PQTNet: Pixel-wise Quantitative Thermography Neural Network for Estimating Defect Depth in Polylactic Acid Parts by Additive Manufacturing

TL;DR

This paper introduces PQT-Net, a neural network that accurately estimates defect depths in 3D-printed PLA parts using thermal imaging, leveraging a novel data augmentation and a specialized architecture for improved precision.

Contribution

The study presents a novel neural network architecture with a unique data augmentation method for precise defect depth estimation in additive manufacturing.

Findings

Achieved a minimum MAE of 0.0094 mm in defect depth estimation.

Demonstrated R > 99% indicating high model accuracy.

Outperformed other deep learning models in quantitative defect characterization.

Abstract

Defect depth quantification in additively manufactured (AM) components remains a significant challenge for non-destructive testing (NDT). This study proposes a Pixel-wise Quantitative Thermography Neural Network (PQT-Net) to address this challenge for polylactic acid (PLA) parts. A key innovation is a novel data augmentation strategy that reconstructs thermal sequence data into two-dimensional stripe images, preserving the complete temporal evolution of heat diffusion for each pixel. The PQT-Net architecture incorporates a pre-trained EfficientNetV2-S backbone and a custom Residual Regression Head (RRH) with learnable parameters to refine outputs. Comparative experiments demonstrate the superiority of PQT-Net over other deep learning models, achieving a minimum Mean Absolute Error (MAE) of 0.0094 mm and a coefficient of determination (R) exceeding 99%. The high precision of PQT-Net underscores its potential for robust quantitative defect characterization in AM.