A Cloud-based IIoT System for Quality Control in Manufacturing: Techniques and Applications

Abstract

The focus of this literature review is on IIoT quality control systems for industrial businesses that are hosted in the cloud. Cloud-based IIoT systems were found to be useful for collecting and analysing data in real time, allowing manufacturers to spot and fix quality control issues as soon as they arise, according to the research. It is possible to use data analytics and machine learning methods to provide useful insights and boost the efficiency of the system. For cloud-based IIoT systems to be really effective, they must integrate a wide variety of data sources and systems. However, there are challenges in putting these systems in place, the most significant of which being the need for expertise in areas such as data analytics and software development. System compatibility and user data security are two additional points of friction. Numerous approaches and solutions that have been created and deployed to deal with these problems are also discussed in this study. Edge computing, big data analytics, machine learning, virtualization, containerization, and interoperability standards are all examples of such methods and tools. When properly planned and funded, the implementation of cloud-based IIoT systems in manufacturing has the potential to completely transform the sector by delivering real-time data analytics and remote access to both data and applications.