OPTIMISATION OF A FUNCTIONALLY GRADED HIP STEM MADE OF POROUS METALLIC MATERIAL
Bibliographic record
Abstract
Introduction After arthroplasty, stress shielding and high shear stresses at the bone-implant interface are common problems of load bearing implants (e.g. hip prostheses). Stiff implants cause stress shielding, which is thought to contribute to bone resorption 1 . High shear stresses, originated by low-stiffness implants, have been related to pain and interfacial micro-movements², prohibiting adequate implant initial fixation. A non-homogeneous distribution of mechanical properties within the implant could reduce the stress shielding and interfacial shear stresses 3 . Such an implant is called “functionally graded implant” (FGI). FGI require porous materials with well-controlled micro-architecture, which can now be obtained with new additive manufacturing technologies (e.g. Electron Beam Melting). Finite element (FE) simulations in ANSYS-v14.5 are used to develop an optimization methodology to design a hip FGI. Methodology A coronal cut was performed on a femur model (Sawbones®) with an implanted Profemur®TL (Wright Medical Inc.) stem to obtain the 2D-geometry for FE simulations. The central part of the FGI stem was made porous, the neck and inferior tip were solid. Ti6Al4V elastic material was assumed (E=120 GPa, v=0.3). Three bone qualities were considered for the optimization: poor (E=6GPa; v=0.3); good (E=12GPa; v=0.3); excellent (E=30GPa; v=0.3). The structure of bone evolves to maintain a reasonable level of the strains. Similarly in the proposed algorithm, the strut sections of the porous material evolve to keep stresses (proportional to strains) at a reasonable level. Starting with a very small strut section, resulting in an almost zero-rigidity stem, strut sections are increased or decreased as a function of the stresses they support. This is done incrementally, until force values corresponding to normal walking of an 80 kg person (1867 N) 4 are reached. Force direction was vertical and no action of the abductors was considered, to analyze the worst case scenario. The optimized FGI microstructure is defined by the strut diameter distributions. Since the distance between struts remain constant, variations in strut diameters result in variations in density. Optimized FGI porous structure was compared for the three bone qualities considered and with a solid stem in terms of bone stresses. Results Different bone qualities result in slightly different strut diameter distribution (Fig.1). An excellent bone quality (E=30 GPa) results in a less dense porous structure, where some dense zones are substituted by a thick strut surrounded by a low density area. As can be expected, a poor bone quality (E=6 GPa) results in a denser porous structure. Compared with the solid stem, in general the FGI stem produced higher bone stresses. Locally, the stresses augmented proximally, while diminished distally (Fig.2). This is expected to result in a smaller influence of stress shielding, and better load transfer. Conclusion The presented algorithm succeeded obtaining an optimal strut diameter distribution from low rigidity struts, using a strategy similar to bone remodelling (i.e. maintaining certain stress level within the struts). Optimized diameter distribution was obtained with little computational cost.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".