Bayesian Network Learn Method for Machine Tool Thermal Stability Modeling
Maintain the thermal stability of the machine tool is a common problem to achieve intelligent and precise processing control, and its difficulty lies in modeling and real-time compensation. In this paper, considering the correlation of various factors, the correlation of those factors according to experiment data was analysis and optimized, and a dynamic model of thermal error compensation of CNC machine tool based on Bayesian Network theory was found. Moreover, because of the self-learning feature of Bayesian network, the model can be continuously optimized by updating dynamic coefficient, and reflect the changes of processing condition. Finally, the feasibility and validation of this model were proved through the experiment.
Xiaoming Sang, Pengcheng Wang, Liqun Ai, Yungang Li and Jinglong Bu
X. Li and D. F. Feng, "Bayesian Network Learn Method for Machine Tool Thermal Stability Modeling", Advanced Materials Research, Vols. 284-286, pp. 932-935, 2011