Accuracy measurement is an essential front step to error compensation of industrial robots. Different measurement methods have been tried all the way over the past years. In this paper, we propose a novel method to measure the positioning distance accuracy based on the multi-station technology of the laser tracer device. Compared with the laser tracker device, this method is more precise in measuring the coordinate values of the robot end-effector. Besides, the laser tracer is more portable than the laser tracker. At the last part, a practical example is presented in which we have carried out in an domestic industrial robot.
This paper proposes an energy analysis method of the laser tracing measurement optical system. Based on the principle of the laser tracing measurement optical system, an energy model is established to analyze the effects of non-ideal optical elements on the energy of the optical system. The simulation results show that the interference pattern is the most obvious when the split ratios of the beam splitters in the interference part and the tracing part are respectively 6:4 and 7:3. Under the above split ratios, the interference signal energy values of four receivers are close to each other and the visibility of fringe pattern reaches 0.99. The visibility of fringe patterns of four interference signals is reduced when the reflectivity of all polarization beam splitters is under non-ideal conditions in an entire optical system. The non-ideality of the transmittance of the polarization beam splitters does not affect the visibility of fringe patterns. The paper provides the theoretical basis for the accuracy improvement, reliability evaluation, optical system design and the selection of optical elements of laser tracing measurement systems.
A new approach was proposed by combing Ensemble Empirical Mode Decomposition (EEMD) algorithm and Back Propagation (BP) neural network for detection of gear through transmission noise analysis. Then feature values of the feature signals are calculated. The feature values which have a great difference for different defect types are chosen to build an eigenvector. BP neural network is used to train and learn on the eigenvector for recognition of gear defects intelligently. In this study, a comparative experiment has been performed among normal gears, cracked gears and eccentric gears with fifteen sets of different gears. Experimental results indicate that the proposed method can detect gear defect features carried by the transmission noise effectively.
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