Possible Applications of Edge Computing in the Manufacturing Industry—Systematic Literature Review

Abstract

This article presents the results of research with the main goal of identifying possible applications of edge computing (EC) in industry. This study used the methodology of systematic literature review and text mining analysis. The main findings showed that the primary goal of EC is to reduce the time required to transfer large amounts of data. With the ability to analyze data at the edge, it is possible to obtain immediate feedback and use it in the decision-making process. However, the implementation of EC requires investments not only in infrastructure, but also in the development of employee knowledge related to modern computing methods based on artificial intelligence. As the results of the analyses showed, great importance is also attached to energy consumption, both in ongoing production processes and for the purposes of data transmission and analysis. This paper also highlights problems related to quality management. Based on the analyses, we indicate further research directions for the application of edge computing and associated technologies that are required in the area of intelligent resource scheduling (for flexible production systems and autonomous systems), anomaly detection and resulting decision making, data analysis and transfer, knowledge management (for smart designing), and simulations (for autonomous systems).

The Future of Electronic Commerce in the IoT Environment

“…Edge computing is performed close to the data source or "edge" of the network, typically on devices such as smart sensors and monitoring systems [7]. This computing is ideal for low-latency and real-time data processing [8].…”

Digital Thread Roadmap for Manufacturing and Health Monitoring the Life Cycle of Composite Aerospace Components

“…This requires not just strong computing power, but strong local computing power. The use of edge devices powerful enough to run machine learning models is relatively recent [22], and because these innovations are a result of rapidly improving computational power in smaller edge devices, the aerospace industry is still working to identify all potential use cases for digital twins, process control, and more [23]. In addition to higher first-time-right quality, the use of edge devices in process control could lead to maintaining a minimal material consumption [24].…”

“…The direct process steps within the design, manufacture, test, and life cycle monitoring of a composite component are all necessary to create a digital thread, but there are key supporting elements that are critical as well [55]. These include the ability to: capture data at each step using sensors and in-process metrics; utilize edge and IoT elements to capture data on location [56], and this can also allow for not just capture but analysis [23]; create a data mapping process and the architecture that allows for effective remote/cloud access potentially years after its initial capture [57]; effectively track an entire production line [58], optimize processes for both humans and robots [59]; and utilize AI to provide analysis, insight, and recommended action [22].…”

IoT-Based Data Mining Framework for Stability Assessment of the Laser-Directed Energy Deposition Process

“…Edge devices are specialized computing units positioned at the edge of a network, meaning close to the processes and machines from which data is generated. The incoming data is immediately processed and pre-defined data points are subsequently transferred to a cloud server [28]. In comparison to a direct connection between the data-producing entity and the cloud, applying edge devices has been shown to improve latency, reliability, and computational load management in Industry 4.0 applications [27].…”

“…In comparison to a direct connection between the data-producing entity and the cloud, applying edge devices has been shown to improve latency, reliability, and computational load management in Industry 4.0 applications [27]. Moreover, edge devices can also ensure a secure and private data transfer that is in line with Industry 4.0 requirements [28].…”