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Monitoring of particulate matter emissions from 3D printing activity in the home setting

Khaki, Shirin, Duffy, Emer, Smeaton, Alan F. orcid logoORCID: 0000-0003-1028-8389 and Morrin, Aoife orcid logoORCID: 0000-0003-1061-8528 (2021) Monitoring of particulate matter emissions from 3D printing activity in the home setting. Sensors, 21 (9). ISSN 1424-8220

Abstract
Consumer-level 3D printers are becoming increasingly prevalent in home settings. However, research shows that printing with these desktop 3D printers can impact indoor air quality (IAQ). This study examined particulate matter (PM) emissions generated by 3D printers in an indoor domestic setting. Print filament type, brand, and color were investigated and shown to all have significant impacts on the PM emission profiles over time. For example, emission rates were observed to vary by up to 150-fold, depending on the brand of a specific filament being used. Various printer settings (e.g., fan speed, infill density, extruder temperature) were also investigated. This study identifies that high levels of PM are triggered by the filament heating process and that accessible, user-controlled print settings can be used to modulate the PM emission from the 3D printing process. Considering these findings, a low-cost home IAQ sensor was evaluated as a potential means to enable a home user to monitor PM emissions from their 3D printing activities. This sensing approach was demonstrated to detect the timepoint where the onset of PM emission from a 3D print occurs. Therefore, these low-cost sensors could serve to inform the user when PM levels in the home become elevated significantly on account of this activity and furthermore, can indicate the time at which PM levels return to baseline after the printing process and/or after adding ventilation. By deploying such sensors at home, domestic users of 3D printers can assess the impact of filament type, color, and brand that they utilize on PM emissions, as well as be informed of how their selected print settings can impact their PM exposure levels.
Metadata
Item Type:Article (Published)
Refereed:Yes
Uncontrolled Keywords:3D printing; indoor air quality; particulate matter; low-cost sensors
Subjects:UNSPECIFIED
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
DCU Faculties and Schools > Faculty of Science and Health > School of Chemical Sciences
Research Institutes and Centres > National Centre for Sensor Research (NCSR)
Research Institutes and Centres > INSIGHT Centre for Data Analytics
Publisher:MDPI
Official URL:https://dx.doi.org/10.3390/s21093247
Copyright Information:© 2021 The Authors. Open Access (CC-BY 4.0)
ID Code:27396
Deposited On:26 Jul 2022 13:29 by Thomas Murtagh . Last Modified 26 Jul 2022 13:29
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