The Feed-Forward and Backpropagation Artificial Neural Networks (FFBP-ANN) are generally employed for cut surfaces quality characteristics predictions. However, the determination of the neurons on the hidden layer and the training parameters’ values are tasks requiring many trials according to the Full-Factorial Approach (FFA). Therefore, in this work, a methodology is presented for the optimization of an FFBP-NN and the application of the Taguchi Design of Experiments (TDE). Nine combinations of four variables were examined, having three levels each, according to the L9 (3⁴) orthogonal array. The number of neurons in the hidden layer (N), the learning rate (mu), the increment factor (mu+) and the decrement factor (mu-) are employed as variables. In addition, Mean Squared Error (MSE) and overall regression index (Rall) was decided as the objective functions. Thus, TDE diminishes the FFBP-ANN arrangements to nine from eighty-one of FFA. The optimized FFBP-ANN predicts the surface roughness in various cut depths during laser cutting of thin thermoplastic plates.

前馈和反向传播人工神经网络 (FFBP-ANN) 通常用于切割表面质量特性预测。然而，根据全因子方法（FFA），确定隐藏层上的神经元和训练参数的值是需要多次试验的任务。因此，在这项工作中，提出了一种优化 FFBP-NN 和应用田口实验设计 (TDE) 的方法。根据 L9 (3 ⁴) 正交阵列。隐藏层中的神经元数量 (N)、学习率 (mu)、增量因子 (mu+) 和减量因子 (mu-) 被用作变量。此外，均方误差（MSE）和总体回归指数（Rall）被确定为目标函数。因此，TDE 将 FFBP-ANN 安排从 FFA 的八十一个减少到九个。优化的 FFBP-ANN 可预测在激光切割薄热塑性板期间不同切割深度的表面粗糙度。