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To analyse the strength and mechanical behaviour of hip implants, it is essential to employ an appropriate loading model. Generating computational models supplemented with muscle forces is a complicated task, especially in the initial phase of implant development. This research aims to expand the possibilities of the simpler acetabular cage model based on joint loads without significantly increasing the demand for computing resources. A Python script covered and grouped the loads from daily activities. The ten calculated major loads were compared with the maximum of the walking and stair climbing loads through the finite element analyses of a custom-made acetabular cage. Sensitivity analyses were performed for the surrounding bones’ elastic modulus and the pelvis boundary conditions. The major loads can geometrically cover the entire load spectrum of daily activities. The effect of many high-magnitude force vectors is uncertain in the approach that uses the most common maximum loads. Using these resultant major loads, a new stress concentration area could be detected on the acetabular cage, besides the stress concentration areas induced by the loads reported in the literature. The qualitative correctness of the results is also supported by a control computed tomography scan: a fracture occurred in an extensive, high-stress zone. The results are not sensitive to changes in the elastic modulus of the surrounding bone and the boundary conditions of the model. The presented load vectors and the algorithm make more extensive static analyses possible with little computational overhead. The proposed method can be used for checking the static strength of similar implants.

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Research significance: In the clinical practice, surgeons sometimes must deal with extended bone defects. Among others, bone grafts are used for filling the large absence. After implantation, the structure of the graft can change, and the graft's load-bearing effect can be significant. This leads to the idea, that during the design of an implant this effect should be taken into account in the finite element simulations. In this paper, the authors show the implementation of the bone graft adaptation. Methodology: This programming task was done by using Python, Tcl and the HyperMesh interface. The bone remodeling algorithm and the related parameters were from the literature research. The results are shown with a finite element model prepared for the Optistruct solver, where the geometry models were based on a patient's CT data. Results: Viewing the bone graft's elemental apparent density, the most loaded areas could be detected. Conclusion: The model can predict qualitatively the bone graft's change, which can provide additional information for the implant design. Further analyses are required to investigate the sensitivity of the results.