Abstract

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Abstract

Cloud manufacturing represents a service-oriented manufacturing paradigm that allows ubiquitous and on-demand access to various customisable manufacturing services in the cloud. While a vast amount of research in cloud manufacturing has focused on high-level decision-making tasks, such as service composition and scheduling, the link between field-level manufacturing data and the cloud manufacturing platform has not been well established. Efficient data acquisition, communication, storage, query, and analysis of field-level manufacturing equipment remain a significant challenge that hinders the development of cloud manufacturing systems. Therefore, this paper investigates the implementation of the emerging Industrial Internet of Things (IIoT) technologies in a cloud manufacturing system to address this challenge. We propose a service-oriented plug-and-play (PnP) IIoT gateway solution based on a generic system architecture of IIoT-supported cloud manufacturing system. Service-oriented data schemas for manufacturing equipment are developed to capture just-enough information about field-level manufacturing equipment and allow efficient data storage and query in a cloud time-series database (TSDB). We tested the feasibility and advantages of the proposed approach via the practical implementation of the IIoT gateways on a 3D printer and a machine tool.

Introduction

Cloud manufacturing represents a service-oriented manufacturing paradigm where manufacturing resources are virtualised as manufacturing services that can be managed and configured in an intelligent and unified way in the cloud and allows ubiquitous and on-demand network access [1], [2], [3]. Cloud manufacturing aims to achieve efficient integration and sharing of distributed manufacturing resources to enable efficient on-demand production of highly customised products. As a promising trend of the future of manufacturing, ever since the introduction of the concept of cloud manufacturing in 2010 [4], various research topics related to cloud manufacturing such as architecture design, resource virtualisation, service selection and composition, service searching and matching, and task scheduling have been widely discussed and studied in both academia and industry during the last decade [5]. However, despite the vast amount of research effort in this field, one has to admit that the envisioned cloud manufacturing paradigm has not been achieved yet.