Evaluation of Direct Rapid Prototyping Pattern for Investment Casting
Bibliographic record
Abstract
The ability of rapid prototyping (RP) technology to fabricate direct part of any complex shape as a sacrificial pattern in shorter lead time has benefited the foundry industries significantly. The quality of investment casting (IC) parts is directly related to the master pattern fabricated from RP process. The main objective of this study was to evaluate the quality characteristics of various RP patterns that were fabricated by various RP processes which include 3D Printer (3DP), Fused Deposition Modeling (FDM) and Multijet Modeling (MJM). Evaluation of the RP patterns was carried out on dimensional accuracy, surface roughness and pattern shrinkage. Different internal pattern designs for the RP parts were developed using Insight software for FDM process and Solidworks 2011 for other RP systems. In addition to the quality assessments, the effect of the internal pattern designs on the burn out behaviour of the RP patterns was also evaluated. Experimental results showed that FDM and MJM produced patterns with better accuracy, surface roughness and part shrinkage when compared to 3DP. It was evident that the internal pattern structure improved the accuracy of the patterns produced from all RP processes. Results showed that FDM and MJM processes were superior in terms of mold cleanliness when no residual ash was observed during the burn out stage. Significant oxidation of ceramic powder was observed on the molds of the 3DP patterns which need to be removed manually from the molds.
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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 | 0.001 | 0.003 |
| 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) | 0.001 | 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".