New Indices for Evaluating Vibration Characteristics of Flexible-Joint Robots

Zhang, Ping; Li, Yuwen

doi:10.3390/app10144895

Cited by 3 publications

(2 citation statements)

References 37 publications

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“…Because of torsion, the speed of the motor does not directly transfer to the speed of the load machine [3]. Rolling mills [4], wind turbines [5], robotic manipulators [6][7][8][9], exoskeletons [10,11], electric cars [12], and countless similar mechanisms are all affected by this phenomenon, making it a crucial scientific problem to be solved.…”

Section: Introductionmentioning

confidence: 99%

Virtual Signal Calculation Using Radial Neural Model Applied in a State Controller of a Two-Mass System

2023

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Neural network approaches have commonly been used to solve complex mathematical equations in the literature. They have inspired the modifications of state controllers and are often implemented for electrical drives with an elastic connection. Given that the addition of a virtual signal can provide adaptive properties to classical controllers and that selected feedback signals can also be replaced with a virtual state variable from a neural network, several combinations can be considered and compared. In this paper, Radial Basis Function neural-network-based control algorithms are proposed in which online updating of the output weights is used. Analyses of simulation experiment results reveal that the proposed control algorithms significantly improve the operation of classic-state feedback controllers applied to two-mass systems in the presence of parameter uncertainty.

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Section: Introductionmentioning

confidence: 99%

Virtual Signal Calculation Using Radial Neural Model Applied in a State Controller of a Two-Mass System

2023

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“…Some authors have researched into this phenomenon [28][29][30] and have provided tools that help to address this issue from a workshop-oriented perspective. A correct selection of robots, cutting tools and optimum machining conditions is decisive in order to produce parts with suitable dimensional tolerances and surface quality in robotic machining systems.…”

Section: Introductionmentioning

confidence: 99%

A New Approach to the Consideration and Analysis of Critical Factors in Robotic Machining

et al. 2020

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The relative low stiffness of industrial robots is a major limitation on the development of flexible and reconfigurable systems in applications in which process forces and vibration lead into significant tool path deviations with respect to the programmed path as in the case of robotic machining. This paper presents a novel factorial procedure that allows for the preliminary study of the main conditions in robotic machining operations and it determines the critical factors that are affecting the machining path of any robotic cell in order to obtain the process conditions with lower path deviations. In this procedure the most influential robotic machining constraints were identified and classified, the factorial design of experiments was used to enable the execution of the experimental tests and the machining tool path deviation predictive methodology (PREMET) was used to determine the cutting tool path deviation between the programmed and the experimental path as a function of the process variables. Experimental trials have been carried out in order to determine the main factors that affect the robotic machining and influence the main constraints of the process, showing a reduction greater than a 36% of the cutting tool path deviation in groove milling of aluminum. The critical factors identified in order of importance are: hardness of the material, location of the workpiece, orientation of milling head relative to working direction and cutting conditions. This procedure can be extended to future factorial studies to improve the precision of robotic machining (in operations such as face milling, contouring, pocketing) and to establish design criteria for machining robotic cells.

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