Konovalenko Ihor, Maruschak Pavlo, Prentkovskis Olegas, Junevičius Raimundas
Department of Industrial Automation, Ternopil National Ivan Pul'uj Technical University, Rus'ka str. 56, 46001 Ternopil, Ukraine.
Department of Mobile Machinery and Railway Transport, Faculty of Transport Engineering, Vilnius Gediminas Technical University, Plytinės g. 27, LT-10105 Vilnius, Lithuania.
Materials (Basel). 2018 Dec 5;11(12):2467. doi: 10.3390/ma11122467.
The research of fractographic images of metals is an important method that allows obtaining valuable information about the physical and mechanical properties of a metallic specimen, determining the causes of its fracture, and developing models for optimizing its properties. One of the main lines of research in this case is studying the characteristics of the dimples of viscous detachment, which are formed on the metal surface in the process of its fracture. This paper proposes a method for detecting dimples of viscous detachment on a fractographic image, which is based on using a convolutional neural network. Compared to classical image processing algorithms, the use of the neural network significantly reduces the number of parameters to be adjusted manually. In addition, when being trained, the neural network can reveal a lot more characteristic features that affect the quality of recognition in a positive way. This makes the method more versatile and accurate. We investigated 17 models of convolutional neural networks with different structures and selected the optimal variant in terms of accuracy and speed. The proposed neural network classifies image pixels into two categories: "dimple" and "edge". A transition from a probabilistic result at the output of the neural network to an unambiguously clear classification is proposed. The results obtained using the neural network were compared to the results obtained using a previously developed algorithm based on a set of filters. It has been found that the results are very similar (more than 90% similarity), but the neural network reveals the necessary features more accurately than the previous method.
金属断口图像研究是一种重要方法，可获取有关金属试样物理和力学性能的有价值信息，确定其断裂原因，并开发优化其性能的模型。在这种情况下，主要研究方向之一是研究粘性分离韧窝的特征，这些韧窝是在金属断裂过程中在其表面形成的。本文提出了一种基于卷积神经网络的断口图像上粘性分离韧窝检测方法。与经典图像处理算法相比，神经网络的使用显著减少了手动调整的参数数量。此外，在训练时，神经网络可以揭示更多以积极方式影响识别质量的特征。这使得该方法更加通用和准确。我们研究了17种不同结构的卷积神经网络模型，并在准确性和速度方面选择了最优变体。所提出的神经网络将图像像素分为两类：“韧窝”和“边缘”。提出了一种从神经网络输出的概率结果到明确清晰分类的转换方法。将使用神经网络获得的结果与使用基于一组滤波器的先前开发算法获得的结果进行了比较。结果发现，两者结果非常相似（相似度超过90%），但神经网络比先前方法更准确地揭示了所需特征。
