Effect of processing parameters on the microhardness, shear, and tensile properties of layer-cladded Inconel 718
Bibliographic record
Abstract
Inconel 718 alloy (IN718) is a popular choice for aerospace hot-end components due to its exceptional mechanical properties. This study investigates the impact of processing parameters (i.e., laser power, powder feeding rate, and scanning speed) on the microhardness, bond strength, and tensile strength of layer-cladded IN718. The results show that IN718 coatings have high microhardness (277.15 HV0.1), strong metallurgical bonding strength (33.97 kN), substantial yield strength (794.09 MPa), impressive ultimate tensile strength (1171.81 MPa), and notable elongation at failure (8.24 %) under laser power is 1.2 kW, powder feeding rate is 250 mg/s, and scanning speed is 4.5 mm/s. In particular, decreased laser energy input enhances microhardness, yield strength, and ultimate tensile strength but reduces bonding strength and elongation. This is attributed to improved Nb element segregation, the reduction of the Laves phase, and grain refinement. However, inadequate energy input leads to cracks and unmelted powder, negatively affecting metallurgical bonding strength. The shear and tensile fracture mechanism of the IN718 coatings is a typical ductile fracture with a microvoid accumulation fracture. The study can facilitate the fabrication of the IN718 coatings with the excellent mechanical properties and the applications in the engineering field.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.000 | 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".