Machine Learning-Guided Process Optimization for Fatigue Life Enhancement in Additively Manufactured Titanium Dental Implants
Bibliographic record
Abstract
Titanium and titanium alloys are widely used as raw materials for dental implants due to their high corrosion resistance and favorable mechanical properties. Among the methods for producing these implants is the additive manufacturing method. One method for investigating the properties of these implants can be based on machine learning.. study presents a machine learning (ML)-driven approach to predict and optimize the fatigue life and build quality of titanium dental implants fabricated through additive manufacturing (AM). By analyzing parameters such as laser power, scan speed, hatch spacing, and preheat temperature, the models assess their influence on porosity, surface roughness, and fatigue performance. A synthetic dataset was generated based on validated AM ranges. Through correlation analysis, feature importance modeling, and classification/regression techniques, we identify optimal processing windows. Random Forest and XGBoost models demonstrated high predictive accuracy, validating the feasibility of data-driven process control for producing high-performance implants with extended fatigue resistance.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".