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Luna, Ryan

Publications and source records attributed to Luna, Ryan.

The effect of current and lambda on white-etch-crack failures

White etching cracks (WECs) have been associated with premature failure of wind turbine roller bearings. Various drivers for the generation of WECs have been identified such as loading conditions, slip, steel quality, lubrication, hydrogen embrittlement, corrosion fatigue cracking, and stray electrical currents passing through the surface. Here, in this work, a benchtop test rig utilizing a three-ring-on-roller test configuration was used to investigate the effect of electrical current and operation in different lubricating regimes, defined by lambda (λ), on high-quality bearing steel samples tested in a commercially available power transmission EP gear lubricant. It was observed that there is an inverse correlation between the magnitude of electric current applied to the ring/ roller system and time-to-failure. Higher current magnitudes lead to shorter time-to-failure than lower current magnitudes, with macropitting as the main failure mode. Sub-surface investigation revealed the presence of WECs in all cases. For the same current magnitude, tests conducted in boundary and mixed lubrication regimes showed that time-to-failure increased as lambda increased, and the tests resulted in WEC related macropits, whereas tests conducted in near-hydrodynamic regime resulted in surface damage with no macropit. It was also noted that a shift toward near-hydrodynamic lubrication resulted in a distinct surface distress on the roller surface. Furthermore, there seems to be a transition in the mixed regime during which the surface distress occurred. The damage on the surface of the test samples resembled non-spatially, periodic, groove-like corrugations and, in some cases, crater-like depressions. Sub-surface imaging, performed by sequential sectioning, revealed the presence of WECs in all cases, and broad, branching cracks that were more prevalent under the more severe boundary conditions.

17 WIND ENERGY↗

Towards Autonomous Operations of the Robonaut 2 Humanoid Robotic Testbed

The Robonaut project has been conducting research in robotics technology on board the International Space Station (ISS) since 2012. Recently, the original upper body humanoid robot was upgraded by the addition of two climbing manipulators ("legs"), more capable processors, and new sensors, as shown in Figure 1. While Robonaut 2 (R2) has been working through checkout exercises on orbit following the upgrade, technology development on the ground has continued to advance. Through the Active Reduced Gravity Offload System (ARGOS), the Robonaut team has been able to develop technologies that will enable full operation of the robotic testbed on orbit using similar robots located at the Johnson Space Center. Once these technologies have been vetted in this way, they will be implemented and tested on the R2 unit on board the ISS. The goal of this work is to create a fully-featured robotics research platform on board the ISS to increase the technology readiness level of technologies that will aid in future exploration missions. Technology development has thus far followed two main paths, autonomous climbing and efficient tool manipulation. Central to both technologies has been the incorporation of a human robotic interaction paradigm that involves the visualization of sensory and pre-planned command data with models of the robot and its environment. Figure 2 shows screenshots of these interactive tools, built in rviz, that are used to develop and implement these technologies on R2. Robonaut 2 is designed to move along the handrails and seat track around the US lab inside the ISS. This is difficult for many reasons, namely the environment is cluttered and constrained, the robot has many degrees of freedom (DOF) it can utilize for climbing, and remote commanding for precision tasks such as grasping handrails is time-consuming and difficult. Because of this, it is important to develop the technologies needed to allow the robot to reach operator-specified positions as autonomously as possible. The most important progress in this area has been the work towards efficient path planning for high DOF, highly constrained systems. Other advances include machine vision algorithms for localizing and automatically docking with handrails, the ability of the operator to place obstacles in the robot's virtual environment, autonomous obstacle avoidance techniques, and constraint management.

Badger, Julia↗