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Solid Modeling News Today : Breaking News, Live Updates & Top Stories | Vimarsana

JCC adds courses combining in-person, online learning

JAMESTOWN, N.Y. — SUNY Jamestown Community College (JCC) enhanced course offerings starting this spring semester by adding seven HyFlex courses to its catalog.

Design and Control of a Novel Variable Stiffness Series Elastic Actuat by Emre Sariyildiz, Rahim Mutlu et al

This article expounds the design and control of a new variable stiffness series elastic actuator (VSSEA). It is established by employing a modular mechanical design approach that allows us to effectively optimize the stiffness modulation characteristics and power density of the actuator. The proposed VSSEA possesses the following features: no limitation in the work range of output link; a wide range of stiffness modulation (∼20 N·m/rad to ∼1 KN·m/rad); low-energy-cost stiffness modulation at equilibrium and nonequilibrium positions; compact design and high torque density (∼36 N·m/kg); and high-speed stiffness modulation (∼3000 N·m/rad/s). Such features can help boost the safety and performance of many advanced robotic systems, e.g., a cobot that physically interacts with unstructured environments and an exoskeleton that provides physical assistance to human users. These features can also enable us to utilize variable stiffness property to attain various regulation and traje

A Non-autoregressive Dynamic Model based Welding Parameter Planning Me by Zhihu Li, Zhimin Hou et al

Wire and arc additive manufacturing(WAAM) is a promising method for directly manufacturing parts with complex shapes. However, the accuracy of the existing welding parameter planning methods would dramatically decrease when bead geometry changes dynamically due to the long-term dependence, strong coupling, and hysteresis properties of the WAAM process. To this end, a non-autoregressive(NAR) dynamic model is proposed to predict the bead geometry, and an adaptive model predictive control(aMPC) method is proposed to plan welding parameters to achieve high manufacturing accuracy. First, in the proposed dynamic model, the long-term dynamic characteristics of the WAAM process are modeled by a resample long short-term memory(Re-LSTM) network by considering the fluidity of the welding pool, which is the crucial factor of dynamic characteristics of the welding process. Second, in the proposed aMPC method, the strong coupling is addressed by high multi-objective performance, and the hysteresis i

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