Multistation machining process is widely applied in contemporary manufacturing environment. Modeling of variation propagation in multistation machining process is one of the most important research scenarios. Due to t...Multistation machining process is widely applied in contemporary manufacturing environment. Modeling of variation propagation in multistation machining process is one of the most important research scenarios. Due to the existence of multiple variation streams, it is challenging to model and analyze variation propagation in a multi-station system. Current approaches to error modeling for multistation machining process are not explicit enough for error control and ensuring final product quality. In this paper, a mathematic model to depict the part dimensional variation of the complex multistation manufacturing process is formulated. A linear state space dimensional error propagation equation is established through kinematics analysis of the influence of locating parameter variations and locating datum variations on dimensional errors, so the dimensional error accumulation and transformation within the multistation process are quantitatively described. A systematic procedure to build the model is presented, which enhances the way to determine the variation sources in complex machining systems. A simple two-dimensional example is used to illustrate the proposed procedures. Finally, an industrial case of multistation machining part in a manufacturing shop is given to testify the validation and practicability of the method. The proposed analytical model is essential to quality control and improvement for multistation systems in machining quality forecasting and design optimization.展开更多
Bistatic/multistatic radar has great potential advantages over its monostatic counterpart. However, the separation of a transmitter and a receiver leads to difficulties in locating the target position accurately and g...Bistatic/multistatic radar has great potential advantages over its monostatic counterpart. However, the separation of a transmitter and a receiver leads to difficulties in locating the target position accurately and guaranteeing space-timefrequency synchronization of the transmitter and the receiver.The error model of space-time-frequency synchronization in a motion platform of bistatic/multistatic radar is studied. The relationship between the space synchronization error and the transmitter platform position, receiver platform position, moving state, and beam pointing error, is analyzed. The effect of space synchronization error on target echo power is studied. The target scattering characteristics are restructured by many separate scattering centers of the target in high frequency regions. Based on the scattering centers model of the radar target, this radar target echo model and the simulation method are discussed. The algorithm of bistatic/multistatic radar target echo accurately reflects the scattering characteristics of the radar target, pulse modulation speciality of radar transmitting signals, and spacetime-frequency synchronization error characteristics between the transmitter station and the receiver station. The simulation of bistatic radar is completed in computer, and the results of the simulation validate the feasibility of the method.展开更多
针对现有MOESP(multiple-input multiple-output output-error state space model identification)和N4SID(numerical algorithm for subspace state space systemidentification)算法在计算状态空间模型系统矩阵(A、B、C、D)时的不足,...针对现有MOESP(multiple-input multiple-output output-error state space model identification)和N4SID(numerical algorithm for subspace state space systemidentification)算法在计算状态空间模型系统矩阵(A、B、C、D)时的不足,提出1种改进的子空间辨识方法。该方法利用MOESP算法可以根据系统观测矩阵直接计算出系统矩阵A和输出矩阵C的优点,先计算矩阵A和C,然后采用N4SID算法计算输入矩阵B和前馈矩阵D。该方法既能够避免MOESP算法在计算矩阵B和D时需要构建大矩阵的缺点,又能避免N4SID算法在计算矩阵A和C时需要求解线性最小二乘的问题,降低了算法的复杂性。将该算法应用于某天然气电站和Alstom气化炉模型的辨识中,通过考核算法的CPU运算时间、CPU浮点数运算次数(floating-pointoperations,FLOPS)和相对误差等指标,将该算法与原有MOESP和N4SID算法进行了比较。计算结果表明,改进的子空间辨识算法能够在保证较好辨识精度的前提下,提高原有算法的计算效率,特别是在大容量数据样本条件下,能够有效降低CPU运算时间和FLOPS。展开更多
基金supported by National Department Fundamental Research Foundation of China (Grant No. B222090014)National Department Technology Fundatmental Foundaiton of China (Grant No. C172009C001)
文摘Multistation machining process is widely applied in contemporary manufacturing environment. Modeling of variation propagation in multistation machining process is one of the most important research scenarios. Due to the existence of multiple variation streams, it is challenging to model and analyze variation propagation in a multi-station system. Current approaches to error modeling for multistation machining process are not explicit enough for error control and ensuring final product quality. In this paper, a mathematic model to depict the part dimensional variation of the complex multistation manufacturing process is formulated. A linear state space dimensional error propagation equation is established through kinematics analysis of the influence of locating parameter variations and locating datum variations on dimensional errors, so the dimensional error accumulation and transformation within the multistation process are quantitatively described. A systematic procedure to build the model is presented, which enhances the way to determine the variation sources in complex machining systems. A simple two-dimensional example is used to illustrate the proposed procedures. Finally, an industrial case of multistation machining part in a manufacturing shop is given to testify the validation and practicability of the method. The proposed analytical model is essential to quality control and improvement for multistation systems in machining quality forecasting and design optimization.
基金supported by the National Natural Science Foundation of China(61271327)
文摘Bistatic/multistatic radar has great potential advantages over its monostatic counterpart. However, the separation of a transmitter and a receiver leads to difficulties in locating the target position accurately and guaranteeing space-timefrequency synchronization of the transmitter and the receiver.The error model of space-time-frequency synchronization in a motion platform of bistatic/multistatic radar is studied. The relationship between the space synchronization error and the transmitter platform position, receiver platform position, moving state, and beam pointing error, is analyzed. The effect of space synchronization error on target echo power is studied. The target scattering characteristics are restructured by many separate scattering centers of the target in high frequency regions. Based on the scattering centers model of the radar target, this radar target echo model and the simulation method are discussed. The algorithm of bistatic/multistatic radar target echo accurately reflects the scattering characteristics of the radar target, pulse modulation speciality of radar transmitting signals, and spacetime-frequency synchronization error characteristics between the transmitter station and the receiver station. The simulation of bistatic radar is completed in computer, and the results of the simulation validate the feasibility of the method.
文摘针对现有MOESP(multiple-input multiple-output output-error state space model identification)和N4SID(numerical algorithm for subspace state space systemidentification)算法在计算状态空间模型系统矩阵(A、B、C、D)时的不足,提出1种改进的子空间辨识方法。该方法利用MOESP算法可以根据系统观测矩阵直接计算出系统矩阵A和输出矩阵C的优点,先计算矩阵A和C,然后采用N4SID算法计算输入矩阵B和前馈矩阵D。该方法既能够避免MOESP算法在计算矩阵B和D时需要构建大矩阵的缺点,又能避免N4SID算法在计算矩阵A和C时需要求解线性最小二乘的问题,降低了算法的复杂性。将该算法应用于某天然气电站和Alstom气化炉模型的辨识中,通过考核算法的CPU运算时间、CPU浮点数运算次数(floating-pointoperations,FLOPS)和相对误差等指标,将该算法与原有MOESP和N4SID算法进行了比较。计算结果表明,改进的子空间辨识算法能够在保证较好辨识精度的前提下,提高原有算法的计算效率,特别是在大容量数据样本条件下,能够有效降低CPU运算时间和FLOPS。
