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

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...