Evaluation of machine learning models in predicting mechanical properties of additive-manufactured parts
Bibliographic record
Abstract
Different machine learning (ML) models have been applied in predicting mechanical properties of additive-manufactured parts. However, the existing research primarily focuses on individual mechanical properties and overlooks interrelations among multiple properties and their combined responses to additive manufacturing (AM) process parameters. This research investigates ML models for predicting ultimate tensile strength (UTS), compressive strength (CS), and Young's modulus (YM) of fused filament fabricated parts. Gaussian process regression (GPR), support vector machines (SVMs), neural networks, and linear regression models are evaluated. GPR achieved the highest performance for UTS and CS with R 2 values of 0.95 and mean absolute errors (MAE) of 0.78 and 2.4 MPa, respectively. For YM, SVM performed best with an R 2 of 0.90 and an MAE of 23 MPa. Shapley Additive Explanations analysis reveals that printing temperature and infill density are the most influential parameters for UTS and CS, respectively, while YM is primarily affected by infill density and wall thickness. These findings highlight the critical role of AM process parameters, such as wall thickness, layer height, infill density, print speed, and print temperature, in determining the part performance, and demonstrate the potential of ML models to effectively predict mechanical properties of 3D-printed components.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".