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

ABSTRACT: 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

SUBMITTER: Wang R 

PROVIDER: S-EPMC11365934 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature