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ABSTR ACT In situ melt pool monitoring is a set of technologies widely deployed on industrial, metals-based laser powder bed fusion (LPBF) additive manufacturing (AM) systems. This study investigates the use of a calibrated tungsten ribbon lamp as a reference standard to calibrate a photodetector based, on-axis melt pool monitoring system. Calibration demonstrates two functions: (a) enable a reference for measuring and ensuring system repeatability, and (b) enable reference to physical temperature values based on the measured photodetector signals. The second function is explored in this paper. A regression-based model is derived based on bichromatic Planck thermometry theory. The calibrated tungsten lamp is then placed within a LPBF system, and resulting photodetector signals are measured at different lamp temperature set points to calibrate the model. Finally, several additional characterization tests and their results are presented verifying the temporal response of the lamp, measurement noise as a function of sampling time, and spectroscopic measurements of the LPBF optics and their potential effect on temperature calibration. A framework is also developed to normalize temperature readings across the build plate to remove location-dependent optical artifacts. KEYWORDS: additive manufacturing, in situ process monitoring, coaxial melt pool monitoring Introduction Process Monitoring in Additive Manufacturing In situ process monitoring in additive manufacturing (AM) utilizes various sensor modalities to detect variations in the AM process, with the aim of associating these variations with defects in finished components (Everton et al. 2016 McCann et al. 2021 Grasso and Colosimo 2017 Spears and Gold 2016). Nondestructive evaluation methods are not robust to geometry. Some become less reliable when components have intricate surfaces, and others struggle as the total amount of material in a component’s cross section increases (Chauveau 2018). Ultimately, a goal of AM process monitoring is to detect component defects in arbitrary geometries. One common technique is coaxial melt pool monitoring, in which single point or image-based photodetectors are optically aligned with the laser. One or more wavelength range is selected for measurement. Process monitoring is critical in AM due to the presence of stochastic defects as well as complex but predictable process deviations. For example, if the same component is fabricated multiple times, defects may occur in similar locations each time due to complex thermal behavior. In the case of stochas- tic defects, it is entirely impossible to predict a priori where defects will form with reasonable reliability if the same com- ponent is manufactured multiple times, defects may occur in entirely different locations with different frequencies (Snow et al. 2020). In the case of predictable process deviations, these may be modeled a priori, but process monitoring is desired to confirm that the process occurred as expected. In the case of stochastic defects, process monitoring or post-process inspec- tion is essential to validating the components. Due to the complexity of the relationship between process monitoring data and final part quality, researchers have turned to machine learning and deep learning for component certifi- cation. These methods are able to produce rich representations of process monitoring data (Meng et al. 2020). However, these models, as interpolators, are not necessarily generalizable. It is critical that process monitoring signals are consistent across machines and across the build plate of a given machine for developed machine learning models to function. For example, a convolutional neural network (CNN)—commonly used to process AM process data—is comprised of shift-equivariant layers under the assumption that the identity of process signals THERMAL CALIBRATION OF RATIOMETRIC, ON-AXIS MELT POOL MONITORING PHOTODETECTOR SYSTEM USING TUNGSTEN STRIP LAMP BY BRETT DIEHL*†, ALBERTO CASTRO*, LARS JAQUEMETTON*, AND DARREN BECKETT* * Sigma Labs Inc., Santa Fe, New Mexico, USA † brettd@sigmalabsinc.com Materials Evaluation 80 (4): 64–73 https://doi.org/10.32548/2022.me-04271 ©2022 American Society for Nondestructive Testing ME | TECHPAPER 64 M A T E R I A L S E V A L U A T I O N • A P R I L 2 0 2 2