Beyond global energy density for direct energy deposition: machine learning for modelling and optimizing process parameters
Bibliographic record
Abstract
Abstract Additive manufacturing (AM) has numerous process parameters that lead to significant variation in the success and quality of the manufactured product. The input parameter of ‘global energy distribution’ (GED) is used throughout the literature to describe the input energy onto the surface of a build due to the combination of laser power, laser scanning speed and laser spot size. This paper identifies more accurate modelling using machine learning for the GED constituent process parameters and their influence on the responses of manufacturing layer height, relative density and grain size on the build. The layer height was best modelled using an artificial neural network (ANN) which produced an R2 value of 0.97 and a root mean square error (RMSE) over the data set of 0.03mm. An accurate prediction model was produced to aid manufacturers in setting the ‘layer height’ user parameter. The relative density was best modelled using multi linear regression (MLR) and produced an R2 of 0.73 and a RMSE of 0.66%. The grain size was best modelled using an ANN and produced an R2 of 0.85 and RMSE of 9.68µm. These models show why reproducibility is difficult when considering GED singularly, as each of the constituent parameters influence these individual responses to varying magnitudes. The methodology presented should aid industrial users with resource efficient process parameter response modelling with application in process/product optimization.
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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 | 0.002 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".