Metals

Metal additive manufacturing (AM) offers promising advancements in producing implants with complex geometry for biomedical applications, where accuracy and near-net-shape production are essential. Metal AM by laser powder bed fusion (PBF-LB) is a promising route to produce biodegradable iron implants made of complex lattice structures. However, processing windows for pure iron remain poorly defined. This work focuses on optimizing PBF-LB parameters for pure iron using a design of experiments (DoE) approach on bulk samples of different geometries to evaluate different parameters. Hatch laser power, scanning speed, hatch distance and point distance were varied and their effect on porosity, surface roughness and dimensional accuracy was evaluated. This was followed by the fabrication of rhombitruncated cuboctahedron (RTCO) lattice structures with the best parameters previously defined for the bulk samples. The best parameter set (hatch laser power 180 W, scanning speed 600 mm/s, hatch distance 110 µm and point distance 12 µm, corresponding to a volumetric energy density of 90.9 J/mm³) produced bulk samples with a porosity as low as 0.07% (99.93% density) measured in polished sections. Using these parameters, RTCO lattices with designed relative densities of 10.28%, 35.29% and 65.16% were successfully manufactured with small geometric deviations and good control of strut thickness and relative density. The results of this study define a robust PBF-LB processing window for pure iron and demonstrate the feasibility of producing geometrically controlled, biodegradable iron lattice structures suitable for future load-bearing biomedical applications.

Punching is a widely adopted cold sheet metal forming process, prized for its cost-effectiveness and high production efficiency. However, premature tool failure remains a persistent challenge, leading to increased downtime and maintenance costs. This study investigates solutions to mitigate tool failure through a combination of 3D design optimization, Finite Element Modeling (FEM), and Response Surface Methodology (RSM). Specifically, FEM was used to analyze stress distribution and deformation in the punch under varying geometric and operational parameters, while RSM optimized the design space to identify key factors influencing tool life. The findings reveal that each proposed solution, including modifications to punch geometry, clearance, and material treatment, offers distinct advantages and trade-offs. A comparative analysis of these solutions highlighted one optimal design, which was then further analyzed using FEM to predict damage progression. While this study provides a framework for reducing tool failure, experimental validation of the damage prediction model is recommended for future work to confirm the numerical results. This research aims to provide industrial practitioners with actionable insights and methodologies to enhance punch durability, ultimately reducing production interruptions and costs.

CoCrNi medium-entropy alloys (MEAs) have emerged as a promising class of structural materials due to their exceptional strength–ductility synergy. However, the lack of composition-dependent predictive models severely hinders rational alloy design, forcing reliance on costly trial-and-error experimentation. This study develops a comprehensive theoretical model to predict the yield strength of single-phase face-centered-cubic (FCC) Co_(1-x-y)Cr_yNi_x MEAs by quantitatively evaluating the contributions of grain boundary and solid solution strengthening. The model demonstrates that increasing Cr content significantly enhances grain boundary strengthening through elevated shear modulus and Peierls stress, whereas Ni has a minimal effect. Solid solution strengthening, determined by the minimum resistance among Co–Cr, Co–Ni, and Cr–Ni atomic pairs, peaks at 1726.21 MPa for the composition Co₁₇Cr₆₄Ni₁₉. For equiatomic CoCrNi, theoretical yield strengths range from 1287.8 to 1575.4 MPa across grain sizes of 0.5–50 µm, showing excellent agreement with experimental results. This work provides a reliable, composition-dependent predictive framework that surpasses traditional trial-and-error methods, enabling efficient design of high-strength MEAs through targeted control of lattice distortion and elemental interactions.