Reducing aerosol formation from SLS PA12 powder using response surface methodology via optimization of refresh rate and powder handling activities

A critical issue with Selective laser sintering (SLS) is the release of airborne particulates during powder handling, which pose health risks and environmental concerns. Three factors (input variables) were optimized to minimize the following response variables: particulate matter (PM2.5, PM10), ul...

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Main Authors: Muhamad Damanhur, Amir Abdullah, Hariri, Azian, Ab Ghani, Sharin, Md Fauadi, Muhammad Hafidz Fazli, Mustafa, Mohd Syafiq Syazwan, Zakaria, Anies Faziehan, Subrig, Ummu Sakinah
Format: Article
Language:en
Published: 2024
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Online Access:http://eprints.uthm.edu.my/12759/1/J18902_eb1dc87c6ac3e5170822060881592d69.pdf
http://eprints.uthm.edu.my/12759/
https://doi.org/10.31893/multiscience.2025269
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Summary:A critical issue with Selective laser sintering (SLS) is the release of airborne particulates during powder handling, which pose health risks and environmental concerns. Three factors (input variables) were optimized to minimize the following response variables: particulate matter (PM2.5, PM10), ultrafine particles (UFP), and total suspended particles (TSP) using response surface methodology (RSM). The following parameters were found to minimize the PM2.5, PM10, UFP, and TSP during the pre-processing activities of SLS: (1) Factor A: 100% (100% recycled powder), (2) Factor B: 33% (the powder is thirty-three percent cover when it is collected from the mixing machine), and (3) Factor C: full cover (the powder is completely covered when it is transferred to the SLS 3D printer). These optimal settings resulted in the highest desirability of 0.816. Experiments were conducted using the aforementioned settings to validate the results and the percentage difference between the predicted and experimental values was less than 5%, indicating the reliability of the PM2.5, PM10, UFP, and TSP response surface models. These models will be useful to the operators of SLS 3D printing to predict the PM2.5, PM10, UFP, and TSP as a function of the refresh rate and pre-processing activities considered in this study.