Kosarac Aleksandar, Mladjenovic Cvijetin, Zeljkovic Milan, Tabakovic Slobodan, Knezev Milos
Faculty of Mechanical Engineering, University of East Sarajevo, 71123 Istočno Sarajevo, Bosnia and Herzegovina.
Department of Production Engineering, Faculty of Technical Sciences, University of Novi Sad, 21000 Novi Sad, Serbia.
Materials (Basel). 2022 Jan 18;15(3):700. doi: 10.3390/ma15030700.
Surface quality is one of the most important indicators of the quality of machined parts. The analytical method of defining the arithmetic mean roughness is not applied in practice due to its complexity and empirical models are applied only for certain values of machining parameters. This paper presents the design and development of artificial neural networks (ANNs) for the prediction of the arithmetic mean roughness, which is one of the most common surface roughness parameters. The dataset used for ANN development were obtained experimentally by machining AA7075 aluminum alloy under various machining conditions. With four factors, each having three levels, the full factorial design considers a total of 81 experiments that have to be carried out. Using input factor-level settings and adopting the Taguchi method, the experiments were reduced from 81 runs to 27 runs through an orthogonal design. In this study we aimed to check how reliable the results of artificial neural networks were when obtained based on a small input-output dataset, as in the case of applying the Taguchi methodology of planning a four-factor and three-level experiment, in which 27 trials were conducted. Furthermore, this paper considers the optimization of machining parameters for minimizing surface roughness in machining AA7075 aluminum alloy. The results show that ANNs can be successfully trained with small data and used to predict the arithmetic mean roughness. The best results were achieved by backpropagation multilayer feedforward neural networks using the BR algorithm for training.
表面质量是机械加工零件质量的最重要指标之一。由于定义算术平均粗糙度的分析方法较为复杂，在实际中未被应用，仅针对特定加工参数值应用经验模型。本文介绍了用于预测算术平均粗糙度（最常见的表面粗糙度参数之一）的人工神经网络（ANN）的设计与开发。用于ANN开发的数据集是通过在各种加工条件下对AA7075铝合金进行加工实验获得的。该全因子设计有四个因素，每个因素有三个水平，总共需要进行81次实验。利用输入因子水平设置并采用田口方法，通过正交设计将实验次数从81次减少到27次。在本研究中，我们旨在检验基于小输入输出数据集（如应用四因素三水平实验的田口方法进行27次试验的情况）获得人工神经网络结果时的可靠性。此外，本文考虑了对AA7075铝合金加工中的加工参数进行优化以最小化表面粗糙度。结果表明，ANN可以用少量数据成功训练，并用于预测算术平均粗糙度。使用BR算法进行训练的反向传播多层前馈神经网络取得了最佳结果。
