3DBODY.TECH 2020 - Paper 20.33

L. Degueldre et al., "Improving the Fit of Respiratory Face Masks through 3D Scanning, Finite Elements Analysis and Additive Manufacturing", Proc. of 3DBODY.TECH 2020 - 11th Int. Conf. and Exh. on 3D Body Scanning and Processing Technologies, Online/Virtual, 17-18 Nov. 2020, #33, https://doi.org/10.15221/20.33.


Improving the Fit of Respiratory Face Masks through 3D Scanning, Finite Elements Analysis and Additive Manufacturing


Loic DEGUELDRE 1, Jonathan BORDUAS 2, Francis DION 2, Patrick LAURIN 2, Aude CASTONGUAY 2, Sean-Philippe VIENS 2, Franck LE NAVEAUX 1, Bahe HACHEM 1, David BENOIT 1, Julien CLIN 1

1 Numalogics, Montreal (QC), Canada;
2 Technologies ShapeShift 3D Inc., Montreal (QC), Canada


The 2020 COVID-19 pandemic sheds a new light on the importance of wearing a mask to prevent virus propagation. A respiratory mask’s efficiency is highly dependent on its adjustment with the face. With traditional face masks, achieving a perfect seal with the face proves to be challenging, even impossible without the aggressive tightening of its straps. Healthcare workers typically wear protective face masks for several consecutive hours, leading to discomfort, inflammation, or more serious injuries.
This paper highlights how the combination of current technologies such as 3D scanning, Finite Element simulation, machine learning, and additive manufacturing offers a seamless workflow to generate a sterilizable, reusable and validated custom-fit mask.
The process starts with a 3D face scan acquired from a mobile 3D scanning device. From this scan, the software Shapeshift3D automatically repairs the 3D scan and fits the mask to the facial features. A custom Finite Element model is created by morphing the mesh of a generic face model and its corresponding mask onto the person’s face. The fit, achieved through customization, is then assessed via biomechanically simulating the mask’s tightening while accounting for the face-mask interface and the facial tissue’s behavior. A real time assessment of the mask’s sealing properties and the pressure on the skin is made possible by a machine learning algorithm that was trained on a database of face mask tightening simulations (ANSYS software). This approach allows to quantify in real-time the level of fit achieved through customization. The custom mask is then 3D printed and assembled before being shipped to the end-user.


3D Face Scan, 3D scanning, custom fit, Additive Manufacturing, Machine Learning, Finite Element Analysis, Real-Time Simulation, Cloud Computing, Biomechanics, Mesh Morphing


Full paper: 2033degueldre.pdf
Proceedings: 3DBODY.TECH 2020, 17-18 Nov. 2020, Online/Virtual
Paper id#: 33
DOI: 10.15221/20.33
Presentation video: 2033degueldre.mp4

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