Due to the coaxial connection of engine, motor and pump, the dynamic characteristics of hybrid construction machinery are changed, which generates a new torsional vibration problem of multi-power sources. To reduce th...Due to the coaxial connection of engine, motor and pump, the dynamic characteristics of hybrid construction machinery are changed, which generates a new torsional vibration problem of multi-power sources. To reduce the torsional vibration of the hybrid construction machinery complex shafting, torsional vibration active control was proposed. The three-mass model of coaxial shafting of hybrid construction machinery was established. The PID control and the fuzzy sliding mode control were chosen to weaken torsional vibration by controlling the motor speed and torque. The simulation results show that the fuzzy sliding mode control has 12% overshoot of the PID control when the engine torque changes. The active control is effective and can realize smooth power switch.展开更多
The performance of cutting machines in terms of energy consumption and vibration directly affects the production costs. In this work, our aim was to evaluate the performance of cutting machines using hybrid intelligen...The performance of cutting machines in terms of energy consumption and vibration directly affects the production costs. In this work, our aim was to evaluate the performance of cutting machines using hybrid intelligent models. For this purpose, a systematic experimental work was performed. A database of the carbonate and granite rocks was established, in which the physical and mechanical properties of these rocks (i.e., UCS, elastic modulus, Mohs hardness, and Schmiazek abrasivity factor) and the operational parameters (i.e., depth of cut and feed rate) were considered as the input parameters. The predictive models were developed incorporating a combination of the multi-layered perceptron artificial neural networks and genetic algorithm (GANN-BP) and the support vector regression method and Cuckoo optimization algorithm (COA-SVR). The results obtained indicated that the performance of the developed GANN-BP and COA-SVR models was close to each other and that these models had good agreements with the measured values. These results also showed that these proposed models were suitable tools in evaluating the performance of cutting machines.展开更多
基金Project(51205415)supported by the National Natural Science Foundation of ChinaProject(14JJ3020)supported by the Natural Science Foundation of Hunan Province,China+2 种基金Project(2013M542129)supported by China Postdoctoral Science FoundationProject(2012QNZT014)supported by the Fundamental Research Funds for the Central Universities,ChinaProject supported by the Postdoctoral Foundation of Central South University,China
文摘Due to the coaxial connection of engine, motor and pump, the dynamic characteristics of hybrid construction machinery are changed, which generates a new torsional vibration problem of multi-power sources. To reduce the torsional vibration of the hybrid construction machinery complex shafting, torsional vibration active control was proposed. The three-mass model of coaxial shafting of hybrid construction machinery was established. The PID control and the fuzzy sliding mode control were chosen to weaken torsional vibration by controlling the motor speed and torque. The simulation results show that the fuzzy sliding mode control has 12% overshoot of the PID control when the engine torque changes. The active control is effective and can realize smooth power switch.
基金Project(11039)supported by Shahrood University of Technology,Iran
文摘The performance of cutting machines in terms of energy consumption and vibration directly affects the production costs. In this work, our aim was to evaluate the performance of cutting machines using hybrid intelligent models. For this purpose, a systematic experimental work was performed. A database of the carbonate and granite rocks was established, in which the physical and mechanical properties of these rocks (i.e., UCS, elastic modulus, Mohs hardness, and Schmiazek abrasivity factor) and the operational parameters (i.e., depth of cut and feed rate) were considered as the input parameters. The predictive models were developed incorporating a combination of the multi-layered perceptron artificial neural networks and genetic algorithm (GANN-BP) and the support vector regression method and Cuckoo optimization algorithm (COA-SVR). The results obtained indicated that the performance of the developed GANN-BP and COA-SVR models was close to each other and that these models had good agreements with the measured values. These results also showed that these proposed models were suitable tools in evaluating the performance of cutting machines.