ABSTRACT In the present study, Artificial Neural Network (ANN) approaches were adopted for the prediction of thrust force (Fz) and torque (Mz) during drilling of St60 workpiece, according to important cutting parameters such as cutting velocity, feed rate, and cutting tool diameter. During the setup of an ANN, some essential difficulties like the determination of network architecture, the determination of weight coefficients and the selection of training algorithm should be addressed. A combination of genetic algorithm and neural networks (GA-ANN) formulates those difficulties as an optimization problem and resolve it by the help of a suitable optimization method. Finally, a comparison between ANN with network architecture determined by a simple trial and error approach and ANN with architecture determined by a GA-ANN approach is conducted. The comparison of the models showed clearly that adopting genetic algorithm (GA) equals to the improvement of the efficiency of the network performance.

中文翻译：

---

使用遗传算法优化 ANN 的可持续钻井作业模型

摘要 在本研究中，根据切削速度、进给率和切削量等重要切削参数，采用人工神经网络 (ANN) 方法预测 St60 工件钻孔过程中的推力 (Fz) 和扭矩 (Mz)。刀具直径。在建立人工神经网络的过程中，需要解决一些基本的困难，如网络架构的确定、权重系数的确定和训练算法的选择。遗传算法和神经网络 (GA-ANN) 的结合将这些困难表述为一个优化问题，并通过合适的优化方法来解决它。最后，进行了具有由简单试错法确定的网络架构的 ANN 与具有由 GA-ANN 方法确定的架构的 ANN 之间的比较。