This work focuses on the development of a numerical mold filling simulation for the rotational molding process. In the rotational molding process, a dry fiber preform is placed in a mold and impregnated with a thermoset matrix under rotation. Additionally, metallic load introduction elements can be inserted into the mold and joined with co-curing or form-fit, resulting in hybrid drive shafts or tie rods. The numerical model can be used to simulate the impregnation of the preform. Based on the resin transfer molding process, an OpenFOAM solver is extended for the rotational molding process. Permeability, kinetic and curing models are selected and adapted to the materials used. A wireless measurement solution with a capacitive sensor is developed to validate the model. Comparisons between measurements and numerically calculated impregnation times to reach the capacitive sensor with the matrix show good quality of the developed model. The average deviation between calculated result and measured mean values in the experiment is 43.8% the maximum deviation is 65.8% . The model can therefore be used to predict the impregnation progress and the curing state.

Objective: To assess the discriminatory ability of vertebra-specific volumetric bone mineral density (vBMD), finite element analysis-derived fracture load (FEA-derived FL), and texture analysis (TA) features for incidental vertebral fractures, and to compare performance between thoracic and lumbar levels. Materials and methods: We retrospectively reviewed baseline and follow-up thoracolumbar CT scans from 420 patients and identified 11 patients with incidental vertebral fractures contributing to 20 fractured vertebrae (7 females; mean age 65.5years). For each fractured vertebra, three level-matched control vertebrae from patients without fractures were selected, yielding 58 controls across 29 control patients (total 78 vertebrae). Parameters evaluated include vBMD, FEA-derived FL, and TA features (24 total). Discriminatory ability was assessed using area under the curve (AUC) values. Results: vBMD, FEA-derived FL, and 4 of 24 TA features showed group-wise differences between fractured and control vertebrae groups. AUCs were 0.76 [95% CI 0.55–0.90] (vBMD) and 0.73 [95% CI 0.52–0.90] (FEA-derived FL); selected texture features ranged 0.70–0.72. Region-stratified AUC point estimates were higher in the lumbar than in the thoracic vertebrae, but the 95% CIs were wide/overlapping; comparisons are descriptive. Conclusion: vBMD had the numerically largest AUC point estimate for discriminating fractured from control vertebrae; FEA-derived FL was similar, and selected texture features showed modest discrimination with comparable point estimates across lumbar and thoracic levels, generating the hypothesis of less region dependence. Regional comparisons are descriptive. Findings are exploratory and intended to prioritize candidate measures for validation and future multivariable modeling before any clinical application.

The finite element (FE) method is a cornerstone of patient-specific biomechanical analysis, yet most workflows assign isotropic linear elastic behaviour, and neglect bone’s intrinsic anisotropic and non-linear response to load. We present PBMGA (Python-based Bone material grouping and anisotropy), a novel open-source tool that automates the calculation and element-specific assignment of non-linear and transversely isotropic (and, in principle, more general anisotropic) bone material parameters using user-defined equations. PBMGA integrates three customisable material grouping strategies: Percentual Thresholding, Adaptive Clustering, and Equidistant Grouping, to compress the number of unique material sets, significantly reducing computational complexity in downstream FE simulations without compromising accuracy. Its modular architecture supports seamless integration with existing preprocessing workflows and scalable analysis of large clinical datasets. By combining accurate material modelling with high‐throughput capability, PBMGA enhances biomechanical prediction and paves the way for more efficient clinical diagnostics and treatment planning.

Finite Element Analysis (FEA) is a powerful computational technique used to assess bone strength and fracture risk based on patient-specific anatomical data. However, the widespread clinical and research adoption of this technique is often hindered by the complex and labor-intensive preprocessing steps required to convert clinical imaging data into analysis-ready finite element models, including segmentation, mesh generation, material property mapping, and solver-specific file formatting. To address this bottleneck, we developed BoneMesh, an automated meshing and material-mapping software module integrated into the open-source 3D Slicer platform. BoneMesh streamlines the generation of high-quality, patient-specific tetrahedral meshes directly from CT-based bone segmentations and efficiently assigns bone mineral density values derived from the imaging data. We verified the performance of BoneMesh against established commercial and freeware software (Abaqus, Simpleware and Bonemat) using CT-scans and segmentations from frequently analyzed human bones (Femur, Tibia, Lumbar vertebra, and Thoracic vertebra), demonstrating similar or superior performance in achieving targeted mesh quality parameters, including mesh edge length accuracy, element shape factors, and aspect ratios. BoneMesh significantly reduces manual intervention, preprocessing time, and the potential for human errors, thus offering an open-source, accessible, reliable, and efficient pipeline for advancing patient-specific finite element simulations in clinical research and potentially in routine clinical assessments.

Purpose: To develop and evaluate an automated workflow for the setup of single-vertebra finite element (FE) simulations from clinical CT data. Specifically, we quantified how automated endplate identification, vertebra-specific coordinate system definition, and load-application-point assignment influence the simulated fracture-load estimates. Methods: We analyzed 113 vertebrae from 70 patients that had previously undergone manual FE setup. The automated pipeline identified vertebral endplates, assigned a vertebra-specific coordinate system, and assigned the load application point for axial compression simulations. Automated setups were visually graded as good, acceptable, or bad using predefined criteria. Agreement with manual reference models was evaluated, and the effects of each setup component on fracture-load estimates were quantified separately. Results: Of 113 vertebrae, 53 (47%) automated setups were graded good, 40 (35%) acceptable, and 20 (18%) bad. In the good subset, fully automated models showed strong agreement with manual reference workflow ( R ² = 0.950), with a median percent difference in fracture-load estimate of 13.0% (95% CI, 9.0% to 15.3%). Among individual setup components, endplate identification produced the smallest change in fracture-load estimates, followed by coordinate-system definition, whereas the load-application-point assignment produced the largest deviations and the widest limits of agreement. Changes in load-point location showed the clearest association with changes in fracture-load estimates, whereas endplate-area differences showed no clear association. Conclusion: Automated setup of vertebral FE simulations can reduce manual intervention and support scalable processing, but current robustness remains insufficient for fully unsupervised use. In successfully processed cases, the workflow showed strong agreement with a manually configured reference workflow and identified load-point assignment as the most influential boundary-condition component in fracture-load estimates.