In Silico Fit Evaluation of Additively Manufactured Face Coverings

Abstract

In response to the respiratory protection device shortage during the COVID-19 pandemic, the additive manufacturing (AM) community designed and disseminated numerous AM face masks. Questions regarding the effectiveness of AM masks arose because these masks were often designed with limited (if any) functional performance evaluation. In this study, we present a fit evaluation methodology in which AM face masks are virtually donned on a standard digital headform using finite element-based numerical simulations. We then extract contour plots to visualize the contact patches and gaps and quantify the leakage surface area for each mask frame. We also use the methodology to evaluate the effects of adding a foam gasket and variable face mask sizing, and finally propose a series of best practices. Herein, the methodology is focused only on characterizing the fit of AM mask frames and does not considering filter material or overall performance. We found that AM face masks may provide a sufficiently good fit if the sizing is appropriate and if a sealing gasket material is present to fill the gaps between the mask and face. Without these precautions, the rigid nature of AM materials combined with the wide variation in facial morphology likely results in large gaps and insufficient adaptability to varying user conditions which may render the AM face masks ineffective.

Measuring Total Filtration Efficiency of Surgical and Community Face Masks: Impact of Mask Design Features

“…Indeed, the median [fist quartile; third quartile] values of TFE measured without nosepiece at the three flow rates were 12 [9,15] %, 21 [17,29] % and 32 [25,39] % respectively. With the nosepiece in place, the same group of masks tested in identical testing conditions presented TFE values of 15 [13,19] %, 28 [22,33] % and 40 [31,45] %. The comparison of TFE values obtained with and without nosepiece over the 26 tested masks indicated that a significant increase in TFE was achieved when the nosepiece was present (p<0.05, Wilcoxon rank-sum test).…”

“…ASTM has recently provided a framework for the specifications of barrier face covering performance [18]. However, the document recognizes that no accepted methodologies were defined to measure total filtration efficiency from loose-fitting face masks and face coverings despite recent studies have demonstrated the relation of mask design features and fitting with air leakage and overall filtration performance [19], [20], [21], [22].…”

“…Further studies on face mask fitting were also conducted with Finite Element Analysis and aimed at studying the contact pressure between mask and facial anatomy [39]. Additive manufacturing (AM) was investigated in silico by Carr et al as a mean to optimize fitting and reduce leakage, obtaining an effective sealing only by filling the gap between the mask and the face with an adaptable gasket added to the rigid AM materials [19]. Breathability, dependent on the mask filtering material characteristics, also emerged as an important parameter influencing mask filtration efficiency via its strong causal relationship with leakage [40], [27].…”