A Novel Approach of Latency and Energy Efficiency Analysis of IIoT With SQL and NoSQL Databases Communication

Industrial Internet of Things (IIoT)-enabled production facilities generate vast amounts of data, which, if harnessed effectively, can substantially enhance manufacturing efficiency through latency reduction. The selection of the appropriate data storage technology is a pivotal consideration in achieving this objective. While prior studies have examined SQL and NoSQL databases in terms of latency and energy efficiency, these evaluations have not been conducted specifically within the context of IIoT. This paper aims to fill this research gap by conducting a rigorous comparison of SQL and NoSQL databases, focusing on their performance latency and energy efficiency when interfaced with IoT nodes. By elucidating these relationships, our research offers actionable insights that can guide IIoT-enabled manufacturing facilities in optimizing their operations. Specifically, the paper aids in the selection of the most suitable database technology, thereby contributing to latency minimization and efficiency maximization in industrial settings

Intelligent Machine Vision Model for Defective Product Inspection Based on Machine Learning

Quality control is one of the industrial tasks most susceptible to be improved by implementing technological innovations. As an innovative technology, machine vision enables reliable and fast 24/7 inspections and helps producers to improve the efficiency of manufacturing operations. The accessible data by vision equipment will be used to identify and report defective products, understand the causes of deficiencies and allow rapid and efficient intervention in smart factories. From this perspective, the proposed machine vision model in this paper combines the identification of defective products and the continuous improvement of manufacturing processes by predicting the most suitable parameters of production processes to obtain a defect-free item. The suggested model exploits all generated data by various integrated technologies in the manufacturing chain, thus meeting the requirements of quality management in the context of Industry 4.0, based on predictive analysis to identify patterns in data and suggest corrective actions to ensure product quality. In addition, a comparative study between several machine learning algorithms, both for product classification and process improvement models, is performed in order to evaluate the designed system. The results of this study show that the proposed model largely meets the requirements for the proper implementation of these techniques

Intelligent Machine Vision Model for Defective Product Inspection Based on Machine Learning

Quality control is one of the industrial tasks most susceptible to be improved by implementing technological innovations. As an innovative technology, machine vision enables reliable and fast 24/7 inspections and helps producers to improve the efficiency of manufacturing operations. The accessible data by vision equipment will be used to identify and report defective products, understand the causes of deficiencies and allow rapid and efficient intervention in smart factories. From this perspective, the proposed machine vision model in this paper combines the identification of defective products and the continuous improvement of manufacturing processes by predicting the most suitable parameters of production processes to obtain a defect-free item. The suggested model exploits all generated data by various integrated technologies in the manufacturing chain, thus meeting the requirements of quality management in the context of Industry 4.0, based on predictive analysis to identify patterns in data and suggest corrective actions to ensure product quality. In addition, a comparative study between several machine learning algorithms, both for product classification and process improvement models, is performed in order to evaluate the designed system. The results of this study show that the proposed model largely meets the requirements for the proper implementation of these techniques