Carnegie Melon University (Carnegie Melon)
Canvas Category Consultancy : Research : Academic
Carnegie Mellon University challenges the curious and passionate to deliver work that matters.
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CMU Robotics Institute develops system to detect and fix problems in gas pipelines
Researchers in Carnegie Mellon University’s Robotics Institute are developing a modular robot that can creep inside natural gas pipelines to map where pipes are, detect decrepit or leaking pipes, and, when necessary, repair the pipe by applying a resin coating along its inner wall.
Natural gas in the US arrives at 75 million homes and more than five million commercial customers through a network of 1.2 million miles of distribution main lines and 900,000 miles of service lines, according to the DOE. It costs up to $10 million per mile to excavate and repair these existing lines. The REPAIR program aims to use robots and smart coatings to build new pipes within leaky ones. This process – leaving the pipes in place and repairing them from the inside out – could drastically cut costs by DOE estimates.
The researchers have evaluated their system using a testbed built by Peoples Gas. The robotic system now has a 200-foot range, Li said, but the eventual goal is two kilometers (around 6,500 feet). Lu said the current version of the robot is designed for 12-inch diameter pipes and a version for 6-inch pipes is in development.
Creative Robot Tool Use with Large Language Models
We introduce RoboTool, enabling robots to use tools creatively with large language models, which solves long-horizon hybrid discrete-continuous planning problems with the environment- and embodiment-related constraints.
In this work, we are interested in solving language-instructed long-horizon robotics tasks with implicitly activated physical constraints. By providing LLMs with adequate numerical semantic information in natural language, we observe that LLMs can identify the activated constraints induced by the spatial layout of objects in the scene and the robot’s embodiment limits, suggesting that LLMs may maintain knowledge and reasoning capability about the 3D physical world. Furthermore, our comprehensive tests reveal that LLMs are not only adept at employing tools to transform otherwise unfeasible tasks into feasible ones but also display creativity in using tools beyond their conventional functions, based on their material, shape, and geometric features.
🖨️🎛️ 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.