Login / Signup

Identification of effective control technologies for additive manufacturing.

Johannes Lodewykus du PlessisSonette du PreezAleksandr B Stefaniak
Published in: Journal of toxicology and environmental health. Part B, Critical reviews (2022)
Additive manufacturing (AM) refers to several types of processes that join materials to build objects, often layer-by-layer, from a computer-aided design file. Many AM processes release potentially hazardous particles and gases during printing and associated tasks. There is limited understanding of the efficacy of controls including elimination, substitution, administrative, and personal protective technologies to reduce or remove emissions, which is an impediment to implementation of risk mitigation strategies. The Medline, Embase, Environmental Science Collection, CINAHL, Scopus, and Web of Science databases and other resources were used to identify 42 articles that met the inclusion criteria for this review. Key findings were as follows: 1) engineering controls for material extrusion-type fused filament fabrication (FFF) 3-D printers and material jetting printers that included local exhaust ventilation generally exhibited higher efficacy to decrease particle and gas levels compared with isolation alone, and 2) engineering controls for particle emissions from FFF 3-D printers displayed higher efficacy for ultrafine particles compared with fine particles and in test chambers compared with real-world settings. Critical knowledge gaps identified included a need for data: 1) on efficacy of controls for all AM process types, 2) better understanding approaches to control particles over a range of sizes and gas-phase emissions, 3) obtained using a standardized collection approach to facilitate inter-comparison of study results, 4) approaches that go beyond the inhalation exposure pathway to include controls to minimize dermal exposures, and 5) to evaluate not just the engineering tier, but also the prevention-through-design and other tiers of the hierarchy of controls.
Keyphrases
  • healthcare
  • public health
  • air pollution
  • machine learning
  • life cycle
  • particulate matter
  • municipal solid waste
  • quality improvement
  • risk assessment
  • mechanical ventilation