微小孔振动钻削神经网络仿真

    Micro-Hole Vibration Drilling Simulation Based on Artificial Neural Network

    • 摘要: 该文将人工神经网络(ANN)技术引入农业机械化、农业自动化领域中高新技术产品孔加工过程。研究了适用于变参数振动钻削过程仿真与参数优化的神经网络模型和算法。实验表明,ANN成功地实现了振动钻削知识的学习,优化精度较高,为农业机械制造智能化提供了新的研究思路和开发途径。

       

      Abstract: This paper introduced Artificial Neural Network(ANN) into vibration drilling. The model and algorithm of ANN that were applied to varying-parameters vibration drilling simulation and parameter optimization were mainly studied. In this paper, the orthogonal test method was used to organize training samples. Because of its lack, K-dimension crossing test method was also used. There were 27 groups of samples, which were divided into 9 training groups. In training processing. The different samples were input in intersection way, and one of the samples was input as different as possible from another. Network was trained for 9 times, and every time one subgroup was out of training, the selected one was used to test generalization errors. The experiments showed that the ANN systems are of high precision. It also provided a new method for vibration drilling studying and analysis.

       

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