Key Engineering Materials Vol. 1047

Abstract: The present paper got the objective to propose and apply a methodology based on plastic behaviour modeling of a magnesium alloy AZ31 and on a Navier-Stokes approach to describe the rib geometry during printing by FDM (Fused deposition modeling). By the plastic modeling the rib section in terms of equivalent radius is obtained by the application of an already proposed constitutive equation under semisolid condition. The same information is obtained by the calculation of dynamic viscosity coefficient of the material under different conditions of nominal extruder nozzles that are 0.3 and 0.1 mm in radius with related extrusion velocity and internal pressure. The rib radius obtained by the plastic model is higher when the big nozzle is used compared with that given by the Navier-Stokes approach while an opposite behaviour is evidenced with the small nozzle where the apparent viscosity is higher. Increasing printing velocity similar rib dimensions are obtained in both the cases.

Abstract: A machine learning (ML) framework was developed for the prediction of surface topography obtained with maskless grayscale laser lithography based on the spatial distribution of the applied laser energy dose, or virtual photomask. Artificial neural networks (ANNs) were employed, with the virtual photomask and its radial averages selected as input variables and the surface elevation selected as the output variable. Training of ANNs was carried out with data acquired from the production of models comprising a wide range of representative geometries. Hyperparameter optimization was performed by assessing the accuracy of trained ANNs, with the final configuration comprising a single hidden layer with 15 neurons and a Sigmoid activation function. The trained ANN was then employed within an iterative optimization algorithm to determine the best virtual photomask for the production of new objects by updating the virtual photomask based on the predicted error, thus automatically compensating for proximity effects and sharp dose transitions. The developed approach achieved a reduction in average build error from 2.8 µm to 1.3-1.5 µm compared to standard experimental approaches in a single build, improving not only accuracy but also greatly reducing time requirements for optimization of the process.

Abstract: Laser Powder Bed Fusion (LPBF) represents a significant advancement in metal additive manufacturing, facilitating the near-net-shape fabrication of intricate, high-performance tool-steel components. This process is accomplished through a layer-by-layer selective melting technique and extremely rapid solidification (ranging from 10³ to 10⁶ K/s). The establishment of process maps — both empirical and predictive frameworks that correlate variables such as laser power, scan speed, and defect thresholds — is crucial for defining processing windows and optimising parameter selection. In tool steels, the influence of alloying elements is significant as they affect solidification behaviour, phase stability, and susceptibility to cracking. Carbide-forming additions can constrict the defect-free range, while stabilising elements can enhance toughness and dimensional accuracy. This study aims to develop a process map for S6 tool steel by varying laser scan speed and laser power. Small cubes are printed using various combinations of these parameters, followed by microstructural characterisation of the as-built material. This characterisation includes optical microscopy (OM) and porosity assessment. After establishing the process map for S6, the resulting microstructures are compared with those previously characterised for LPBF-processed S2 tool steel. This comparison provides valuable insight into the differences in the as-built microstructures of LPBF S2 and LPBF S6, particularly in relation to how the presence and relative amounts of alloying elements influence processability and microstructural development.

Abstract: Producing INCONEL® 625 (IN625) components by laser powder bed fusion (PBF-LB) demands a careful selection of process parameters to concurrently ensure high densification, stable microstructural features, and adequate surface integrity. Previous studies investigated the isolated effect of these parameters or narrow volumetric energy density (VED) ranges, albeit without offering indications on how to simultaneously optimize surface roughness, microhardness, and density. Furthermore, the validity of VED as an input for process optimization is still debated. The present study offers a systematic exploration of the laser power–scan speed (P–v) space over a wide VED interval (33–400 J/mm³) to identify stable and robust process regimes for PBF-LB of IN625. Cylindrical samples built according to dissimilar P–v combinations reveal an extended process window where the properties of interest remain well balanced. Within this region, surface roughness below 10 µm, microhardness near 300 HV1000, and relative density over 99.5% were consistently achieved. Furthermore, distinct P–v combinations sharing the same VED value were confirmed to produce markedly different results, underscoring the limitations of VED as a predictive descriptor. The findings allowed to establish quantitative guidelines for selecting robust P–v conditions, offering a practical foundation for future data-driven or physics-informed multi-objective process optimisation of PBF-LB IN625.

Abstract: This contribution presents a combined approach for in-situ experimental characterisation and numerical modelling of thermo-mechanical behaviour in directed energy deposition (DED). Full-field temperature and substrate deformation are measured simultaneously using infrared (IR) thermography and stereo digital image correlation (DIC) during laser-beam powder deposition on a thin substrate. The experimental data are used to calibrate thermal boundary conditions and to validate a macroscopic finite-element model. The validated framework is then applied to compare different deposition strategies, demonstrating the capability of the coupled measurements and simulations to capture transient thermal fields, deformation evolution and toolpath-dependent effects relevant for process optimisation.

Abstract: Lattice metamaterials with adjustable auxetic behavior are characterized by periodic configurations of interconnecting struts and nodes, allowing for precise control over their macroscopic mechanical properties. Different lattice configurations were examined, two-unit cell variants with varying void fractions were assembled into crystalline-inspired designs, specifically simple cubic and body-centered cubic. Using vat photopolymerization, fabrication was carried out using a transparent biomedical elastomeric resin that was chosen for its exceptional ductility and strain tolerance. The curing, crosslinking and thermal mechanical stability of the resin were examined using Fourier Transform Infrared Spectroscopy and Differential Scanning Calorimetry, before and after polymerization. In order to determine specific stiffness, specific yield strength, mechanical characterization involved quasi-static uniaxial compression testing. The effect of different aspects of the macroscopic structures was also observed, exploiting diverse possible applications. The combination of geometry and the behavior of the elastomeric material allowed the creation of lightweight structures that could support large reversible deformations that could be used in soft robotics and healthcare devices.

Abstract: Additive manufacturing (AM) technologies have enabled the fabrication of customizable, low-cost capacitive sensors for a wide range of applications, including robotics, automation, and bioelectronics. Although various AM techniques have been explored, structural inconsistencies often remain a challenge, limiting the performance and reproducibility of printed dielectric layers. Stereolithography (SLA), offers higher resolution and denser prints, yet the use of commercial photopolymer resins as dielectric materials remains underexplored. This study investigates two commercial SLA-compatible resins, a flexible medical-grade elastic resin and a dental-grade resin, as potential dielectric layers for capacitive force sensors. Both resins are biocompatible for short-term use or skin contact, making them suitable also for medical applications. The elastic 50A-V1 resin exhibited a Young’s modulus of E = 5.0 ± 0.2 MPa up to approximately 60% strain, whereas the Dental Clear V2 resin showed a significantly higher modulus of E = 1020 ± 80 MPa under the same conditions. Therefore, the elastic resin was subsequently chosen as the dielectric material to fabricate a proof-of-concept capacitive force sensor, which exhibited a final capacitance of 1.13 ± 0.03 pF within a force range of 10 to 400 N. The findings serve as a preliminary step towards the development of fully 3D-printed capacitive force sensors for integration into soft robotic and smart biomedical systems.

Abstract: Laser powder bed fusion (LPBF) has become a key manufacturing route for high-value components, yet accurate prediction of part quality, particularly relative density, remains challenging due to complex interactions among process parameters, material properties, melt-pool physics and shielding environment. Traditional physics-based simulations offer mechanistic insight but suffer from model-form uncertainty and computational cost while purely data-driven machine learning (ML) models often lack interpretability, physical consistency and transferability across build conditions. To address these gaps, we propose a physics-informed machine learning (PIML) framework that integrates structured domain knowledge with symbolic regression to derive compact, interpretable analytical expressions for LPBF density. The framework constructs a knowledge base (KB) comprising dimensionless and normalized physics-informed process descriptors (PIPDs) that encode energy input, thermal diffusion and melt-track geometry; these descriptors form a physically consistent feature space for learning. The framework also serves as a foundation for the proposed future community-driven, knowledge-graph-based modelling of LPBF processes. The capability of the framework is demonstrated by modelling the relative density of additively manufactured maraging steel and evaluating cross-atmosphere transferability from Argon-shielded to Nitrogen-shielded builds. The resulting symbolic models provide a transparent, extensible and physically meaningful alternative to black-box ML, achieving high predictive performance under the training regime (R = 0.964) and strong generalization across printing atmospheres (R = 0.896).

Abstract: Functionally graded materials represent a promising strategy for locally optimizing component properties while reducing both economic and environmental costs. To date, no study has addressed the development of a compositional gradient between 316L stainless steel and Invar 36 using the Wire Arc Additive Manufacturing (WAAM) process, despite the strong potential of this material combination. Indeed, such a gradient would combine the very low coefficient of thermal expansion (CTE) of Invar 36 with the low cost and excellent chemical resistance of 316L stainless steel. A particularly relevant application for this type of gradient is the storage of hydrogen or liquefied natural gas, where tanks are subjected to severe thermal stresses due to cryogenic operating temperatures. In addition, these structures must withstand aggressive environments and hydrogen exposure, which can induce material embrittlement, while maintaining sufficient mechanical properties to ensure structural integrity during service. Designing an optimal gradient therefore requires a detailed understanding of how mechanical, thermal, and chemical properties evolve with chemical composition. This study provides a preliminary assessment of these evolutions. The results show that the addition of 15–25 wt.% Invar 36 to 316L leads to a reduction in microhardness and ultimate tensile strength (UTS), associated with the disappearance of ferritic and σ phases, while significantly enhancing ductility. At higher Invar 36 contents, microhardness increases and ductility decreases due to carbide formation. From a thermal standpoint, the CTE does not follow a linear trend: it remains high up to approximately 75 wt.% Invar 36 Nb, then decreases sharply as the ferromagnetic behavior characteristic of Invar becomes dominant. Corrosion resistance remains satisfactory for Invar 36 contents below 15 wt.%, whereas higher contents lead to reduced chemical performance due to chromium dilution. Overall, these findings establish clear criteria for selecting optimal compositions in the design of a 316L–Invar 36 compositional gradient. They provide an essential foundation for the development, via WAAM, of robust and high-performance functionally graded materials suitable for applications requiring high dimensional stability, good chemical resistance, and controlled costs.