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.

翻译：增材制造（AM）部件中的缺陷深度量化仍然是无损检测（NDT）领域的一项重大挑战。本研究提出了一种像素级定量热成像神经网络（PQT-Net）来解决聚乳酸（PLA）部件的这一挑战。一个关键创新是一种新颖的数据增强策略，它将热序列数据重构为二维条纹图像，从而保留了每个像素热扩散的完整时间演化过程。PQT-Net架构包含一个预训练的EfficientNetV2-S主干网络和一个带有可学习参数的自定义残差回归头（RRH），用于优化输出。对比实验证明了PQT-Net相对于其他深度学习模型的优越性，其实现了0.0094毫米的最小平均绝对误差（MAE），且决定系数（R）超过99%。PQT-Net的高精度凸显了其在增材制造中进行稳健定量缺陷表征的潜力。