison between technologies.Data Type: Research Software112 36 - Some of the metrics are blocked by yourconsent settings
Item type:Research Data, Access status: with files , Design rule for efficient support connection point spacing in laser powder bed fusion of Ti6Al4V(2024-08-22); ; The supplementary material data sheet provide the measured data for the developed illustrations for the publication https://doi.org/10.3139/9783446471733.006. The varied parameters are: support distance, support diameter, and overhang angle. The target parameter is the component thickness which has to be 1 mm as designed. More information are avaible in the ReadMe file.Data Type: Dataset53 60 - Some of the metrics are blocked by yourconsent settings
Item type:Research Data, Access status: with files , Parametric Design of Infillstructures as CAD Data for the publication entitled: Design Optimization of Additively Manufactured Components Using Simulation-Based Analysis of Infill Structures(2024-06-06)CAD Data: Parametric designs of honeycomb and lattice structures as rectangular 3-point bending specimens and cylindrical compression specimens according to ISO7438 and DIN50106.Data Type: Measurement and Test Data38 26 - Some of the metrics are blocked by yourconsent settings
Item type:Research Data, Access status: with files , Parameter Optimization Files (COMSOL Multiphysics 6.0) for Dimensioning of Biomimetic Beams under Bending(2024-01-30); ; ; The research data contains four parameter optimization files (1-4) of two types of biomimetic beams (RD1 and RD2). The files contain the simulation files and results for the parameter optimization of the designs for various bending load cases. The parametric designs consider certain manufacturing constraints of the PBF-LB/M process. File 5 contains the parameter optimization of a solid cylindrical beam which was used as a reference design. Files 6 and 7 contain information about the mesh convergence study.Data Type: Simulation Data166 216 - Some of the metrics are blocked by yourconsent settings
Item type:Research Data, Access status: with files , Additional Data: ML-Algorithm Source Code; for Publication: PREDICTIVE MODELING OF LATTICE STRUCTURE DESIGN FOR 316L STAINLESS STEEL USING MACHINE LEARNING IN THE L-PBF PROCESS(2024-01-25); This is the Source Code of the ML Algorithm that was trained with the corresponding data set to predict the optimal design of the infill structure based on a given set of mechanical requirementsData Type: Source Code66 24