标题： 将机器学习与多模态监测系统相结合，利用声学和视觉传感来评估激光定向能量沉积中的几何变化 显示英文标题

标题： Integrating Machine Learning with Multimodal Monitoring System Utilizing Acoustic and Vision Sensing to Evaluate Geometric Variations in Laser Directed Energy Deposition

摘要： 激光定向能量沉积（DED）增材制造由于熔池动力学和工艺变化的复杂性，难以保证零件的一致质量。虽然许多研究集中在缺陷检测上，但很少有工作验证了用于评估熔池动力学和工艺质量的工艺监控系统。本研究提出了一种新型多模态监控框架，协同整合接触式声发射（AE）传感与同轴相机视觉，以实现对DED零件几何变化的逐层识别和评估。实验研究使用了三种零件配置：无孔的基准零件、带有3毫米直径通孔的零件以及带有5毫米通孔的零件，以测试系统的区分能力。原始传感器数据进行了预处理：声学信号被过滤以提取时域和频域特征，而相机数据则进行了熔池分割和形态特征提取。评估了多种机器学习算法（包括SVM、随机森林和XGBoost），以找到分类逐层几何变化的最佳模型。集成的多模态策略实现了94.4%的优越分类性能，相比之下，仅使用AE为87.8%，仅使用相机为86.7%。验证确认集成系统有效捕捉了与几何变化相关的结构振动特征和表面形态变化。尽管本研究专注于特定几何形状，但所展示的区分特征的能力为未来在表征零件变化（如几何误差和制造引起的缺陷）方面的应用奠定了技术基础。 显示英文摘要

摘要： Laser directed energy deposition (DED) additive manufacturing struggles with consistent part quality due to complex melt pool dynamics and process variations. While much research targets defect detection, little work has validated process monitoring systems for evaluating melt pool dynamics and process quality. This study presents a novel multimodal monitoring framework, synergistically integrating contact-based acoustic emission (AE) sensing with coaxial camera vision to enable layer-wise identification and evaluation of geometric variations in DED parts. The experimental study used three part configurations: a baseline part without holes, a part with a 3mm diameter through-hole, and one with a 5mm through-hole to test the system's discerning capabilities. Raw sensor data was preprocessed: acoustic signals were filtered for time-domain and frequency-domain feature extraction, while camera data underwent melt pool segmentation and morphological feature extraction. Multiple machine learning algorithms (including SVM, random forest, and XGBoost) were evaluated to find the optimal model for classifying layer-wise geometric variations. The integrated multimodal strategy achieved a superior classification performance of 94.4%, compared to 87.8% for AE only and 86.7% for the camera only. Validation confirmed the integrated system effectively captures both structural vibration signatures and surface morphological changes tied to the geometric variations. While this study focuses on specific geometries, the demonstrated capability to discriminate between features establishes a technical foundation for future applications in characterizing part variations like geometric inaccuracies and manufacturing-induced defects.