Measurement of Form Errors and Comparative Cost Analysis for the Component Developed by Metal Printing (DMLS) and Stir Casting
Bibliographic record
Abstract
In the continuously changing scenario of manufacturing industries, the demand for rapid production and specific material component is increased day by day. In this context, the additive manufacturing technique proves a suitable option to develop complex geometry shapes with optimized use of the material as well as energy. In this work, an attempt is to develop a 3D physical component of connecting rod by direct metal laser sintering (DMLS) process. The process parameters such as scanning speed 6m/s, laser power 200 W, layer thickness of 25 µm were kept constant. The same geometry component is also produced by a traditional stir casting method to compare the dimensional accuracy and deviations. The CAD model of the connecting rod was prepared by CATIA V6. All the dimensions were measured by a counter measuring machine (CMM). The surface roughness of both the final product was also measured to discuss the surface quality and physical surface defects. In addition to it, a cost analysis of both the process to develop the same component is also discussed. From the result, it is found that the dimensional error for 3D metal printing component is quite low and occurred in the range of 4 % to 7% in XY, YZ radial and circular plane direction compared to stir casting component 4% to 10% in the same planes. The surface roughness value Ra and Rz for the 3D metal printing surface (2.339 and 8.439 µm ) were quite low compared to stir cast surface (4.417 and 13.372 µm). However, the overall cost of 3D metal printing is higher than the stir casting component.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".