No abstract

The geometric error determines the product quality and function to a certain extent. Among them, the roundness error is one of the important indicators for evaluating the geometric error of shaft parts. With the increase of industrial requirements, there are higher requirements for the accuracy and efficiency of roundness error evaluation. However, most of the traditional roundness error evaluation models used in the industry can no longer meet the needs of current industrial processing in terms of efficiency and accuracy. This paper proposes a new roundness error evaluation method based on twin support vector machines (TWSVMs). First, according to the roundness error evaluation and the TWSVMs theory, the roundness error evaluation model with the TWSVMs is obtained. Then, experimental research and analysis are carried out, and the accuracy and efficiency of the traditional roundness evaluation method and the new method are compared. The research results show that the new roundness evaluation method based on the TWSVMs proposed in this paper can efficiently and accurately evaluate the roundness error, and can be applied to the online evaluation of the roundness error in industrial processing to improve the processing efficiency.

No abstract

In order to solve the problem that the error evaluation delay and the size and roundness of workpiece can not meet the processing requirements at the same time in online measurement. First this paper proposes an online fusion control method for the size and roundness error of workpiece, which can not only improve the processing efficiency, but also improve the consistency of workpiece quality. Then, the Long Short-Term Memory(LSTM) is used to predict the workpiece information of online measurement, and the error is calibrated according to the predicted value. The LSTM is used to predict the workpiece information in real time, and the process parameters are adjusted in time when the prediction value is out of the theoretical boundary to avoid error accumulation. Finallyr the online grinding measurement experiment based on the LSTM is designed and carried out, and the relationship between the dimension of input tensor and the prediction accuracy is analyzed through the experimental results. The results show that the LSTM can accurately predict the grinding size sequence and roundness sequence, and has good universality. The small batch machining is carried out according to the experimental results. Statistical analysis shows that the grinding accuracy is significantly improved by using the fusion prediction and calibration method.