Carnegie Melon University (Carnegie Melon)
Consultancy : Research : Academic
Carnegie Mellon University challenges the curious and passionate to deliver work that matters.
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🖨️🎛️ One-Camera Method Reveals Added Insights in Additive Manufacturing
We introduce an experimental method to image melt pool temperature with a single commercial color camera and compare the results with multi-physics computational fluid dynamic (CFD) models. This approach leverages the principle of two-color (i.e., ratiometric) thermal imaging, which is advantageous because it negates the need for a priori knowledge of melt pool emissivity, plume transmissivity, and the camera’s view factor. The color camera’s ability to accurately measure temperature was validated with a National Institute of Standards and Technology (NIST) blackbody source and tungsten filament lamp between temperatures of 1600 K and 2800 K. To demonstrate the technique, an off-axis high-speed color camera operating at 22 500 frames per second capturing a 2.8 mm by 2.8 mm area on the build plate was used to image both no-powder and powder single beads on a commercial laser powder bed fusion machine. Melt pool temperature fields for 316L stainless steel at varying processing conditions show peaks between 3300 K and 3700 K depending on the laser power and increased variability in the presence of powder. Measurements of nickel superalloy 718 and Ti-6Al-4V show comparable temperatures, with increased plume obstruction, especially in Ti-6Al-4V due to vaporization of aluminum. Multi-physics CFD models are used to simulate metal melt pools but some parameters such as the accommodation and Fresnel coefficients are not well characterized. Fitting a FLOW-3D® CFD model to ex-situ measurements of the melt pool cross-sectional geometry for 316L stainless steel identifies multiple combinations of Fresnel coefficient and accommodation coefficient that lead to geometric agreement. Only two of these combinations show agreement with the thermal images, motivating the need for thermal imaging as a means to advance validation of complex physics models. Our methodology can be applied to any color camera to better monitor and understand melt pools that yield high-quality parts.
UVA Research Team Detects Additive Manufacturing Defects in Real-Time
Introduced in the 1990s, laser powder bed fusion, or LPBF uses metal powder and lasers to 3-D print metal parts. But porosity defects remain a challenge for fatigue-sensitive applications like aircraft wings. Some porosity is associated with deep and narrow vapor depressions which are the keyholes.
“By integrating operando synchrotron x-ray imaging, near-infrared imaging, and machine learning, our approach can capture the unique thermal signature associated with keyhole pore generation with sub-millisecond temporal resolution and 100% prediction rate,” Sun said. In developing their real-time keyhole detection method, the researchers also advanced the way a state-of-the-art tool — operando synchrotron x-ray imaging — can be used. Utilizing machine learning, they additionally discovered two modes of keyhole oscillation.
auton-survival: An Open-Source Package for Regression, Counterfactual Estimation, Evaluation
Real-world decision-making often requires reasoning about when an event will occur. The overarching goal of such reasoning is to help aid decision-making for optimal triage and subsequent intervention. Such problems involving estimation of Times-to-an-Event frequently arise across multiple application areas, including, predictive maintenance. Reliability engineering and systems safety research involves the use of remaining useful life prediction models to help extend the longevity of machinery and equipment by proactive part and component replacement.
Discretizing time-to-event outcomes to predict if an event will occur is a common approach in standard machine learning. However, this neglects temporal context, which could result in models that misestimate and lead to poorer generalization.