- Multimodal Data Fusion in 3D Printing Quality Prediction
- Project Year:
2018
- REU Student (s):
Xiaotong Gui | Pomona College CA
| Xinru Liu | Wheaton College MA
- Student 1 Institution:
Pomona College
- Student 2 Institution:
Wheaton College
- Project Mentor:
Weihong 'Grace' Guo
- Project Mentor Area:
Industrial and Systems Engineering
- Project Abstract:
This study focuses on the analysis of 3D surface measurements and quality prediction of 3D-printed dome-shaped objects using multimodal data fusion. Dimension, profile, and surface roughness were measured and represented in image data. Dimension reduction techniques were employed for extracting spatial patterns from the measurement images. Quality metrics were developed using profile deviation and surface roughness. In the end, classification and regression models were built to predict quality. The results propose feature extraction from high-dimensional image data as a promising technique for efficient and automated quality inspection.
