Imperial College London
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DrConnorMyant

Faculty of Engineering, Dyson School of Design Engineering

Senior Lecturer
 
 
 
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Contact

 

connor.myant Website

 
 
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Location

 

M224Royal College of ScienceSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Li:2021:10.1108/rpj-10-2020-0231,
author = {Li, S and Waheed, U and Bahshwan, M and Wang, LZ and Kalossaka, LM and Choi, J and Kundrak, F and Lattas, A and Ploumpis, S and Zafeiriou, S and Myant, CW},
doi = {10.1108/rpj-10-2020-0231},
journal = {Rapid Prototyping Journal},
pages = {1302--1317},
title = {A scalable mass customisation design process for 3D-printed respirator mask to combat COVID-19},
url = {http://dx.doi.org/10.1108/rpj-10-2020-0231},
volume = {27},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - PurposeA 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.Design/methodology/approachFour 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.FindingsRespirator 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.Originality/valueA 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 consider
AU  - Li,S
AU  - Waheed,U
AU  - Bahshwan,M
AU  - Wang,LZ
AU  - Kalossaka,LM
AU  - Choi,J
AU  - Kundrak,F
AU  - Lattas,A
AU  - Ploumpis,S
AU  - Zafeiriou,S
AU  - Myant,CW
DO  - 10.1108/rpj-10-2020-0231
EP  - 1317
PY  - 2021///
SN  - 1355-2546
SP  - 1302
TI  - A scalable mass customisation design process for 3D-printed respirator mask to combat COVID-19
T2  - Rapid Prototyping Journal
UR  - http://dx.doi.org/10.1108/rpj-10-2020-0231
UR  - https://www.emerald.com/insight/content/doi/10.1108/RPJ-10-2020-0231/full/html
UR  - http://hdl.handle.net/10044/1/90841
VL  - 27
ER  -
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