Papers by Author: Jian Liang Guo

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Authors: Jian Liang Guo, Lian Qing Chen
Abstract: Turning operation of slender shaft poses a great challenge in machining field. It is due to the fact that the accuracy of machined workpiece depends almost completely on the operator’s skill by using conventional methods. To improve the accuracy of slender shaft with less operation skill, this paper presents an error compensation system, which includes a fuzzy PID controller. As a part of the controller, the traditional PID controller is used to control a follower rest for improving the dimensional accuracy. And the fuzzy controller incorporates skilled operators’ knowledge into an automatic machining system. At the end, turning experiments are carried out to verify the efficacy of the error compensation system. The experimental results indicate that the dimensional accuracy of slender shaft using the system is higher and more stable than that by using conventional methods.
Authors: Bo Di Cui, Jian Liang Guo
Abstract: Accurate predictive modeling is an essential prerequisite for optimization and control of production in modern manufacturing environments. For slender bar turning operations, dimensional deviation is one of the most important product quality characteristics due to the low stiffness of part. In this study, radial basis function neural network is employed to investigate dimensional errors in slender bar turning. The relationship between cutting parameters and dimensional errors is firstly described by the proposed model. Simulation is provided to investigate the effects of cutting parameters on dimensional errors. Further, real-time predictive model based on radial basis function neural network is developed to perform the dimensional error monitoring during slender bar turning process. Experiments verify that the proposed in-process predictive system has the ability to monitor efficiently dimensional errors within the range that they have been trained.
Authors: Jian Liang Guo, Lian Qing Chen, Jun Chi, Xun Yang
Abstract: Due to the difficulty of real-time directly measuring the surface roughness of workpiece, on-line control of grinding quality remains a challenging problem. In this paper, surface roughness of slender shaft during grinding is predicted by using multi-sensor data fusion. Location of the grinding point along the workpiece axis is measured by a grating ruler while an eddy sensor mounted on the machined body provides the workpiece vibration information. The data from these sensors fuse to give an estimation of the workpiece surface roughness. Grinding experiments were carried out to validate this method and the results indicate that the prediction error of Ra is not over 0.1 um for the given setup.
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