用深度学习分析热成像，实时判断超声波增材制造质量
Advanced Predictive Quality Assessment for Ultrasonic Additive Manufacturing with Deep Learning Model
- 基于热图像和卷积神经网络，实现过程质量分类
- 多场景模型准确率均超97%，最高达98.29%
- 适合制造业质检与工艺控制，尤其有传感器的复杂场景
超声波增材制造（UAM）通过超声波焊接将金属箔片逐层粘合至基底，形成致密金属构件。但某些工艺条件可能导致层间缺陷，影响最终产品质量。本研究提出一种基于深度学习的卷积神经网络（CNN）方法，利用热成像进行在制品质量监测。在五种功率水平（300W、600W、900W、1200W、1500W）下，对带与不带热电偶的样本进行监督标注分类。构建了四种不同场景的CNN模型：无热电偶（基准）、有热电偶、仅无热电偶跨功率、仅有热电偶跨功率，以及两者结合跨功率。结果显示，结合基准与热电偶图像的模型准确率达98.29%，基准图像跨功率为97.10%，热电偶图像为97.43%，两者跨功率为97.27%。所有模型准确率均高于97%，证明该系统可有效识别并分类UAM过程中的工艺状态，为制造环境中的质量保证与过程控制提供可靠工具。
原文摘要 · Abstract (English)
Ultrasonic Additive Manufacturing (UAM) employs ultrasonic welding to bond similar or dissimilar metal foils to a substrate, resulting in solid, consolidated metal components. However, certain processing conditions can lead to inter-layer defects, affecting the final product's quality. This study develops a method to monitor in-process quality using deep learning-based convolutional neural networks (CNNs). The CNN models were evaluated on their ability to classify samples with and without embedded thermocouples across five power levels (300W, 600W, 900W, 1200W, 1500W) using thermal images with supervised labeling. Four distinct CNN classification models were created for different scenarios including without (baseline) and with thermocouples, only without thermocouples across power levels, only with thermocouples across power levels, and combined without and with thermocouples across power levels. The models achieved 98.29% accuracy on combined baseline and thermocouple images, 97.10% for baseline images across power levels, 97.43% for thermocouple images, and 97.27% for both types across power levels. The high accuracy, above 97%, demonstrates the system's effectiveness in identifying and classifying conditions within the UAM process, providing a reliable tool for quality assurance and process control in manufacturing environments.
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