Influence of Selective Laser Melting Process and Heat Treatment Parameters on the Corrosion Resistance of 17-4 Precipitation Hardening Stainless Steel
Bibliographic record
Abstract
Selective laser melting (SLM) is an advanced additive manufacturing technique that enables the fabrication of complex metal components with high precision. However, inadequate parameter optimization can lead to defects that compromise the corrosion resistance of fabricated parts. Therefore, optimizing both SLM and heat treatment parameters is essential for enhancing electrochemical properties. The present work aims to determine the effect of the SLM process and heat treatment parameters on the corrosion resistance of SLM-made 17-4 PH stainless steel. A set of SLM and heat treatment parameters (laser power, scanning speed, aging time, and aging temperature) was determined by employed Taguchi method and a set of cyclic potentiodynamic polarization and electrochemical impedance experiments was performed in 3.5 wt% NaCl solution to generate corrosion data. The Taguchi method and statistical analysis of variance reveal the effect of laser power, scanning speed, aging time, and aging temperature on corrosion current density and passive film resistance of the SLM-made 17-4 PH samples. Laser power and aging temperature had the most significant effects, with lower laser power and higher aging temperature leading to decreased corrosion resistance, as indicated by higher corrosion current density and lower passive film resistance. Additionally, this study proposes empirical predictive models to estimate the electrochemical properties of SLM-made 17-4 PH stainless steel.
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 | 0.001 |
| 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 | 0.000 | 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".