PQTNet：用于通过增材制造估计聚乳酸部件缺陷深度的像素级定量热成像神经网络
PQTNet: Pixel-wise Quantitative Thermography Neural Network for Estimating Defect Depth in Polylactic Acid Parts by Additive Manufacturing
AI总结：
PQTNet通过像素级定量热成像神经网络，实现了对增材制造聚乳酸部件缺陷深度的高精度估计，具有高精度和鲁棒性的定量缺陷表征能力。
摘要：
在增材制造（AM）组件中，缺陷深度量化仍然是非破坏性检测（NDT）中的重大挑战。本研究提出了一种像素级定量热成像神经网络（PQT-Net），以解决聚乳酸（PLA）部件中的这一挑战。关键创新是新的数据增强策略，将热序列数据重建为二维条纹图像，保留每个像素的完整热扩散时间演化。PQT-Net架构结合了预训练的EfficientNetV2-S主干网络和自定义的残差回归头（RRH）以及可学习参数以优化输出。比较实验表明，PQT-Net优于其他深度学习模型，实现了最小的平均绝对误差（MAE）为0.0094毫米和超过99%的决定系数（R）。PQT-Net的高精度凸显了其在AM中进行稳健定量缺陷表征的潜力。
英文摘要：
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.
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