A digital twin is a virtual model of a process, product, or service, which is one of the key technologies in the fourth industry. The pairing of the virtual and physical world allows analysis of data and monitoring of systems to head off problems before they occur. This paper presents a digital twin architecture and a system based on an interoperable data model. It explains how to build a digital twin for the integrated control monitoring using edge devices, data analytics, and realistic 3D visualization. The system allows continuous collaboration between field engineers for data gathering, designers for modeling 3D models, and layout engineers for layout changing by generating 3D digital twin models automatically. The system helps stakeholders focus on their respective roles to build digital twins. Examples applied to the Korean automotive parts makers are also introduced in this paper. The system can be easily used by small and medium-sized enterprises (SMEs) as well as large companies. Beyond simply watching the production site with CCTV, the production site can be intuitively managed based on the digital twin.

Purpose: In this paper, a three-dimensional (3D) printer-based manufacturing line and supporting system, which supports personalized/customized manufacturing for individual businesses or start-up companies, was studied to evaluate the practicality of using additive manufacturing for personalization/mass customization. Design/methodology/approach: First, factory-as-a-service (FaaS) system, which provides factory as a service to customers, was proposed and designed to manufacture various products within a distributed manufacturing environment. This system includes 3D printer-based material extrusion processes, vapor machine/computer numerical control machines as post-processes and assembly and inspection processes with an automated material handling robot in the factory. Second, a virtualization module for the FaaS factory was developed using a simulation model interfaced with a cloud-based order and production-planning system and an internet-of-things-based control and monitoring system. This is part of the system for manufacturing operations, which is capable of dynamic scheduling in a distributed manufacturing environment. In addition, simulation-based virtual production was conducted to verify and evaluate the FaaS factory for the target production scenario. Main information of the simulation also has been identified and included in the virtualization module. Finally, the established system was applied in a sample production scenario to evaluate its practicality and efficiency. Findings: Additive manufacturing is a reliable, feasible and applicable technology, and it can be a core element in smart manufacturing and the realization of personalization/mass customization. Originality/value: Various studies on additive manufacturing have been conducted with regard to replacing the existing manufacturing methods or integrating with them, but these studies mostly focused on materials or types of additive manufacturing, with few advanced or applied studies on the establishment of a new manufacturing environment for personalization/mass customization.

Specialised knowledge on analytics algorithms is necessary to conduct a data analysis; thus, there are limits to how a manufacturing manager who is not a data analysis expert conducts analysis tasks. This study proposed a manufacturing-specialised data analytics algorithm template and a cloud-based big data analytics platform to resolve such a problem and to enable those not specialising in data analysis to perform analysis tasks. The manufacturing-specialised data analytics algorithm template is a concept in which data characteristics are considered according to manufacturing problems to systematically provide the pre-processing, data mining, and visualisation algorithms required for conducting analysis tasks. A cloud-based big data analytics platform is designed to use algorithm templates for analysis and is capable of not only storing and managing data per company but also creating analysis models and confirming results. The platform consists of the following: inter-connected middleware, data storage, a batch manager, a data analytics modeller, and an analysis engine. This study applied the proposed manufacturing-specialised data analytics algorithm template and cloud-based big data analytics platform to the machine problem-solving process executed by mobile phone component production companies. It also sought study limitations and offered development plans for future research.