What question did this study set out to answer?

This study aims to create a framework that uses standardized fabric properties to predict garment fit through virtual prototyping.

A decision support framework for garment fit prediction: standardizing fabric mechanical properties in 3D virtual prototyping

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Randomized trial integrates standardized fabric properties with 3D prototyping to enhance garment fit prediction.

This study aims to create a framework that uses standardized fabric properties to predict garment fit through virtual prototyping.
Collected fabric mechanical data and constructed a dataset.
Used machine learning techniques, including decision trees and multi-target random forest models for prediction.
Evaluated the model's performance based on classification accuracy and regression outcomes.
The sequential model achieved a classification accuracy of 78.3%.
The multi-target random forest attained 97.0% accuracy in deformation-region classification.
Strain and stress prediction coefficients were 0.916 and 0.884 respectively, indicating strong predictive performance.

Phan et al. (2026) studied this question.

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View Full Paper

Ask AI

Bookmark

Authors

Randomized trial integrates standardized fabric properties with 3D prototyping to enhance garment fit prediction.

This study aims to create a framework that uses standardized fabric properties to predict garment fit through virtual prototyping.
Collected fabric mechanical data and constructed a dataset.
Used machine learning techniques, including decision trees and multi-target random forest models for prediction.
Evaluated the model's performance based on classification accuracy and regression outcomes.
The sequential model achieved a classification accuracy of 78.3%.
The multi-target random forest attained 97.0% accuracy in deformation-region classification.
Strain and stress prediction coefficients were 0.916 and 0.884 respectively, indicating strong predictive performance.

Phan et al. (2026) studied this question.

View Full Paper

Ask AI

Bookmark