Scale, Material Concentration, Stress Relief and Part Removal Effects on the Dimensional Behaviour of Selected AlSi10Mg Components Manufactured by Laser Powder Bed Fusion
Bibliographic record
Abstract
Laser Powder Bed Fusion (LPBF) is a predominant Additive Manufacturing (AM) process. While metallic LPBF is gaining popularity, one of the barriers facing its wider industrial use is the current relatively limited knowledge with respect to its dimensional and geometrical performance, as well as the inability to predict it. This paper presents an experimental investigation of the geometrical and dimensional deviations of selected LPBF-manufactured components according to the ASME Y14.5 (2009) standard. In this study, two types of axisymmetric parts (cylinder and cylindrical pyramid) were designed with three different levels of material concentration, and replicated at three different scales for a total of 18 test artifacts. These parts were manufactured from AlSi10Mg powder using an EOSINT M280 printer, subjected to stress relief annealing at 300 °C for two hours, removed from the platform and finished by micro shot peening. A complete statistical analysis was carried out on the artifacts before and after each post-processing step. The results of this investigation allowed for the quantification of the intra- (same part) and inter- (different parts) scale effects, as well as of the material concentration, stress relief, part removal and micro shot peening effects on the overall three-dimensional (3D) profile deviations and on the dimensional deviations of some selected features (e.g., diameter, thickness). For example, cylindrical pyramid parts showed the following average deviations of their outside diameters: a −63 µm shrinkage of the as-built part diameter as compared to its computer-assisted design (CAD) value, a +20 µm expansion after stress relief annealing as compared to the precedent step, a −18 µm shrinkage after part removal and, finally, a −50 µm shrinkage after micro shot peening.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 teacher head, 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".