Effect of microstructural evolution during melt pool formation on nano-mechanical properties in LPBF based SS316L parts

Jeyaprakash, N.; Saravana Kumar, M.; Yang, Che-Hua; Cheng, Yanhai; Radhika, N.; Sivasankaran, S.

doi:10.1016/j.jallcom.2023.172745

Cited by 10 publications

(1 citation statement)

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“…There is also increasing business interest in developing other custom parameters, for example, to print amorphous crystal structures [27]. Research has also looked at controlling the microstructure via process parameter engineering in order to analyze and tailor materials' mechanical performance [28,29]. This increases the need for parameter sets-or, more specifically, it requires an agile way of creating parameters to meet quality, performance, and productivity demands.…”

Section: Introductionmentioning

confidence: 99%

Metal Laser-Based Powder Bed Fusion Process Development Using Optical Tomography

Björkstrand,

Akmal,

Salmi

2024

Materials

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In this study, a set of 316 L stainless steel test specimens was additively manufactured by laser-based Powder Bed Fusion. The process parameters were varied for each specimen in terms of laser scan speed and laser power. The objective was to use a narrow band of parameters well inside the process window, demonstrating detailed parameter engineering for specialized additive manufacturing cases. The process variation was monitored using Optical Tomography to capture light emissions from the layer surfaces. Process emission values were stored in a statistical form. Micrographs were prepared and analyzed for defects using optical microscopy and image manipulation. The results of two data sources were compared to find correlations between lack of fusion, porosity, and layer-based energy emissions. A data comparison of Optical Tomography data and micrograph analyses shows that Optical Tomography can partially be used independently to develop new process parameters. The data show that the number of critical defects increases when the average Optical Tomography grey value passes a certain threshold. This finding can contribute to accelerating manufacturing parameter development and help meet the industrial need for agile component-specific parameter development.

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Section: Introductionmentioning

confidence: 99%