Evaluating Clad Formations in Directed Energy Deposition Process Using a Dimensionless Analysis
Bibliographic record
Abstract
Abstract The growing interest in the directed energy deposition process in different industries has warranted a deeper understanding in the properties of the basic building block of the method: clad formation. In this study, the clad formation obtained by depositing stainless steel 316L (SS316L) powder with different laser traverse speed, laser power, and powder flow rate, were investigated. Repeatability was ensured through a wide sample range per parameter. The clad showed an increase in height with an increase in powder flow rate and a decrease in traverse speed. A deeper melt pool was formed with a higher laser power. Dimensionless analysis suggested that these effects of the parameters generally scale with a non-dimensional combination of the process parameters considered, which can be easily determined before printing and hence practical for applications. The analysis also revealed that different regimes of clad formation can be demarcated by the linear energy density, another term that relies only on process parameters that are prescribed. A critical linear energy density for printing of SS316L clads in this study was observed, below which resulted in poor adhesion to the substrate with occasional delamination. Interestingly, once the critical linear energy density was met, regardless of the variations in one or more of the parameters, there was generally no difference in hardness except for a few cases. These anomalies were further investigated using the electron back scattered diffraction technique, which illustrated how the microstructure of a clad will affect its macroscopic property such as hardness. With its ability to extract a wealth of insights, the procedure from this work will be useful for obtaining a range of optimal process parameters based on the type of application that is required of a directed energy deposition process.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".