Screw Production Optimization Using Artificial Neural Network (ANN) Technology

Abstract

The application of artificial neural network model and techniques for the optimization of screw production has been investigated. Detection of defects in a company’s production processes is an essential and vital aspect of maintaining quality and smooth production control. The production process can give rise to numerous shortfalls, leading to maximum production expenses. As a result of this, scrutinizing screws, which constitute an important component of various mechanical parts, emerges as a pivotal procedure. This study considered countersunk head 5.2mm screws with stripped screws defects, surface dirt, and surface damage. Industrial camera with efficient specifications for image acquisition of the screws and Deep Convolutional Neural Network (DCNN) which involves software tools (Google’s machine learning architecture Tensorflow, and Keras) technique were utilized for the detection of micro flaws on metal screw surfaces. From the results obtained, the proposed convolutional neural network technique results were compared with traditional machine vision technique such as using LeNet-5 and a detection accuracy of 97% and average detection time per picture is 1.3 s, making the technique faster in processing time and higher in accuracy compare to other validated techniques.