Microstructural and Mechanical Integrity of 3D Printed 316L Stainless Steel
Bibliographic record
Abstract
Hindrance to the advancement of materials processing and components using metal-based additive manufacturing is a result of numerous challenges due to the complex mechanisms that occur such as multiple modes of heat, mass, and momentum transfers induced by localized laser scanning. This results in processing defects such as gas entrapment, unmelted and over-melted powders, aggregation of constituent phases and microcracks that affect the integrity of the printed parts. To avoid defects/flaws in parts and establish relationships between process parameters and part quality, it is critical to understanding the effect of processing parameters on the evolution of microstructural heterogeneities which influence the properties and quality of parts fabricated via AM. In this study, the effect of DMLS parameters on part quality and microstructural evolution is studied as a baseline for tailoring the microstructure of parts for a specific application. Various printing parameters are combined to create fifteen samples, which are then studied extensively to find parameters that create good microstructure and mechanical behavior. The Volumetric Energy Densities (VEDs) is the identifying parameter used here to state a range where samples with good integrity can be fabricated. It was observed that during the processing of 316L stainless steel using Direct Metal Laser Sintering (DMLS), parts with good microstructural integrity as well as high performance were obtained when Volumetric Energy Densities (VEDs) between 45 and 110 J/mm3 are used. The volume fraction of surface porosities, unmelted powders, over-melted regions and consequently part quality and performance were compromised when VEDs below this range were chosen but above the range, more gas pores are introduced into the sample. The range above the suitable can be used for the part required in biomedical application, components for heat exchangers or parts placed in front of car bumpers for energy absorption. For structural application, the total elimination of pores is required hence using a higher laser power in combination with large hatch spacing, or a combination of low power with a small hatch spacing is going to yield a better microstructure. The scan speed is the most sensitive parameter of the three individual parameters even though how sensitive the scan speed depends on the other parameters. The evolution of microstructure features is also very dependent on the printing parameters. Within the range of acceptable VEDs that produced parts with good microstructural integrity and performance, it was observed that the laser power, scanning speed and hatch spacing had a significant effect on the evolution of columnar and cellular sub-grain structures. Increasing the laser power and scan speeds resulted in thicker and well-defined cellular and columnar subgrains with relatively lower hardness while an increase in hatch spacing leads to less developed substructures. Laser power, scan speed, and hatch spacing also affect the wear resistance of the sample. Increasing power improves the wear behavior of the sample if the hatch spacing of 100 m and above but tends to reduce the wear performance if using small hatch spacing. Thus, samples within the acceptable range also exhibit better mechanical properties in terms of higher hardness values and good wear properties. These mechanical properties are compromised when the heat accumulation in the sample during fabrication is very high.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".