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To meet the test requirements of the key structural part in the super high-rise building, a 108MN 6-dof advanced structural testing system is developed. And for the high-speed shear test of the large rubber bearing, the instantaneous flow demand of the testing system is far more than the conventional hydraulic testing equipment. For this, the piston accumulator group with the separated nitrogen cylinder is adopted as the system auxiliary hydraulic power. In the paper, the optimization design of the accumulator group is implemented though continuously optimizing the volume ratio of the accumulator and the nitrogen cylinder. And when the accumulator group works, its pressure decreases from the precharge pressure to the working pressure constantly, which will impact the loading accuracy, and the excessive pressure is also not conducive to the protection for the specimen. To restrain the pressure fluctuation, a pressure control loop with the large-flow cartridge valve is presented, the associated model is set up and the control method is developed. The results show that with PD control the oil supply pressure of the accumulator group can be stabilized within the required range, and the control precision is high.

Ni–TiC composite coatings were prepared on substrate of aluminum alloy by pulse electrodeposition. The plating parameters for optimizing wear and corrosion resistance of Ni–TiC composite coatings were selected by orthogonal test, including the TiC particles concentration, current density, duty cycle, frequency and stirring rate. A three-layer back propagation (BP) artificial neural network with Lavenberg–Marquardt algorithm was established by MATLAB, which was used to train the network and predict orthogonal experimental data. Additionally, the best combination of parameters of the composite coatings were predicted and verified by experiments. The wear and corrosion resistance of Ni–TiC coatings were characterized and analyzed. The results indicated that the BP mode, which shows a relative error of approximately 3 %, could effectively predict the wear and corrosion resistance of Ni–TiC composite coatings. Ni–TiC composite coatings deposited by optimal synthetic condition showed dispersed TiC particles and continuous Ni matrix, as well as excellent wear and corrosion resistance.

The plating parameters for optimizing the wear and corrosion resistance of Ni-TiC composite coatings were selected by orthogonal test, mainly including the TiC particles concentration, current density, duty cycle, frequency and stirring rate. A three-layer BP (Back Propagation) neural network with Lavenberg-Marquardt algorithm was established by MATLAB, which was used to train the network and predicted orthogonal experimental data. In addition, the best parameters combination of the composite coating were predicted and verified by experiments. The results predicted through the proposed BP model are in good agreement with the experimental values, the relative error is small, and the maximum error is less than 3% and the coefficient of determination value is 0.9997.

The model based on Internal Force Space to reduce the internal force of super-redundant actuated parallel mechanism is put forward in this paper. The model is proposed to restrain the internal force in the redundant actuated parallel mechanism especially in super-redundant actuated parallel mechanism(SRPM) with long stroke, such as the Million Tons Tester's (MTT) horizontal redundant driving mechanism. It study a method to work out the internal force of redundant actuated parallel mechanism by using the result of the actuators' force vector and the Internal Force Space basis. Using the internal force as the feedback to regulate the servo values' control signals is proposed in this paper to reduce the redundant actuated parallel mechanism's internal force. Compared to traditional pressure stabilizing controller the model based on Internal Force Space has a better effect in reducing the internal force. At last, the joint simulation with MATLAB and ADAMS is built to test the model.

Physicochemical Problems of Mineral Processing; ISSN 2083-3989