Additive manufacturing of AISI 304L stainless steel: A review of processing parameters and mechanical performance
Bibliographic record
Abstract
Additive manufacturing (AM) has become a favorable method for producing 304L stainless steel (SS) for various industrial applications, which is owing to its favorable characteristics including corrosion resistance, mechanical performance, and design flexibility. This review paper presents a comprehensive overview of the processing factors along with the mechanical performance of AM-fabricated 304L SS (AM304LSS). Firstly a discussion is provided for the fundamental principles of AM techniques that are common for processing SS304L. This includes selective laser melting (SLM), laser beam powder bed fusion (LB-PBF), direct metal laser sintering (DMLS), directed energy deposition (DED), wire-and-arc additive manufacturing (WAAM). Subsequently, the impact of key processing factors i.e. laser power, and powder characteristics on the microstructure and mechanical properties of AM304LSS is presented. In addition, this article examines recent progress in process optimization strategies and post-processing techniques for improving and enhancing the mechanical properties and surface finish of AM 304L stainless steel components. Finally, significant insights are provided for researchers, engineers, and practitioners involved in the advancement and application of AM304LSS components.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".