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Longxiang Shao

Publications and source records attributed to Longxiang Shao.

2 recordsLinked to original sources

Comparison and Evaluation of Different Simulation Environments for Rigid Body Systems

Rigid body dynamics simulators are important tools for the design, analysis and optimization of mechanical systems in a variety of technical and scientific applications. This study examines four different simulation environments (Adams, Simscape, OpenModelica, and VEROSIM), focusing in particular on the comparison of the modeling methods, the numerical solvers, and the treatment of numerical problems that arise especially in closed-loop kinematics (esp. redundant boundary conditions and static equilibrium problem). A novel and complex crane boom of a real forestry machine serves as a practical benchmark application example. The direct comparison of the different solution approaches in the examined simulation tools supports the user in selecting the most suitable tool for his application.

cs.RO

Application of the learning from errors principle in tufting machines

The principle of learning from errors is pedagogically powerful but often impractical in industrial settings due to risks to safety and equipment. This paper presents an integrated training approach specifically designed for tufting machine operators. It uses hybrid digital twins, augmented reality (AR), and Petri Net-based modelling to apply the learning from errors principle effectively. Operator actions and errors are simulated via experimentable digital twins (EDTs), and the consequences of errors are visualized in AR, enabling safe, experiential learning. A Petri Net model formally represents the process, including typical faults and recovery paths, and is implemented in VEROSIM using SOML++. This hybrid framework provides a scalable foundation for AR-guided training systems that reduce risk and accelerate skill acquisition.

eess.SY