Papers by Author: Qian Jian Guo

Abstract: With the increasing requirements of the machining accuracy of CNC machine tools, the impact of thermal deformation is growing. Thermal error compensation technology can predict and compensate the thermal errors in real-time, and improve the machining accuracy of the machine tool. In this paper, the research objects of thermal error compensation is expanded to the volumetric error of the machine tool, the volumetric error modeling of a three-axis machine tool is fulfilled and a compensator is developed for the compensation experiment, which provides scientific basis for the improvement of the machining accuracy.

Abstract: Honeycomb ceramic is the key component of the regenerative system. The numerical simulation was performed using FLUENT, a commercial computational fluid dynamics (CFD) code, to compare simulation results to the test data. The regenerative process of a honeycomb ceramic regenerator was simulated under different conditions. Experiments were carried out on honeycomb regenerators that are contained in a methane oxidation reactor. The calculated temperatures of flue gas inlet were compared with the ones measured. The tendency of the temperature is the same as the experiment.

Abstract: Four key temperature points of a CNC machine tool were obtained in this paper, and a thermal error model based on the four key temperature points was proposed by using based back propagation neural network. A thermal error compensation system was developed based on the proposed model, and which has been applied to the CNC machine tool in daily production. The results show that the thermal error in workpiece diameter has been reduced from 33 to 6 .

Abstract: The thermal distortion of YK3610 hobbing machine is analyzed. The concept of clustering analysis is proposed and implemented on the gear hobbing machine. The model was used in the experimental of thermal error compensation. The results show that the thermal error compensation control system can reduce thermal errors significantly and the prediction accuracy of the thermal error model is high enough.

Abstract: This paper proposes a new thermal error modeling methodology called Clustering Regression Thermal Error Modeling which not only improves the accuracy and robustness but also saves the time and cost of gear hobbing machine thermal error model. The major heat sources causing poor machining accuracy of gear hobbing machine are investigated. Clustering analysis method is applied to reduce the number of temperature sensors. Least squares regression modeling approach is used to build thermal error model for thermal error on-line prediction of gear hobbing machine. Model performance evaluation through thermal error compensation experiments shows that the new methodology has the advantage of higher accuracy and robustness.

Abstract: Through analysis of the thermal errors affected NC machine tool, a new prediction model based on BP neural networks is presented, and ant colony algorithm is applied to train the weights of neural network model. Finally, thermal error compensation experiment is implemented, and the thermal error is reduced from 35μm to 6μm. The result shows that the local minimum problem of BP neural network is overcome, and the model accuracy is improved.