Abstract
Industry 4.0 (I4.0) is the fourth industrial revolution and a synonym for intelligent manufacturing. It drives the convergence of several cutting-edge technologies to provoke autonomous, fully integrated, collaborated, highly automated, and customized industries. Edge Computing (EC), a highly distributed framework, emerged a couple of years ago and embraced the industry to leverage the benefit of low latency and near real-time performance. It brings computation and storage in the close proximity of end devices and reduces the cloud overhead. In addition to improved operational efficiency, storage, and latency, EC further reduces the cost, improves productivity with higher quality maintenance and customer satisfaction. At the digital-to-digital stage of the Physical-Digital-Physical (PDP) loop, adapting EC can furnish tremendous benefits and further accelerate the next stages of the loop. This survey identifies the past and present works oriented towards Intelligent Manufacturing integrated with the EC platform and categorizes the research based on architecture, intelligence platform, edge objectives, and application. Herein, the authors have incorporated; (1) The progress in I4.0 following the PDP loop; (2) The discussion on EC in I4.0 and their Research Trend; (3) Methods to bring intelligence to the edge. To the best of our knowledge, it is the first review article that focuses on the applications and objectives of EC in Intelligent Manufacturing. It also outlines the optimum solutions to bring intelligence to the edge by overcoming the resource and complexity-bound with accuracy and latency constraints for the decision-making processes. Future directions include the less explored research areas, challenges in edge deployment in industries, and the integration of trending technologies such as Blockchain, Software Defined Networking, and 5 G with EC to excite the EC researchers. A few collaborative edge scenarios are discussed for the promotion and application of EC in I4.0. Nevertheless, efficient edge deployments face many challenges since studies are still limited to conceptual levels or design steps, and future orientation to application strategies for Smart Manufacturing is required.
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