Activation functions are the most significant properties of artificial neural networks (ANN) because these functions are directly related with the ability of ANN in learning or modelling a system or a function. Furthermore, another reason for the significance of the fact that determination of optimal activation function in ANN is its relationship with success level. In this experimental study, the effects of different types of wire electrodes, cooling techniques and workpiece materials on surface roughness (Ra) and cutting speed (Vc) in wire electrical discharge machining (WEDM) were investigated by using trainable activation functions (AFt) and modelling them in ANNs. So far, a number of methods have been performed according to the data set in order to optimally predict Ra and Vc results. Among these methods, randomized ANN with AFt was found to be the best one for robust prediction according to RMSE values. While the value was 0.280 for Vc, it was 0.2104 for Ra. Optimum activation functions in Ra and Vc were found at first and third degree trainable functions, respectively.

激活功能是人工神经网络（ANN）的最重要属性，因为这些功能与ANN学习或建模系统或功能的能力直接相关。此外，ANN中最佳激活函数的确定这一事实具有重要性的另一个原因是其与成功水平的关系。在这项实验研究中，通过使用可训练的激活函数（AFt）和电火花加工（WEt），研究了不同类型的线电极，冷却技术和工件材料对线放电加工（WEDM）中的表面粗糙度（Ra）和切削速度（Vc）的影响。在人工神经网络中对其建模。到目前为止，已经根据数据集执行了许多方法，以便最佳地预测Ra和Vc结果。在这些方法中，根据RMSE值，发现具有AFt的随机ANN是进行稳健预测的最佳方法。Vc的值为0.280，Ra的值为0.2104。在一级和三级可训练函数中分别找到了Ra和Vc中的最佳激活函数。