Binocular Model: A deep learning solution for online melt pool temperature analysis using dual-wavelength Imaging Pyrometry

TL;DR

This paper introduces the Binocular deep learning model for real-time, high-accuracy melt pool temperature analysis in metal additive manufacturing, significantly improving speed and automation over traditional methods.

Contribution

The study presents a novel dual-input deep learning model that converts raw dual-wavelength data into temperature maps at 750 fps, enabling real-time monitoring in metal AM.

Findings

Achieves 0.95 R-squared accuracy in temperature estimation.

Processes up to 750 frames per second, 1000 times faster than traditional methods.

Enhances real-time monitoring capabilities in laser powder bed fusion.

Abstract

In metal Additive Manufacturing (AM), monitoring the temperature of the Melt Pool (MP) is crucial for ensuring part quality, process stability, defect prevention, and overall process optimization. Traditional methods, are slow to converge and require extensive manual effort to translate data into actionable insights, rendering them impractical for real-time monitoring and control. To address this challenge, we propose an Artificial Intelligence (AI)-based solution aimed at reducing manual data processing reliance and improving the efficiency of transitioning from data to insight. In our study, we utilize a dataset comprising dual-wavelength real-time process monitoring data and corresponding temperature maps. We introduce a deep learning model called the "Binocular model," which exploits dual input observations to perform a precise analysis of MP temperature in Laser Powder Bed Fusion (L-PBF). Through advanced deep learning techniques, we seamlessly convert raw data into temperature maps, significantly streamlining the process and enabling batch processing at a rate of up to 750 frames per second, approximately 1000 times faster than conventional methods. Our Binocular model achieves high accuracy in temperature estimation, evidenced by a 0.95 R-squared score, while simultaneously enhancing processing efficiency by a factor of $\sim1000x$ times. This model directly addresses the challenge of real-time MP temperature monitoring and offers insights into the encountered constraints and the benefits of our Deep Learning-based approach. By combining efficiency and precision, our work contributes to the advancement of temperature monitoring in L-PBF, thus driving progress in the field of metal AM.