Sub-surface thermal measurement in additive manufacturing via machine learning-enabled high-resolution fiber optic sensing

• Published in: Nature communications Vol. 15; no. 1; pp. 7568 - 12
• Main Authors: Wang, Rongxuan, Wang, Ruixuan, Dou, Chaoran, Yang, Shuo, Gnanasambandam, Raghav, Wang, Anbo, Kong, Zhenyu (James)
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 31.08.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 639/166/988; 639/301/930/1032; 639/624/1107/510; Additive manufacturing; Beds (process engineering); Bragg gratings; Continuous fibers; Cooling rate; Demodulation; Embedding; Fiber optics; Finite element method; Heating; Humanities and Social Sciences; Laser cooling; Laser damage; Learning algorithms; Machine learning; Manufacturing; Manufacturing industry; Mathematical models; Measurement techniques; Mechanical properties; Melting; Microstructure; multidisciplinary; Optical measuring instruments; Optical properties; Science; Science (multidisciplinary); Sensors; Spatial discrimination; Spatial resolution; Surface temperature; Temperature gradients; Temperature measurement; Thermal analysis; Thermal measurement; Three dimensional printing
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-024-51235-7

Microstructures of additively manufactured metal parts are crucial since they determine the mechanical properties. The evolution of the microstructures during layer-wise printing is complex due to continuous re-melting and reheating effects. The current approach to studying this phenomenon relies on time-consuming numerical models such as finite element analysis due to the lack of effective sub-surface temperature measurement techniques. Attributed to the miniature footprint, chirped-fiber Bragg grating, a unique type of fiber optical sensor, has great potential to achieve this goal. However, using the traditional demodulation methods, its spatial resolution is limited to the millimeter level. In addition, embedding it during laser additive manufacturing is challenging since the sensor is fragile. This paper implements a machine learning-assisted approach to demodulate the optical signal to thermal distribution and significantly improve spatial resolution to 28.8 µm from the original millimeter level. A sensor embedding technique is also developed to minimize damage to the sensor and part while ensuring close contact. The case study demonstrates the excellent performance of the proposed sensor in measuring sharp thermal gradients and fast cooling rates during the laser powder bed fusion. The developed sensor has a promising potential to study the fundamental physics of metal additive manufacturing processes. Measuring sub-surface thermal conditions during 3D printing is crucial for microstructure evolution understanding and control. Authors use embedded fiber optic sensors to measure sub-surface temperatures and use machine learning to improve sensor resolution to 30 µm, providing detailed data for thermal modeling and prediction.