Mechanical parts of engines help to reduce friction and carry weight for linear or rotating motion. Modern engines are complex systems with structural elements, mechanisms, and mechanical parts. The building blocks of the engines are joined together using several mechanical components that are similar in shape and size. During the assembly and disassembly of these complex engines, the mechanical components get mixed up. The traditional classification techniques for components are laborious with high costs. Existing research for classifying mechanical components uses algorithms that work based on shape descriptors and geometric similarity thereby resulting in low accuracies. Hence, there is a need to develop an automatic classification technique with high accuracy. This study classified four mechanical components (bearing, nut, gear, and bolt) using four deep learning models (AlexNet, DenseNet-121, ResNet-50, and SqueezeNet). In the result, Densenet-121 achieved the highest performance at an accuracy of 98.3%, sensitivity of 95.8%, specificity of 98.5%, and area under curve of 98.5%.

Keywords : Mechanical Components, Deep Learning, Convolutional Neural Network, Engine, Transfer Learning.