Abstract
Industrial Internet of Things practitioners are adopting the concept of digital twins at an accelerating pace. The features of digital twins range from simulation and analysis to real-time sensor data and system integration. Implementation examples of modeling-oriented twins are becoming commonplace in academic literature, but information management-focused twins that combine multiple systems are scarce. This study presents, analyzes, and draws recommendations from building a multi-component digital twin as an industry-university collaboration project and related smaller works. The objective of the studied project was to create a prototype implementation of an industrial digital twin for an overhead crane called “Ilmatar”, serving machine designers and maintainers in their daily tasks. Additionally, related cases focus on enhancing operation. This paper describes two tools, three frameworks, and eight proof-of-concept prototypes related to digital twin development. The experiences show that good-quality Application Programming Interfaces (APIs) are significant enablers for the development of digital twins. Hence, we recommend that traditional industrial companies start building their API portfolios. The experiences in digital twin application development led to the discovery of a novel API-based business network framework that helps organize digital twin data supply chains.
Highlights
Digital twin (DT) represents a new paradigm for the Industrial Internet of Things
Some of the results were intentionally built as part of the digital twin, while others were separate development efforts connected to the crane
This study presented, analyzed, and gave recommendations based on an industryuniversity project that developed a multi-component digital twin for an industrial overhead crane
Summary
Digital twin (DT) represents a new paradigm for the Industrial Internet of Things. DTs are linked to a real-world counterpart and leverage several technologies and other paradigms, such as simulation, artificial intelligence, and augmented reality, for optimizing the operation of the counterparts. DTs are being built at an accelerating pace in both industry and academia and the digital twin term has reached multiple expressions of recognition, such as being among the IEEE Computer Society’s Top 12 Technology Trends for 2020 [1]. The purpose and use cases of the concept have since been further described in further leading publications [4,5,6]. These concentrate on various areas of mechanical engineering, which is a trend continued by the majority of digital twin applications as shown by multiple review articles [7,8,9,10,11,12]. For a more in-depth exploration of the background of the digital twin concept, we refer to Section II in Autiosalo et al [16]
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