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.

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%.

In semiconductor manufacturing, ion mill etching (IME) is an emerging technique that uses ion-beam sources to remove materials to a certain depth, which requires a high degree of stability and precision. Accurate prediction of the remaining useful life (RUL), which provides a significant tool for fault diagnosis, is essential for maintenance scheduling and health management in IME process. However, RUL prediction is a challenging task, as the distribution of effective information is uneven and the degradation trend is unclear in the process. Recently, data-driven methods are widely applied in RUL prediction, due to their ability in extracting key features from the condition monitoring data without prior knowledge. Most deep learning based models treat the data collected from different sensors independently, without considering the dependencies across the sensor channels. To solve the above problems, this study proposes a squeeze-and-excitation and Transformer based model for RUL prediction. The squeeze-and-excitation module enables the re-calibration of feature weights to make more efficient use of important features and suppress useless features. The improved Transformer network is applied to have a stronger ability for temporal feature extraction. Experiments are conducted to evaluate the effectiveness of the proposed method, and the results show that the performance of our model is better than other competitive methods.

Decision rules for machining method chains mined from historical machining documents can help technologists quickly design new machining method chains. However, the main factor that limits the practical application of existing rough set models is that the boundary regions are too large. Therefore, a decomposition-reorganization method (DRM) is proposed to mine rules for machining method chains. First, binary coding is used to decompose the existing machining method chains, and the decision rules for a single machining method are mined based on rough set reduction. Then, machining method chains are obtained by reorganizing the machining methods in accordance with the decision rules. DRM can eliminate the boundary regions without human intervention and recommend machining method chains for all features whose parameters have appeared in historical machining documents. Finally, three types of shell parts are used to verify the effectiveness of DRM.