There is still a considerable gap in the definition of the weldability of Duplex Stainless Steel (DSS). A lack of clarity that is explained by the standard specification of the maximum content of equivalent carbon that defines a “weldable” steel coupled with the fact that the alloying elements of DSS exceed this defined limit of weldability. In this paper, welding quality in an inert environment and in presence of chlorides is analyzed with the aim of defining optimum welding conditions of 2001, 2304, and 2205 DSS. The same procedure is followed for a hybrid weld between DSS 2205 and a low carbon mild steel, S275JR. As main output, this study defined the optimal welding conditions with tungsten inert gas without filler for each type of DSS weld that showed excellent anti-corrosion performance, with the exception of the DSS 2205-S275JR weld where widespread corrosion was observed. Additionally, this study established a relationship between the thermal input during welding and the content of alloying elements in defect-free joints. Furthermore, it demonstrated that an increase in ferrite content did not lead to a worse corrosion resistance, as expected after passivation.

Steel rebars of structures exposed to cyclic loadings and marine environments suffer an accelerated deterioration process by corrosion fatigue, causing catastrophic failure before service life ends. Hence, stainless steel rebars have been emerging as a way of mitigating pitting corrosion contribution to fatigue, despite the increased cost. The present study proposes a corrosion fatigue semiempirical model. Different samples of rebars made of carbon steel, 304L austenitic (ASS), 316L ASS, 2205 duplex (DSS), 2304 lean duplex stainless steels (LDSS), and 2001 LDSS have been embedded in concrete and exposed to a tidal marine environment for 6 months. Corrosion rates of each steel rebar have been obtained from direct measurement and, considering rebar standard requirements for fatigue and fracture mechanics, an iterative numerical model has been developed to derive the cycles to failure for each stress range level. The model resulted in a corrosion pushing factor for each material, able to be used as an accelerating coefficient for the Palmgren-Miner linear rule and as a performance indicator. Carbon steel showed the worst performance, while 2001 LDSS performed 1.5 times better with the best cost-performance ratio, and finally 2205 DSS performed 1.5 times better than 2001 LDSS.

Additive manufacturing (AM) of scaffolds enables the fabrication of customized patient-specific implants for tissue regeneration. Scaffold customization does not involve only the macroscale shape of the final implant, but also their microscopic pore geometry and material properties, which are dependent on optimizable topology. A good match between the experimental data of AM scaffolds and the models is obtained when there is just a few millimetres at least in one direction. Here, we describe a methodology to perform finite element modelling on AM scaffolds for bone tissue regeneration with clinically relevant dimensions (i.e., volume > 1 cm³). The simulation used an equivalent cubic eight node finite elements mesh, and the materials properties were derived both empirically and numerically, from bulk material direct testing and simulated tests on scaffolds. The experimental validation was performed using poly(ethylene oxide terephthalate)-poly(butylene terephthalate) (PEOT/PBT) copolymers and 45 wt% nano hydroxyapatite fillers composites. By applying this methodology on three separate scaffold architectures with volumes larger than 1 cm³, the simulations overestimated the scaffold performance, resulting in 150–290% stiffer than average values obtained in the validation tests. The results mismatch highlighted the relevance of the lack of printing accuracy that is characteristic of the additive manufacturing process. Accordingly, a sensitivity analysis was performed on nine detected uncertainty sources, studying their influence. After the definition of acceptable execution tolerances and reliability levels, a design factor was defined to calibrate the methodology under expectable and conservative scenarios.