Selective Laser Melting of Stainless Steels: A review of Process, Microstructure and Properties
Bibliographic record
Abstract
Metal additive manufacturing is revolutionizing how we produce and use materials. Selective Laser Melting (SLM) is one of the most popular additive manufacturing techniques for creating high-performance metal components. Stainless Steel is preferred for additive manufacturing due to its powder form availability, low cost, mechanical properties, and corrosion resistance. However, the complex thermal history and rapid solidification in the SLM process led to an out-of-equilibrium microstructure of resulting components, which can affect their mechanical properties. To better understand the relationship between processing, microstructure, and properties, exploring and enhancing SLM-fabricated stainless-steel components is essential. This review comprehensively overviews the selective laser melting process, key processing parameters, and commonly encountered defects. Furthermore, the study presents a detailed discussion of microstructure, mechanical behavior (including hardness, tensile, and fatigue properties), and corrosion resistance of all SLM-manufactured stainless steel grades, along with the effects of various post-process treatments. This paper reveals that the SLM process can produce stainless steel with satisfactory performance that may exceed conventionally processed materials. However, the final section highlights the challenges and research gaps in this field that must be addressed. Graphical Abstract
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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 | 0.001 |
| 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".