Abstract
In recent years, neural network methods with different architectures and training strategies are widely used in machine tool thermal error compensation field, but there are still many problems such as low model accuracy, long training time and bad generalized ability. An integrated neural network classifier is proposed for compensation of thermal error in the paper. The investigation shows that the proposed method has higher classification precision and reliability, and is an ideal pattern classifier. Real cutting experiments are conducted on a CNC turning machine to validate the effectiveness of the method. Both simulation and experiment indicate that the proposed method is quite effective and ubiquitous.
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