Toward Mass Customization Through Additive Manufacturing: An Automated Design Pipeline for Respiratory Protective Equipment Validated Against 205 Faces

Abstract

Respiratory protective equipment (RPE) is traditionally designed through anthropometric sizing to enable mass production. However, this can lead to long-standing problems of low-compliance, severe skin trauma, and higher fit test failure rates among certain demographic groups, particularly females and non-white ethnic groups. Additive manufacturing could be a viable solution to produce custom-fitted RPE, but the manual design process is time-consuming, cost-prohibitive and unscalable for mass customization. This paper proposes an automated design pipeline which generates the computer-aided design models of custom-fit RPE from unprocessed three-dimensional (3D) facial scans. The pipeline successfully processed 197 of 205 facial scans with <2 min/scan. The average and maximum geometric error of the mask were 0.62 mm and 2.03 mm, respectively. No statistically significant differences in mask fit were found between male and female, Asian and White, White and Others, Healthy and Overweight, Overweight and Obese, Middle age, and Senior groups.