Substract Surface Quality Influence on LP-DED Process: Exploring Effects on Bead Characteristics and Microstructure
Bibliographic record
Abstract
Abstract Laser Powder – Directed Energy Deposition (LP-DED) is an Additive Manufacturing (AM) technology that uses metal powder to create structures and repair worn components. Many published works have explored the operational parameters, such as laser power (P), mass flow rate (ṁ) and travel speed (Vf), relating them with the resultant mechanical properties. Increasingly, studies have extended into material selection and modifications of it. The LP-DED process can have some notable applications in mold repair using AISI H13 steel and previous investigations have assessed heat treatments and microstructural characteristics when building and post-processing those repairs. However, the aspect of different substrate surface conditions remains unexplored. This research examines the effects of surface conditions on deposition outcomes using AISI H13 steel for substrate and deposition material. Substrate surface machined, sand blasted and polished before deposition. SEM was used to analyze the cross-section and determine how surface quality influences the bead integrity. The results reveal that as machined surfaces show a dilution variability of 28% on the same bead, while as blasted surface provide more stable bead areas. Additionally, the Heat-Affected Zone (HAZ) in the polished surface deposition was 18% smaller than observed in as machined surface. The study further includes evaluations of depositions area, dilution percentages, and micro-hardness, offering insights into the optimal substrate conditions for L-DED applications.
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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 | 0.000 | 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 | 0.001 | 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".