Dimensional Deviation Prediction Model Based on Scale and Material Concentration Effects for LPBF Process
Bibliographic record
Abstract
Abstract Additive Manufacturing (AM) processes generate parts layer-by-layer without using formative tools. The resulting advantages highlight the capability of AM to become an inherent part of product development. However, process-specific challenges such as high surface roughness, the stair-stepping effect, or dimensional deviations inhibit the establishment of AM at the industrial scale. Thus, AM parts often need to be post-processed using established manufacturing processes. Many process parameters and geometrical factors influence the dimensional accuracy in AM. Published results relating to these deviations are also difficult to compare because they are based on several geometries that are manufactured using different processes, materials, and machine settings. Laser Powder Bed Fusion (LPBF) is gaining in popularity, but one of the obstacles facing its larger industrial use is the limited knowledge of its dimensional and geometrical performances. Therefore, using it requires studying the process and improving the accuracy of the parts involved. This paper represents a new attempt to predict dimensional deviations of LPBF parts. During the project, the scale- and material concentration-related phenomena were implemented in a new image analysis model and applied to the as-built part. We carried out a comparison between the results of the proposed model with those obtained from numerical analyses and experiments. The model does not use finite element analysis, takes less time to compute, and provides reasonable prediction accuracy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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 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".