Dynamic modeling and vibration analysis for defect identification of single-stage gearboxes in the joints of industrial robots with six DOF

Author:

Frej Anis12ORCID,Chiementin Xavier1,Fakher Chaari2,Bolaers Fabrice1,Haddar Mohamed2

Affiliation:

1. Reims Champagne-Ardenne University, Institute of Thermics, Mechanics and Material (ITHEMM), Reims, France

2. National Engineering School of Sfax, Laboratory of Mechanics, Modeling and Production (LA2MP), Sfax, Tunisia

Abstract

In the age of the future industry, many industrial activities have been entrusted to robots due to their ability to improve both accuracy and production. However, during the manufacturing process, the occurrence of defects in the manipulators transmissions can lead to a significant degradation of the product quality, and consequently financial losses. Hence, this paper aims to present a novel methodology to elaborate a robot dynamic model allowing the detection of the defects arising from the transmissions and more precisely the gears faults. First, a geometric model of the robot was developed based on the Modified Denavit-Hartenberg (MDH) approach, to derive the transfer matrices required to model the different components of the robot. Secondly, thanks to these matrices, to the Euler-Lagrange convention and taking into account the assumption of the joint flexibility, the deduction of the global equation of motion of the robot has been performed. The ability of the developed model to detect defects will then be investigated by a series of numerical simulations performed in the case of healthy gears and in the case where a tooth-cracking fault has been introduced, and also under various velocities. Finally, an experimental study and vibration analysis were conducted to validate the results of the numerical simulations and consequently the effectiveness of the proposed model.

Publisher

SAGE Publications

Subject

Mechanical Engineering

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Robot arm damage detection using vibration data and deep learning;Neural Computing and Applications;2023-11-15

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