Self-Organizing Map Neural Network Algorithm for the Determination of Fracture Location in Solid-State Process joined Dissimilar Alloys
This work addresses fracture prediction in solid-state process joined dissimilar alloys, which is an incremental application of an existing unsupervised learning method to a new domain.
The paper tackled the problem of predicting fracture locations in dissimilar friction stir welded alloys by implementing a Self-Organizing Map Neural Network algorithm, achieving a prediction accuracy of 96.92%.
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.