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.
The emergence of additive manufacturing (AM) technologies, such as 3D printing and laser cutting, has created opportunities for new design practices covering a wide range of fields and a diversity of learning and teaching settings. The potential health impact of particulate matter and volatile organic compounds (VOCs) emitted from AM technologies is, therefore, a growing concern for makers. The research behind this paper addresses this issue by applying an indoor air quality assessment protocol in an educational fabrication laboratory. The paper presents the evaluation of the particle emission rate of different AM technologies. Real-time monitoring of multiple three-dimensional Polylactic Acid (PLA), Acrylonitrile Butadiene Styrene (ABS) and Thermoplastic Elastomers (TPE) printers and Polymethyl methacrylate (PMMA) laser cutters was performed in different usage scenarios. Non-contact electrical detectors and off-line gas chromatography–mass spectrometry (GC-MS) were used to detect VOCs. The results show that the emitted particle surface area concentrations vary between 294 and 406.2 μm2/cm3 for three-dimensional printers, and between 55.06 and 92.3 μm2/cm3 for laser cutters. The experiments demonstrate that the emission concentrations were highly dependent on the filtration systems in place. The highest quantities of VOCs emitted included Cyclohexene and Benzyl Alcohol for PLA, ABS and TPE 3D printers, and formic acid and Xylene for PMMA laser cutters. The experiment concludes that signature emissions are detectable for a given material type and an AM technology pair. A suitable mitigation strategy can be specified for each signature detected. Finally, this paper outlines some guidelines for improving indoor air quality in such specific environments. The data provided, as well as the proposed indoor air quality protocol, can be used as a baseline for future studies, and thus help to determine whether the proposed strategies can enhance operator and bystander safety.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.