Part Classification Prediction Based on Convolutional Neural Network

Abstract

Mechanical parts are an important part of the machinery industry. However, the current automated production lines for mechanical parts face great challenges. Different types and sizes of parts make automatic segmentation of parts on industrial lines prone to wrong segmentation. Therefore, this paper proposes a part classification method based on a convolutional neural network. The method can utilize a large amount of data of mechanical parts and then analyze and learn from the data through convolutional neural networks to detect and classify parts more accurately. It can effectively solve the problems of high-cost and error-prone manual parts classification and greatly reduce the cost of automatic detection and classification of enterprises. We adopt this model to conduct a series of experiments on the Pacon dataset. It includes the calculation of weights and the construction of confusion matrix values. The experimental results show that the model achieves excellent performance in parts classification and prediction.