Alejandro Pascual

The increasing adoption of High-Pressure Die Casting (HPDC) technology in the production of automotive body structure components is driven by its potential for efficiency and performance. This technology, however, involves complex physical phenomena with numerous parameters that significantly influence casting quality. In this study, three key die casting parameters—plunger or shot speed, vacuum application, and intensification pressure (IP)—have been evaluated following a Design of Experiment (DoE) approach. The results demonstrate that IP application is instrumental in reducing porosity within the cast specimens, thereby enhancing their mechanical strength and elongation. Furthermore, the combined application of vacuum and IP yields further improvements in elongation by minimizing porosity. These findings are particularly relevant for silicon-free alloys, which eliminate the need for post-casting heat treatments to achieve the required mechanical properties. By optimizing HPDC processes, manufacturers can reduce rejection rates, lower production costs, and improve the overall efficiency of their operations, contributing to the production of high-quality and cost-effective components for the automotive industry.

This work aims to present a digital-twin for prediction of the mechanical behavior and failure of additive manufacturing components, considering the characteristics of heterogeneities, mainly porosities, which appear during the manufacturing process. Therefore, the proposed methodology is based on the development of a CT-based FEM model, as well as the study of different porosity features by CT analysis and FEM modelling. Their influence on predictions is also evaluated. To this end, tensile test specimens were manufactured by means of L-PBF, according to ASTM E8/E8M. Afterwards, the analysis and digitalization of each specimen were carried out using CT, followed by FEM modelling of each one. Finally, the results obtained by the analysis were correlated with experimental tensile tests. Taking advantage of the virtual model proposed, virtual cases and hypothesis were also analyzed. Hence, the promising future of the proposed methodology is highlighted.

In this work, three novel complex concentrated aluminum alloys were developed. To investigate the unexplored region of the multicomponent phase diagrams, thermo-physical parameters and the CALPHAD method were used to understand the phase formation of the Al₈₀Mg₅Sn₅Zn₅Ni₅, Al₈₀Mg₅Sn₅Zn₅Mn₅, and Al₈₀Mg₅Sn₅Zn₅Ti₅ alloys. The ingots of the alloys were manufactured by a gravity permanent mold casting process, avoiding the use of expensive, dangerous, or scarce alloying elements. The microstructural evolution as a function of the variable element (Ni, Mn, or Ti) was studied by means of different microstructural characterization techniques. The hardness and compressive strength of the as-cast alloys at room temperature were studied and correlated with the previously characterized microstructures. All the alloys showed multiphase microstructures with major α-Al dendritic matrix reinforced with secondary phases. In terms of mechanical properties, the developed alloys exhibited a high compression yield strength up to 420 MPa, high compression fracture strength up to 563 MPa, and elongation greater than 12%.

The scope of this work is to assess the influence of porosity on the mechanical behaviour of L-PBF Inconel 718. To this end, some test specimens were manufactured by L-PBF, according to ASTM E8/E8M. Afterwards, these specimens were scanned by means of XRCT in order to analyse the porosity of each one. The XRCT geometry generated by the previous step were used for FEM analysis. Finally, the results obtained by the analysis were correlated with the experimental results, highlighting the promising future of the proposed methodology.

The scope of this work is to present a reverse engineering (RE) methodology to achieve accurate polygon models for 3D printing or additive manufacturing (AM) applications, as well as NURBS (Non-Uniform Rational B-Splines) surfaces for advanced machining processes. The accuracy of the 3D models generated by this RE process depends on the data acquisition system, the scanning conditions and the data processing techniques. To carry out this study, workpieces of different material and geometry were selected, using X-ray computed tomography (XRCT) and a Laser Scanner (LS) as data acquisition systems for scanning purposes. Once this is done, this work focuses on the data processing step in order to assess the accuracy of applying different processing techniques. Special attention is given to the XRCT data processing step. For that reason, the models generated from the LS point clouds processing step were utilized as a reference to perform the deviation analysis. Nonetheless, the proposed methodology could be applied for both data inputs: 2D cross-sectional images and point clouds. Finally, the target outputs of this data processing chain were evaluated due to their own reverse engineering applications, highlighting the promising future of the proposed methodology.