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.

本研究利用计算神经科学的方法，使用自组织映射神经网络来识别摩擦搅拌焊接中异种材料的断口位置，即热机械影响区的铜或铝。结果表明，该算法可以以 96.92% 的准确率预测断裂位置。