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In high-speed cutting processes, traditional Nyquist-Shannon sampling theorem was used for data collection which confront difficult problems of storage, transmission and processing for large amount of cutting force signals. A novel method of cutting force signal acquisition was proposed to realize the compression acquisition of signals based on the ompressed sensing theory. Gauss random matrix was selected as the basic measurement matrix and was redesigned by combining the approximate orthogonal upper triangular decomposition and the minimum correlation coefficient method to improve the compression measurement performance. Then the original cutting force signals were reconstructed from the measurement values by using the efficient compressive sampling matching pursuit algorithm. The experimental results show that the improved Gauss random measurement matrix has higher reconstruction accuracy and stability, and the proposed method greatly reduce the amount of data while ensuing the reconstruction efficiency and accuracy of cutting forces. [ABSTRACT FROM AUTHOR]

In the 3D printing processes, aiming at the problems that the support volumes of the model needed to decrease after printing, the relationships between the printing orientations and the support volumes were studied, and a method to search the printing orientations toward the minimal theory support volumes was developed. First, the background of theory support volumes of discrete grid models was introduced, and a calculative method was suggested to minimize the theory support volumes. Second, the continuity of the theory support volume functions was analyzed to overcome the discontinuity problems of the functions. A derivative-free optimization algorithm was proposed to obtain the correct orientation of minimum theory support volumes. Experiments show that the proposed algorithm may accurately calculate the correct orientations corresponding to the theory support volumes. [ABSTRACT FROM AUTHOR]