The concomitant vibration and deformation produced by propeller blades in single-sided machining seriously affect the surface machining precision. Double-sided symmetrical machining can improve system rigidity through mutual shoring on both sides which abates the concomitant vibration and deformation. However, the actual double-sided symmetrical machining cannot be applied to blade machining due to its shape complexity. The double-sided collaborative machining method combining symmetrical machining and staggered machining is devised in this paper, and its tool path planning algorithm is investigated. Firstly, the algorithm achieves smooth fitting and correspondence of bilateral cutter position points through double-curve interpolation and position data alignment. Secondly, the blade surface is divided into four regions by two partition parameters: tip region, internal region, variable region, and edge region. Then, the conversion between symmetrical machining and staggered machining is completed through the Sigmoid deformation curve in the variable region. Finally, the feasibility and superiority of double-sided collaborative machining are verified through machining experiments.

In this paper, a kinematic separation calibration method of 6R series manipulator is proposed, and its absolute accuracy is improved by a binocular camera and standard sphere. First, a geometric error mapping model for the robotic arm was established, and the error parameters were divided into position parameters and attitude parameters for calibration purposes. Second, in the process of solving error parameters using numerical algorithms, it is easy to encounter matrix ill-conditioned problems. The spectral correction iteration method is introduced to improve the calculation accuracy. Third, three standard balls are installed at the end of the robotic arm as markers, and the center coordinates are measured using a binocular camera to obtain the actual end pose parameters. To verify the effectiveness of the proposed method, a simulation model verification was designed, and the results showed that the separation calibration method was the best. Finally, the IRB-1200 robot was successfully calibrated using the proposed method; the average robot position and angle error after calibration was significantly decreased. The position accuracy was improved by 66.9%, and the attitude accuracy was improved by 86.2%.

To improve the motion accuracy of an XYZ-3RPS hybrid kinematic machine (HKM), a geometric error calibration method via binocular vision measurement is studied. First, to separately calibrate the series kinematic mechanisms (SKMs) and parallel kinematic mechanisms (PKMs), the geometric error identification equations (GEIEs) of the XYZ SKM and 3RPS PKM are derived, respectively. By analyzing the different influence principles of the geometric errors on the position and attitude of the 3RPS PKM, a constraint function is added to the GEIE of the PKM to improve the calculation accuracy. Moreover, the geometric error compensation strategy is based on the structural characteristics of the XYZ-3RPS HKM. In addition, based on the principle of binocular vision measurement, two calibration plates, called dynamic and static calibration plates, are designed as markers to define the coordinate systems, enabling the acquisition of full positions and attitudes. Furthermore, a marker transformation method and an in-situ adjustment method are designed to determine the positions and attitudes of the HKM required for calibration such that the marker is always at the center of the field of view of the camera to improve measurement accuracy. Finally, the effectiveness of the calibration method is verified through prototype experiments.

Screw assemblies of radar are characterized by numerous densely distributed small screws of various types, rendering their manual assembly difficult and inefficient. To improve the assembly quality and efficiency, an automatic assembly system was developed. First, the position and attitude relationships among the workpiece, camera, and base coordinate systems of the manipulator were obtained using three-point location and nine-point calibration methods. Second, a visual servoing strategy is designed for guiding the manipulator such that its position can be adjusted adaptively to achieve the high-precision positioning of threaded holes. Third, a method for evaluating assembly quality is developed. The templates of angle–torque curves of various screws were produced by fitting experimental data. Furthermore, the assembly quality was determined by calculating the similarity between the angle–torque curves and templates based on the Fréchet distance. Finally, the feasibility was verified using a flat plate component with five threaded holes.

Reducing carbon emissions and improving revenue in the face of global warming and economic challenges is a growing concern for airlines. This paper addresses the inefficiencies and high costs associated with current aero-engine on-wing washing strategies. To tackle this issue, we propose a reinforcement learning framework consisting of a Similar Sequence Method and a Taylor DQN model. The Similar Sequence Method, comprising a sample library, DTW algorithm, and boundary adjustment, predicts washed aero-engine data for the Taylor DQN model. Leveraging the proposed Taylor neural networks, our model outputs Q-values to make informed washing decisions using data from the Similar Sequence Method. Through simulations, we demonstrate the effectiveness of our approach.