Optimization of Nd:YAG-Laser Welding Process for Inconel 718 Alloy
Bibliographic record
Abstract
Ni-based superalloys are extensively used in the manufacture of aircraft engine components because of their excellent heat-resistant and corrosion-resistant properties. The principal joining processes for Ni-based superalloys are TIG welding, MIG welding, submerged arc welding, electron beam welding, and CO2 laser welding. In this investigation, a robotic 4-kW continuous-wave Nd:YAG laser system was used to identify the optimal laser welding process for 2.0 mm thick Inconel (IN) 718 sheets. The effect of various processing parameters, which included power input, welding speed, weld geometry and filler wire, was studied using the Taguchi design of experiment (DOE) methodology. The DOE methodology enabled the evaluation of the relationship between the process parameters and the quality of the welded joints, from which the optimal Nd:YAG laser welding process was developed for IN718 alloy. Joint quality was examined by tensile and nondestructive testing methods. Using the optimal process established in this research, mechanically-sound welds with narrow fusion and heat-affected zones were produced. The outcome of this research demonstrates the feasibility of the application of Nd:YAG laser in the joining of IN718 sheets for the manufacture of aircraft engine components.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".