Mechanical and microstructural properties of additively manufactured porous titanium alloy constructs for orthopaedic and maxillofacial reconstruction
Bibliographic record
Abstract
Porous intraosseous implants, fabricated from titanium alloy by selective laser melting (SLM), promote osseointegration and decrease stress shielding. Nevertheless, the application of such constructs in surgery has been restricted due to issues with their structural and mechanical properties. In addition, the flexural properties of porous constructs are not well known. Hence, this research aimed to investigate the mechanical and microstructural properties of porous constructs made from Ti6Al4V alloy for applications such as mandibular reconstruction. Computer models were created of dumbbell-shaped and square prism constructs with cubic pore structures. Five strut thicknesses between 250 and 650 µm with a constant 1 mm unit cell size were created, which gave rise to pores of sizes between 350 and 750 µm. Nonporous models were used as controls. Constructs were fabricated from these models using selective laser melting. Computed tomography was used to investigate internal defects and surface roughness. Internal defects made up < 1.0 % of the total volume. Loose and partially melted particles caused a rough surface on the struts, with arithmetic mean height ranging between 2.0 and 9.5 µm. Finite element analysis (FEA) was performed to simulate tensile and flexural loadings and predict locations of mechanical weakness. Static tensile and three-point bend tests were performed on SLM-built constructs using an Instron screw-type testing machine. The FEA models incorporated mechanical properties of Ti6Al4V, which were sourced from the stress-strain curves from tensile tests on nonporous constructs produced via selective laser melting. There was close agreement between the FEA simulations and the actual tensile and flexural strengths and moduli of the constructs (deviations < 11 %). The results of real-life mechanical tests and FEA tests demonstrated that the modulus and strength values are strongly correlated with strut thickness (R 2 >0.95). Porous Ti6Al4V constructs with strut thicknesses ranging between 350 and 450 µm were found to have modulus and strength values that matched those of the mandible. This study demonstrated that FEA models can accurately predict the mechanical behaviour of SLM-built porous constructs. This will permit the rapid design of patient-specific porous devices that facilitate bone alignment, vascularization, tissue ingrowth, and skeletal function.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.000 | 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 source (direct Gemma or distilled Codex), 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".