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引用次数: 0
摘要
本评论全面分析了在快速成型制造(AM)工艺中至关重要的各种状态监测技术。AM 组件的可靠性和质量取决于对众多参数的精确控制以及对潜在缺陷(如层压、裂缝和孔隙)的及时发现。本文强调原位监测系统(光学、热学和声学)的重要性,这些系统可持续评估制造过程的完整性。采用高速相机和激光扫描仪的光学技术可对 AM 工艺进行实时、非接触式评估,有助于及早发现层错位和表面异常。同时,红外感应等热成像技术在监测复杂的热梯度方面发挥着重要作用,有助于缺陷检测和过程控制。声学监测方法通过音频分析和机器学习的进步得到了加强,为在多变的操作条件下辨别调幅机械的声学特征提供了经济有效的解决方案。最后,机器学习被认为是一种高效的数据处理技术,在特征提取方面大有可为。
Condition Monitoring in Additive Manufacturing: A Critical Review of Different Approaches
This critical review provides a comprehensive analysis of various condition monitoring techniques pivotal in additive manufacturing (AM) processes. The reliability and quality of AM components are contingent upon the precise control of numerous parameters and the timely detection of potential defects, such as lamination, cracks, and porosity. This paper emphasizes the significance of in situ monitoring systems—optical, thermal, and acoustic—which continuously evaluate the integrity of the manufacturing process. Optical techniques employing high-speed cameras and laser scanners provide real-time, non-contact assessments of the AM process, facilitating the early detection of layer misalignment and surface anomalies. Simultaneously, thermal imaging techniques, such as infrared sensing, play a crucial role in monitoring complex thermal gradients, contributing to defect detection and process control. Acoustic monitoring methods augmented by advancements in audio analysis and machine learning offer cost-effective solutions for discerning the acoustic signatures of AM machinery amidst variable operational conditions. Finally, machine learning is considered an efficient technique for data processing and has shown great promise in feature extraction.