A parametric design framework for the mass customization of bicycle helmet

Area of Science:

• Anthropometry
• Biomechanical Engineering
• Computer-Aided Design

Background:

• Head shape variations across different races necessitate race-specific design models.
• Existing research on Chinese head shape clustering and variance is limited.
• Designing helmets for Chinese populations requires dedicated head shape data.

Purpose of the Study:

• To cluster Chinese head shapes for mass customization of head-related products.
• To develop and validate an improved clustering algorithm for head shape analysis.
• To create a parametric modeling program for customized bicycle helmet design.

Main Methods:

• Developed an improved k-medoids algorithm with Cluster Validity Index.
• Clustered 339 Chinese young males (18-30 years) into 7 head shape groups.
• Created a parametric modeling program for bicycle helmets based on cluster results.

Main Results:

• The improved k-medoids algorithm showed superior performance over the traditional method.
• Achieved higher sample participation rates and reduced inter-cluster disparities.
• Parametric modeling program demonstrated enhanced helmet fit and wearer comfort via simulations.

Conclusions:

• The developed clustering method effectively categorizes Chinese head shapes.
• The parametric modeling program significantly improves bicycle helmet mass customization.
• This approach enhances helmet fit and comfort, addressing specific user needs.