论文标题
用于确定固态过程中断裂位置的自组织MAP神经网络算法连接了不同的合金
Self-Organizing Map Neural Network Algorithm for the Determination of Fracture Location in Solid-State Process joined Dissimilar Alloys
论文作者
论文摘要
被称为计算神经科学的主题领域涉及使用数学技术和理论对脑功能进行研究。为了理解大脑如何处理信息，它还可以包括信号处理，计算机科学和物理学的各种方法。在目前的工作中，首次实施了基于神经生物学的机器学习算法，即实施自组织的MAP神经网络，以确定不同的摩擦搅拌焊接AA5754-C11000合金中的断裂位置。肩直径（mm），刀具旋转速度（RPM）和刀具横幅速度（mm/min）是输入参数，而断裂位置，即铜的铜（TMAZ）的样品骨折是铜的铜或IT裂缝的aluminium tmaz骨折。结果表明，实施的算法能够以96.92％的精度预测断裂位置。
The subject area known as computational neuroscience involves the investigation of brain function using mathematical techniques and theories. In order to comprehend how the brain processes information, it can also include various methods from signal processing, computer science, and physics. In the present work, for the first time a neurobiological based unsupervised machine learning algorithm i.e., Self-Organizing Map Neural Network is implemented for determining the fracture location in dissimilar friction stir welded AA5754-C11000 alloys. Too Shoulder Diameter (mm), Tool Rotational Speed (RPM), and Tool Traverse Speed (mm/min) are input parameters while the Fracture location i.e. whether the specimen fracture at Thermo-Mechanically Affected Zone (TMAZ) of copper or it fractures at TMAZ of Aluminium. The results showed that the implemented algorithm is able to predict the fracture location with 96.92% accuracy.
