Machine learning thermal management system for additive manufacturing

Abstract

A thermal measurement system enables high-resolution sub-surface temperature monitoring during additive manufacturing processes through machine learning demodulation of chirped fiber Bragg grating (C-FBG) sensors. An optical sensing subsystem includes a C-FBG sensor that encodes spatial temperature information in wavelength for high-temperature operation. A neural network model transforms complex reflection spectra into spatial temperature profiles with micrometer-scale resolution, overcoming limitations of traditional demodulation methods. A calibration subsystem generates synchronized spectral and thermal imaging data for training the neural network using controlled thermal profiles. The system captures steep thermal gradients and rapid cooling rates during laser powder bed fusion operations. A fiber embedding technique maintains the sensor in a strain-free condition at controlled sub-surface depths. The integration of high-temperature C-FBG sensors with machine learning signal processing achieves significant improvement in spatial resolution compared to traditional fiber optic thermal measurement approaches.

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