An innovative monitoring method using a software capable of 3D mapping data from laser Directed Energy Deposition (L-DED) process
Bibliographic record
Abstract
In metal additive manufacturing, the complex thermal activity of newly deposited layers and its influence in previously deposited material affects the part's shape and quality.With this regard, the aim of this research is to develop a methodology for monitoring laser power, feed speed and melt pool to evaluate effective material joining and maintenance of good deposited layers on the build of metal parts.This novel methodology combines the data acquisition from a L-DED hybrid machine with a cladding head with 2 mm laser spot size in focus.To aid the monitoring method, some software were developed (DTConnect, MPImageGrabber, MPImageProcessor, DTMap2D and DTMap3D) and tested in four geometries: zigzag line and thin wall (2D); a pyramid, and a pyramid mould (3D).The 3D geometries were printed at four different laser configurations (500 W , 550 -450 W , 700 W and 800 -700 W ), at the constant feed speed of 600 mm/min, and mass flow rate of 8.3 g/min, under the scanning strategies of contour and zigzag.These parameters were defined to promote one set that presents major defects and other with uniform microstructure.The pyramid built with 550 -450 W in zigzag strategy has presented the higher percentage of porosity, estimated in 2.76%, whilst the set of 500 W produced the lowest (1.36%).Overall, the 3D builds printed with 500 W have presented defects such as lack of fusion, poor dilution, and porosity.The percentage of porosity has decreased considerably (> 5 times) with the increase of laser power to 700 W and 800 -700 W , which significantly enhanced the quality and homogeneity in both geometries, highly mitigating the defects aforementioned.Each one of the software designed plays an important role from data acquiring and processing, to its graphic representation.Regarding all conditions tested, both DTMap2D and DTMap3D were able to display the process variables of interest in an interactive color map, therefore making human spatially identification of minor changes in the dataset easier.This makes the DTMap3D a potential tool to speed up the identification of critical regions for post-build inspection.This study contributes towards further knowledge in metal additive manufacturing by bringing to the field a monitoring methodology with new monitoring tools, which easy the correlation between printing parameters and the as-built metal workpiece quality.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".