Industrial robots have great advantages in the processing of large and complex components in the aerospace field, but the lack of robot joint stiffness results in poor processing accuracy. This paper first analyzes the stiffness of the robot's joints and establishes a joint error model; Secondly, the kinematics modeling of the KUKA KR 600 robot was carried out by using the Modified D-H method, the established model was calibrated by MATLAB, and the Jacobian matrix J was calculated; Thirdly, the stiffness of the robot joints was identified through experiments; Finally, the joint error model validation was carried out. Results showed that the relative errors between the predicted and actual measured values in the x, y and z directions are 21.39%, 17.01% and 14.46% respectively. It is proved that the established joint error model shows large potential in predicting the deformation of the robot end.
During the cutting process, it is necessary to monitor the wear of the tool and change the tool in time to improve the processing quality and save costs. This paper proposes a set of real-time intelligent monitoring system with accuracy, real-time, synchronization, integration and scalability, and introduces environment-related sensors. In order to adapt to the actual processing process, the performance of the monitoring system is evaluated. A performance evaluation experimental hardware installation model is designed, and a performance evaluation method is provided for the collected multi-source heterogeneous signal data with large frequency differences and complex waveforms, which has high practical value.
Conventional cutting fluid is commonly used in the cutting process of iron-based superalloy. However, cutting fluid is easy to cause environmental pollution and cutting costs increasement. In this paper, turning experiments on superalloy GH2132 were carried out with three different coolants, namely, cutting fluid, liquid nitrogen (LN2), and liquid carbon dioxide (LCO2). The cutting force, specific cutting energy, surface roughness and cutting ambient air particulate concentration were analysed. The results show that, compared with cutting fluid, LN2 is more effective in reducing the cutting force. The specific cutting energy under LN2 is reduced by 25.3% at v = 40 m/min, ap = 0.3 mm and f = 0.1 mm/r. When the feed rate is 0.2 mm/r, the surface roughness under LCO2 can be reduced by 42.4% than the cutting fluid. In addition, the use of LN2 and LCO2 can reduce the concentration of PM2.5 by 64.1% and 81.7%, respectively.
Morphological evolution of ripple on nickel surface induced by temporally shaped femtosecond laser irradiation were studied and compared with titanium. It was revealed that the transformation of single-pulse irradiation into double-pulse irradiation can exert very different influence on ripple morphology evolution for different metals. For nickel, the double-pulse irradiation resulted in the growth in rippled area, ripple period and ripple contrast, compared with the single-pulse irradiation, while double-pulse processing of titanium leads to reductions in in rippled area and ripple period. The contrasted influence of Te on electron-phonon coupling factor (G) for the two metals was the primary factor for the different behaviors of ripple morphology evolution with temporal pulse shaping.
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