Automated Custom Sunglasses Frame Design Using Artificial Intelligence and Computational Design
Abstract
Mass production in product design typically relies on standardized geometries and dimensions to accommodate a broad user population. However, when products are required to interface directly with the human body, such generalized design approaches often result in inadequate fit and reduced user comfort. This limitation highlights the necessity of fully personalized design methodologies based on individual anthropometric characteristics. This paper presents a novel application that automates the design of custom-fit sunglasses through the integration of Artificial Intelligence (AI) and Computational Design. The system is implemented using both textual (Python™ version 3.10.11) and visual (Grasshopper 3D™ version 1.0.0007) programming environments. The proposed workflow consists of the following four main stages: (a) acquisition of user facial images, (b) AI-based detection of facial landmarks, (c) three-dimensional reconstruction of facial features via an optimization process, and (d) generation of a personalized sunglass frame, exported as a three-dimensional model. The application demonstrates a robust performance across a diverse set of test images, consistently generating geometries that conformed closely to each user’s facial morphology. The accurate recognition of facial features enables the successful generation of customized sunglass frame designs. The system is further validated through the fabrication of a physical prototype using additive manufacturing, which confirms both the manufacturability and the fit of the final design. Overall, the results indicate that the combined use of AI-driven feature extraction and parametric Computational Design constitutes a powerful framework for the automated development of personalized wearable products.
Fields of Study & Subjects
Architecture and Computational Design
3D Shape Modeling and Analysis
Product Development and Customization
Publication details
- Journal / source
- Machines
- Volume
- 14
- Issue
- 1
- Pages
- 109–109
- Publication date
- 17 January 2026
- Language
- EN