In Silico Fit Evaluation of Additively Manufactured Face Coverings
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) fu...
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Veröffentlicht in: | Annals of biomedical engineering 2023-01, Vol.51 (1), p.34-44 |
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creator | Carr, Ian A. D’Souza, Gavin Xu, Ming Ozarkar, Shailesh Porter, Daniel Horner, Marc Hariharan, Prasanna |
description | 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. |
doi_str_mv | 10.1007/s10439-022-03026-8 |
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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.</description><identifier>ISSN: 0090-6964</identifier><identifier>ISSN: 1573-9686</identifier><identifier>EISSN: 1573-9686</identifier><identifier>DOI: 10.1007/s10439-022-03026-8</identifier><identifier>PMID: 35902414</identifier><language>eng</language><publisher>Cham: Springer International Publishing</publisher><subject>Adaptability ; Additive manufacturing ; Best practice ; Biochemistry ; Biological and Medical Physics ; Biomedical and Life Sciences ; Biomedical Engineering and Bioengineering ; Biomedicine ; Biophysics ; Classical Mechanics ; COVID-19 ; COVID-19 - epidemiology ; COVID-19 - prevention & control ; Design ; Face ; Humans ; Masks ; Mathematical analysis ; Methodology ; Pandemics ; Pandemics - prevention & control ; Performance evaluation ; Protective equipment ; S.I. : Modeling for Advancing Regulatory Science ; SARS-CoV-2 ; Sizing</subject><ispartof>Annals of biomedical engineering, 2023-01, Vol.51 (1), p.34-44</ispartof><rights>This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2022</rights><rights>2022. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.</rights><rights>This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2022.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c474t-6a471c2641a04f59e3a426e3ef6b1646f2f65419e2fc3821293da547ad8c49733</citedby><cites>FETCH-LOGICAL-c474t-6a471c2641a04f59e3a426e3ef6b1646f2f65419e2fc3821293da547ad8c49733</cites><orcidid>0000-0001-7292-506X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10439-022-03026-8$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10439-022-03026-8$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>230,314,776,780,881,27901,27902,41464,42533,51294</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35902414$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Carr, Ian A.</creatorcontrib><creatorcontrib>D’Souza, Gavin</creatorcontrib><creatorcontrib>Xu, Ming</creatorcontrib><creatorcontrib>Ozarkar, Shailesh</creatorcontrib><creatorcontrib>Porter, Daniel</creatorcontrib><creatorcontrib>Horner, Marc</creatorcontrib><creatorcontrib>Hariharan, Prasanna</creatorcontrib><title>In Silico Fit Evaluation of Additively Manufactured Face Coverings</title><title>Annals of biomedical engineering</title><addtitle>Ann Biomed Eng</addtitle><addtitle>Ann Biomed Eng</addtitle><description>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.</description><subject>Adaptability</subject><subject>Additive manufacturing</subject><subject>Best practice</subject><subject>Biochemistry</subject><subject>Biological and Medical Physics</subject><subject>Biomedical and Life Sciences</subject><subject>Biomedical Engineering and Bioengineering</subject><subject>Biomedicine</subject><subject>Biophysics</subject><subject>Classical Mechanics</subject><subject>COVID-19</subject><subject>COVID-19 - epidemiology</subject><subject>COVID-19 - prevention & control</subject><subject>Design</subject><subject>Face</subject><subject>Humans</subject><subject>Masks</subject><subject>Mathematical analysis</subject><subject>Methodology</subject><subject>Pandemics</subject><subject>Pandemics - prevention & control</subject><subject>Performance evaluation</subject><subject>Protective equipment</subject><subject>S.I. : Modeling for Advancing Regulatory Science</subject><subject>SARS-CoV-2</subject><subject>Sizing</subject><issn>0090-6964</issn><issn>1573-9686</issn><issn>1573-9686</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><recordid>eNp9kT1PwzAQhi0EgvLxBxhQJBaWgM_nOPGCBBUFJBADMFvGscEojYudVOLfk1K-ByYP995zvnsI2QV6CJSWRwkoR5lTxnKKlIm8WiEjKErMpajEKhlRKmkupOAbZDOlZ0oBKizWyQYWkjIOfEROL9vs1jfehGziu-xsrptedz60WXDZSV37zs9t85pd67Z32nR9tHU20cZm4zC30bePaZusOd0ku_PxbpH7ydnd-CK_ujm_HJ9c5YaXvMuF5iUYJjhoyl0hLWrOhEXrxAMILhxzouAgLXMGKwZMYq0LXuq6MlyWiFvkeMmd9Q9TWxvbdlE3ahb9VMdXFbRXvyutf1KPYa4kItISBsDBByCGl96mTk19MrZpdGtDnxQTi7sVUIghuv8n-hz62A7rKVYKBAHIF0C2TJkYUorWfX0GqFooUktFalCk3hWpamja-7nGV8unkyGAy0CaLe5r4_fsf7BvJhSa5Q</recordid><startdate>20230101</startdate><enddate>20230101</enddate><creator>Carr, Ian A.</creator><creator>D’Souza, Gavin</creator><creator>Xu, Ming</creator><creator>Ozarkar, Shailesh</creator><creator>Porter, Daniel</creator><creator>Horner, Marc</creator><creator>Hariharan, Prasanna</creator><general>Springer International Publishing</general><general>Springer Nature B.V</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QF</scope><scope>7QO</scope><scope>7QQ</scope><scope>7SC</scope><scope>7SE</scope><scope>7SP</scope><scope>7SR</scope><scope>7TA</scope><scope>7TB</scope><scope>7U5</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8BQ</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F28</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H8D</scope><scope>H8G</scope><scope>HCIFZ</scope><scope>JG9</scope><scope>JQ2</scope><scope>K9.</scope><scope>KR7</scope><scope>L6V</scope><scope>L7M</scope><scope>LK8</scope><scope>L~C</scope><scope>L~D</scope><scope>M0S</scope><scope>M1P</scope><scope>M7P</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0001-7292-506X</orcidid></search><sort><creationdate>20230101</creationdate><title>In Silico Fit Evaluation of Additively Manufactured Face Coverings</title><author>Carr, Ian A. ; D’Souza, Gavin ; Xu, Ming ; Ozarkar, Shailesh ; Porter, Daniel ; Horner, Marc ; Hariharan, Prasanna</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c474t-6a471c2641a04f59e3a426e3ef6b1646f2f65419e2fc3821293da547ad8c49733</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Adaptability</topic><topic>Additive manufacturing</topic><topic>Best practice</topic><topic>Biochemistry</topic><topic>Biological and Medical Physics</topic><topic>Biomedical and Life Sciences</topic><topic>Biomedical Engineering and Bioengineering</topic><topic>Biomedicine</topic><topic>Biophysics</topic><topic>Classical Mechanics</topic><topic>COVID-19</topic><topic>COVID-19 - 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subjects | Adaptability Additive manufacturing Best practice Biochemistry Biological and Medical Physics Biomedical and Life Sciences Biomedical Engineering and Bioengineering Biomedicine Biophysics Classical Mechanics COVID-19 COVID-19 - epidemiology COVID-19 - prevention & control Design Face Humans Masks Mathematical analysis Methodology Pandemics Pandemics - prevention & control Performance evaluation Protective equipment S.I. : Modeling for Advancing Regulatory Science SARS-CoV-2 Sizing |
title | In Silico Fit Evaluation of Additively Manufactured Face Coverings |
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