This study aims to describe the numerical modeling and nonlinear finite element analysis of typical spinal braces as a first step towards optimizing their topology for 3D printing. Numerical simulation was carried out in Abaqus CAE software Version 2021, utilizing a CAD (Meshmixer Version 3.5.474) scan of an actual spinal brace. Boundary conditions were defined by means of contact surfaces between the human body and the supporting pads located in the interior of the brace. The process of tightening the straps on the rear face of the brace was simulated via appropriate imposed displacements. The response is described through the deformations and developing stresses of the brace and the contact pressures in the areas of interaction with the human body. Parametric analysis indicated that increasing the cross-sectional thickness or elastic modulus of the brace material results in higher maximum von Mises stresses and lower displacements. The comparison between 3D-printed and conventional braces highlighted the potential of 3D-printing technology to achieve comparable performance with customized designs, leveraging the constitutive properties of 3D-printed material obtained from tension tests. The tension tests demonstrated that the 3D-printed material achieved higher values of modulus of elasticity compared to traditional brace materials. Finally, the topology optimization criteria to be applied for the design of spinal braces in the next step of this ongoing research are briefly described.

Customized wrist splints, particularly for upper extremity orthoses like wrist support braces, are commonly used across numerous clinical scenarios. However, the traditional process for producing personalized wrist splints is largely manual and highly dependent on the expertise of orthopedic specialists. This experience-based approach often leads to suboptimal outcomes, necessitating further refinement of the designs. Recent advancements in Additive Manufacturing (AM) have brought significant innovation to various industries, including orthopedics. This study aims to present a comprehensive methodology that integrates advanced design tools, like 3D Scanning, with digital manufacturing techniques to produce tailored wrist splints. The produced hand brace aims to offer enhanced mechanical performance and comfort by precisely fitting an individual’s anatomy while minimizing material usage and weight. To achieve optimal design efficiency, the study explores the application of a topology optimization (TO) approach for design, while the manufacturing process utilizes Fused Deposition Modeling (FDM), an evolving technology within the additive manufacturing (AM) sector.

Customized wrist splints, particularly for upper extremity orthoses like wrist support braces, are commonly used across numerous clinical scenarios. However, the traditional process for producing personalized wrist splints is largely manual and highly dependent on the expertise of orthopedic specialists. This experience-based approach often leads to suboptimal outcomes, necessitating further refinement of the designs. Recent advancements in Additive Manufacturing (AM) have brought significant innovation to various industries, including orthopedics. This study aims to present a comprehensive methodology that integrates advanced design tools ,like 3D Scanning, with digital manufacturing techniques to produce tailored wrist splints. The produced hand brace aims to offer enhanced mechanical performance and comfort by precisely fitting an individual’s anatomy while minimizing material usage and weight. To achieve optimal design efficiency, the study explores the application of a Topology Optimization (TO) approach for design, while the manufacturing process utilizes Fused Deposition Modeling (FDM), an evolving technology within the Additive Manufacturing (AM) sector.

In this work we develop and validate neural-network (NN) constitutive models for structural steels under monotonic and cyclic loading, using internal-variable inputs to encode path dependence. For monotonic tension at \(200-800^{\circ }C\) (Kirby-Preston dataset), a feed-forward NN (\(1-10-10-1\), sigmoid activation) reproduces experimental stress-strain curves to high fidelity; across temperature-specific curves extracted from the experimental data, the median normalized Chamfer distance is \(0.355\%\) of the plot height \((IQR: 0.026-0.361\%)\). For cyclic compression of stainless steel 316 (Chaboche dataset), the internal-variable NN (\(5-30-30-1\)) aligns with the experimental hysteresis loops with median normalized Chamfer distance \(0.032\%\), indicating near-pixel-level curve overlap. We provide new quantitative validation tables and sensitivity plots; results indicate weak dependence on hidden-node count and epochs within practical ranges. The approach is computationally lightweight at inference and amenable to generalization via internal-state encoding. Limitations at very high temperatures (\(>750^{\circ }C\)) and low-strain transitions are discussed, along with integration paths for physics-guided constraints.