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A three-dimensional (3D) printed custom-fit respirator mask has been proposed as a promising solution to alleviate mask-related injuries and supply shortages during COVID-19. However, creating a custom-fit computer-aided design (CAD) model for each mask is currently a manual process and thereby not scalable for a pandemic crisis. This paper aims to develop a novel design process to reduce overall design cost and time, thus enabling the mass customisation of 3D printed respirator masks.

Four data acquisition methods were used to collect 3D facial data from five volunteers. Geometric accuracy, equipment cost and acquisition time of each method were evaluated to identify the most suitable acquisition method for a pandemic crisis. Subsequently, a novel three-step design process was developed and scripted to generate respirator mask CAD models for each volunteer. Computational time was evaluated and geometric accuracy of the masks was evaluated via one-sided Hausdorff distance.

Respirator masks were successfully generated from all meshes, taking <2 min/mask for meshes of 50,000∼100,000 vertices and <4 min for meshes of ∼500,000 vertices. The average geometric accuracy of the mask ranged from 0.3 mm to 1.35 mm, depending on the acquisition method. The average geometric accuracy of mesh obtained from different acquisition methods ranged from 0.56 mm to 1.35 mm. A smartphone with a depth sensor was found to be the most appropriate acquisition method.

A novel and scalable mass customisation design process was presented, which can automatically generate CAD models of custom-fit respirator masks in a few minutes from a raw 3D facial mesh. Four acquisition methods, including the use of a statistical shape model, a smartphone with a depth sensor, a light stage and a structured light scanner were compared; one method was recommended for use in a pandemic crisis considering equipment cost, acquisition time and geometric accuracy.