Fusheng Liang

As a high-precision equipment for solid-phase crystallization, surface mount technology (SMT) machine, whose accuracy has an important impact on the packaging of semiconductor chips. Due to the ultra-high speed reciprocating characteristics of the SMT machine, thermal error (TE) is the main influence factor. To reduce influence of TE on machining accuracy of SMT machine, a TE modeling method based on zebra optimization algorithm optimized least squares support vector machine (ZOA-LSSVM) is proposed. To start with, Canopy clustering algorithm is used to perform ‘coarse’ clustering on the collected temperature variables to give the number of clusters and the initial cluster center. K-means clustering algorithm is used to cluster temperatures based on results provided by Canopy. Using entropy, an indicator of cluster validity, selecting the temperature point with the highest entropy value from each group as the temperature-sensitivity point (TSP), which can reduce the correlation interference between temperature measurement points. Secondly, with selected TSP as the input, the least squares support vector machine (LSSVM) prediction model is constructed, where zebra optimization algorithm (ZOA) is introduced to optimize penalty parameter γ and kernel function σ , and ZOA-LSSVM TE prediction model is established. Finally, taking X -axis of SMT machine as the object, the measurement and verification experiments for the machine tool’s linear axis were designed. The evaluation indexes, namely mean absolute percentage error ( MAPE ), mean absolute error ( MAE ), root mean square error ( RMSE ) and coefficient of determination ( R ² ) are introduced to conduct comparative evaluation on each model. For the CK-ZOA-LSSVM model, the RMSE is 0.0015471, MAE is 0.00055268, MAPE is 0.0000006835, and R ² is 0.95528.

Real-time parametric interpolation plays a crucial role in achieving high-speed and high-precision multi-axis CNC machining. In the interpolation cycle, the position of the next interpolation point is required to be calculated in real-time to guide the action of the machining process. Due to the existence of the positioning error of the interpolation point, it is extremely difficult to eliminate the feedrate fluctuation, which may lead to dramatic decreases in machining quality and the driving capabilities’ saturation of each axis. A computationally efficient and precise feedrate fluctuation minimization method is proposed for the NURBS tool path interpolation in the CNC milling process. The model for the arc length and curvature, with respect to the parameter of the NURBS tool path, is established to reduce the calculation amount required by interpolation points determination. The deviation between the theoretical and actual interpolation step length is decreased by the proposed arc length compensation method to minimize the feedrate fluctuation. In addition, the interpolation points derived from the arc length compensation process are further corrected by performing the Newton iteration to restrict the feedrate fluctuation within the preset accuracy threshold. The effectiveness and superiorities of the proposed feedrate fluctuation minimization method are verified by simulation and milling experiments.

Robots with openness and flexibility have attracted a large number of researchers to conduct in-depth studies in the field of surface machining. However, there is a redundant degree of freedom (DOF) in 6-DOF robot machining: when a ball end milling cutter is used to process curved parts, the tool point needs to strictly follow the planned milling trajectory, but the tool axis vector only needs to be within a certain range. During the machining process, the rotation of the tool around its axis is not constrained. Therefore, it is necessary to optimize the redundant DOF. Aiming at the redundant DOF of the tool axis vector in ball end milling for surface parts, a Redundancy Optimization strategy for Minimum Joint trajectory (ROMJ) is proposed. It takes the shortest trajectory of robot joints as the optimization objective, and the numerical optimization method is adopted to carry out the optimal design of tool axis vector trajectory in the milling process. Before optimization, to decrease the data volume, the number of track points is sampled and adjusted based on curve characterization errors. In the optimization process, considering the obvious difference in the load quality characteristics of the robot joints, a Redundancy Optimization strategy for Minimum Joint trajectory considering the different Load of joints (ROMJ-L) is proposed. The load difference coefficients of each joint are introduced into the optimization objective of the trajectory of robot joints. By using this method, the optimal design of each joint trajectory of the robot is realized. In order to verify the methods proposed in this paper, a comparison experiment is carried out. The results show that under the same tool point trajectory, the proposed methods can significantly reduce the robot joint trajectory, and the joint trajectory is influenced by the load difference of each joint. Finally, an Eflin-10 robot is used to process the butterfly trajectory tool path by the trajectory planned by the ROMJ-L method, and the results show that the method is practical.

In order to investigate the thermal effect of a servo axis’ positioning error on the accuracy of machine tools, an empirical modeling method was proposed, which considers both the geometric and thermal positioning error. Through the analysis of the characteristics of the positioning error curves, the initial geometric positioning error was modeled with polynomial fitting, while the thermal positioning error was built with an empirical modeling method. Empirical modeling maps the relationship between the temperature points and thermal error directly, where the multi-collinearity among the temperature variables exists. Therefore, fuzzy clustering combined with principal component regression (PCR) is applied to the thermal error modeling. The PCR model can preserve information from raw variables and eliminate the effect of multi-collinearity on the error model to a certain degree. The advantages of this modeling method are its high-precision and strong robustness. Experiments were conducted on a three-axis machine tool. A criterion was also proposed to select the temperature-sensitivity points. The fitting accuracy of the comprehensive error modeling could reach about 89%, and the prediction accuracy could reach about 86%. The proposed modeling method was proven to be effective and accurate enough to predict the positioning error at any time during the machine tool operation.

Optical interferometry plays an important role in the topographical surface measurement and characterization in precision/ultra-precision manufacturing. An appropriate surface reconstruction algorithm is essential in obtaining accurate topography information from the digitized interferograms. However, the performance of a surface reconstruction algorithm in interferometric measurements is influenced by environmental disturbances and system noise. This paper presents a comparative analysis of three algorithms commonly used for coherence envelope detection in vertical scanning interferometry, including the centroid method, fast Fourier transform (FFT), and Hilbert transform (HT). Numerical analysis and experimental studies were carried out to evaluate the performance of different envelope detection algorithms in terms of measurement accuracy, speed, and noise resistance. Step height standards were measured using a developed interferometer and the step profiles were reconstructed by different algorithms. The results show that the centroid method has a higher measurement speed than the FFT and HT methods, but it can only provide acceptable measurement accuracy at a low noise level. The FFT and HT methods outperform the centroid method in terms of noise immunity and measurement accuracy. Even if the FFT and HT methods provide similar measurement accuracy, the HT method has a superior measurement speed compared to the FFT method.