Evaluation of novel 3D printed foam patterns for rapid investment casting based on fused filament fabrication
Bibliographic record
Abstract
Purpose This study aims to evaluate the effectiveness of three-dimensional (3D) printed foam polylactic acid (PLA) patterns in reducing ceramic shell stresses and cracking during burnout in the rapid investment casting (RIC) process to improve casting yield and dimensional tolerances. Design/methodology/approach Cylindrical and step-wedge patterns were 3D printed using foam PLA feedstock and compared with patterns from plain PLA and Polyvinyl Butyral (PVB). The patterns were shelled using ceramic slurry and investment cast in A356.1 aluminum alloy. Shell cracking and dimensional tolerances of resulting castings were assessed. Additionally, a complex component was 3D printed, laser-scanned, then cast and rescanned to evaluate dimensional accuracy. Finite Element Analysis (FEA) was conducted on cylindrical geometries to analyze internal mold pressure because of thermal stresses during burnout. Findings The foam PLA for all patterns produced no shell cracking during both ramp and flash burnouts. Castings made from foam PLA patterns showed improved dimensional tolerances and a narrower error distribution in GD&T analysis compared to those made from PLA and PVB. FEA results indicated that the thermomechanical properties of foam PLA reduce internal mold pressure by over 90%, which decreased internal shell stresses. Originality/value This research introduces a novel application of 3D printed foam PLA feedstock in the RIC process as a pattern material. This study demonstrates that foam PLA patterns effectively eliminate shell cracking during burnout and enhance dimensional accuracy. The findings of this study offer a new approach for improving dimensional tolerances and casting yield in RIC, which has not been previously explored.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".