Machine Learning in Industrial IoT Applications for Safety, Security, Asset Localization, Quality Assurance, and Sustainability in Smart Production

Abstract

This study explores the integration of machine learning techniques, notably Support Vector Machines (SVM) and Convolutional Neural Networks (CNN), with industrial production processes for quality assurance. The emphasis is on examining the performance of SVM and CNN through a rigorous assessment of precision, recall, and F1 score in the Performance Metrics Evaluation. Additionally, the study tests the algorithms against existing baseline approaches, evaluating their accuracy and efficiency in fault identification. The results reveal the consistent and strong performance of SVM and CNN, highlighting their revolutionary potential in revolutionizing quality control systems. The findings provide essential insights into the properties of each algorithm, demonstrating their ability to outperform existing methods and contribute to a more versatile and efficient approach to quality assurance in industrial settings.