用深度学习精准预测3D打印塑料件缺陷深度
PQTNet: Pixel-wise Quantitative Thermography Neural Network for Estimating Defect Depth in Polylactic Acid Parts by Additive Manufacturing
- 将热成像序列转为二维条纹图，保留每像素的完整热扩散过程
- 最小平均绝对误差达0.0094毫米，决定系数超过99%
- 适合需要高精度缺陷检测的增材制造质量控制场景
增材制造（AM）部件中的缺陷深度量化仍是无损检测（NDT）的重大挑战。本研究提出一种像素级定量热成像神经网络（PQT-Net），用于聚乳酸（PLA）零件的缺陷深度估计。核心创新在于一种新型数据增强策略，将热序列数据重构为二维条纹图像，完整保留每个像素的热扩散时间演化特征。PQT-Net采用预训练EfficientNetV2-S主干网络，并结合自定义可学习残差回归头（RRH）以优化输出。对比实验表明，PQT-Net优于其他深度学习模型，在测试集上实现最低平均绝对误差（MAE）0.0094 mm，决定系数（R）超过99%。该高精度性能凸显其在增材制造中进行可靠定量缺陷表征的潜力。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。