| Abstract Scope |
Build quality is one of the major factors hindering the wide adoption of additive manufacturing in many industries’ productions, aerospace, and defense. In-situ monitoring is a fundamental step to achieve an overall improvement in printed parts. This review explores advanced optical monitoring and temperature measurement techniques used for in-situ monitoring laser-based additive manufacturing to understand melt pool dynamics during printing to improve build quality. High-speed imaging systems, calibrated illumination and optical filters, enable real-time visualization of melt pool dynamics. Techniques such as bandpass filtering, beam splitting, and synchronized laser illumination enhance signal clarity while protecting sensors. Deep learning approaches, including a real-time object detection algorithm “YOLO”; and convolutional neural networks, are increasingly used for automated detection of melt pool features and defects. In addition, pyrometers, ranging from single wavelength to multi-channel systems, provide non-contact temperature measurements across key thermal zones. These sensors are frequently calibrated using reference plates and thermocouples to ensure accuracy. Together, these monitoring methods support in-situ analysis and form the basis for developing closed-loop control systems. This paper provides a consolidated overview of current configurations, sensor capabilities, and trends, highlighting their critical role in the advancement of intelligent, high-performance additive manufacturing systems. |