The service cycle and dynamic performance of structural parts are afected by the weld grinding accuracy and surface consistency. Because of reasons such as assembly errors and thermal deformation, the actual track of ...The service cycle and dynamic performance of structural parts are afected by the weld grinding accuracy and surface consistency. Because of reasons such as assembly errors and thermal deformation, the actual track of the robot does not coincide with the theoretical track when the weld is ground ofine, resulting in poor workpiece surface quality. Considering these problems, in this study, a vision sensing-based online correction system for robotic weld grinding was developed. The system mainly included three subsystems: weld feature extraction, grinding, and robot real-time control. The grinding equipment was frst set as a substation for the robot using the WorkVisual software. The input/output (I/O) ports for communication between the robot and the grinding equipment were confgured via the I/O mapping function to enable the robot to control the grinding equipment (start, stop, and speed control). Subsequently, the Ethernet KRL software package was used to write the data interaction structure to realize realtime communication between the robot and the laser vision system. To correct the measurement error caused by the bending deformation of the workpiece, we established a surface profle model of the base material in the weld area using a polynomial ftting algorithm to compensate for the measurement data. The corrected extracted weld width and height errors were reduced by 2.01% and 9.3%, respectively. Online weld seam extraction and correction experiments verifed the efectiveness of the system’s correction function, and the system could control the grinding trajectory error within 0.2 mm. The reliability of the system was verifed through actual weld grinding experiments. The roughness, Ra, could reach 0.504 µm and the average residual height was within 0.21 mm. In this study, we developed a vision sensing-based online correction system for robotic weld grinding with a good correction efect and high robustness.展开更多
A certain amount of ammonia reducer were directly injected into the 4102BZLQ Diesel engine's combustion chamber when the combustion temperature decreases to 1573-1073K, NOx generated could be reduced to 1.11g/(kW&...A certain amount of ammonia reducer were directly injected into the 4102BZLQ Diesel engine's combustion chamber when the combustion temperature decreases to 1573-1073K, NOx generated could be reduced to 1.11g/(kW·h). Based on PRF combustion mechanism, NO was tested by using the heavy-duty diesel engine test cycle of ESC thirteen conditions[1], the ammonia spray angle and amount were tested and optimized in different conditions. The test results show that the thermal efficiency of Diesel engine does not decrease while NO exhaust decreases.展开更多
基金Supported by Hunan Provincial Natural Science Foundation of China(Grant No.2021JJ50116).
文摘The service cycle and dynamic performance of structural parts are afected by the weld grinding accuracy and surface consistency. Because of reasons such as assembly errors and thermal deformation, the actual track of the robot does not coincide with the theoretical track when the weld is ground ofine, resulting in poor workpiece surface quality. Considering these problems, in this study, a vision sensing-based online correction system for robotic weld grinding was developed. The system mainly included three subsystems: weld feature extraction, grinding, and robot real-time control. The grinding equipment was frst set as a substation for the robot using the WorkVisual software. The input/output (I/O) ports for communication between the robot and the grinding equipment were confgured via the I/O mapping function to enable the robot to control the grinding equipment (start, stop, and speed control). Subsequently, the Ethernet KRL software package was used to write the data interaction structure to realize realtime communication between the robot and the laser vision system. To correct the measurement error caused by the bending deformation of the workpiece, we established a surface profle model of the base material in the weld area using a polynomial ftting algorithm to compensate for the measurement data. The corrected extracted weld width and height errors were reduced by 2.01% and 9.3%, respectively. Online weld seam extraction and correction experiments verifed the efectiveness of the system’s correction function, and the system could control the grinding trajectory error within 0.2 mm. The reliability of the system was verifed through actual weld grinding experiments. The roughness, Ra, could reach 0.504 µm and the average residual height was within 0.21 mm. In this study, we developed a vision sensing-based online correction system for robotic weld grinding with a good correction efect and high robustness.
基金Sponsored by the Hunan Science and Technology Agency Science Research Program 09(2009GK3091)the Hunan Provincial Education Department Science Research Program 09(09C1138)
文摘A certain amount of ammonia reducer were directly injected into the 4102BZLQ Diesel engine's combustion chamber when the combustion temperature decreases to 1573-1073K, NOx generated could be reduced to 1.11g/(kW·h). Based on PRF combustion mechanism, NO was tested by using the heavy-duty diesel engine test cycle of ESC thirteen conditions[1], the ammonia spray angle and amount were tested and optimized in different conditions. The test results show that the thermal efficiency of Diesel engine does not decrease while NO exhaust decreases.
