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.
翻译: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大脑功能进行调查。为了了解大脑如何处理信息,它也可以包括信号处理、计算机科学和物理学等各种方法。在目前的工作中,首次采用了基于神经生物学的未经监督的机器学习算法,即自组织地图神经网络,以确定不同摩擦中断裂的位置,这些摩擦被焊接的AA5754-C11000合金、太肩诊断仪、工具旋转速度(RPM)和工具轨迹速度(mm/min)是输入参数,而铜的断裂区(TMAZ)的铜标本骨折或铝的TMAZ的断裂。结果显示,执行的算法能够以96.92%的精确度预测断裂地点。