Complex, industry-wide challenges such as sustainable production require collaboration across various industry actors, such as manufacturers, automation system providers and engineering technology suppliers, as well as across their related systems. While the digital twin paradigm offers immense potential through virtual asset representations for advanced predictions and analytics, building digital twins that are shared across organization boundaries and interfacing with the existing systems is not easily achieved. In fact, lack of interoperability of asset digital twins has been recognized as a growth inhibitor as well as limiting reaching the full potential and maturity of digital twins.As a solution, we present a modular, interoperable digital twin architecture named CRADLE, short for Cross-Vendor Asset Digital Twin Architecture to enable collaborations across organizational boundaries and illustrate it via an application to paper manufacturing. The industrial interoperability standards of Asset Administration Shell (AAS), Functional Mock-Up Interface (FMI), OPC UA and AutomationML, are suitably combined for integration with multiple industrial production systems as well as different cloud platforms. Use cases of energy optimization and emissions reduction, operational planning and material flow optimization are enabled through the cross-vendor asset digital twin and illustrated in the context of paper manufacturing.

An open reference architecture is presented for sustainable papermaking based on a combined usage of open industrial interoperability standards such as the Asset Administration Shell, Functional Mockup Interface, AutomationML and OPC UA for modeling and exchanging information towards decarbonization of paper production. The models combine with containerized calculation modules and end-user services, extending the capabilities of existing production systems to support development of a scalable, modular, flexible, and interoperable framework for usage by various manufacturers. The vendor-neutral, domain-agnostic architecture can also be viewed as a blueprint for sustainable manufacturing in other industrial contexts.

For real-time analytics and insight into an industrial production process, it is necessary to deploy a hierarchy of edge nodes that collect massive amounts of telemetry data from many field devices without violating data security constraints in the process core network. Depending on the analytics use case, the requirement for the data at an edge can vary from a hot data stream, warm aggregated data, and cold historical data. Data availability can be severely impacted when communication among edge nodes and devices is interrupted due to network failures. Data backfilling, along with current data transmission with low bandwidth in the process core network is the challenge. This paper proposes a concept for data management called Data Flow as Code (DFaC), which accommodates the requirements of different analytical applications and leverages the advantages of different methods, such as data aggregation, compression, and redundancy removal, and allows configuring edges based on data fingerprints (or data characteristics) to achieve maximum benefit with respect to bandwidth and the quality of the data. Also, we propose DataTrait, which shows a modular implementation of DFaC. We demonstrate the results of extensive numerical experiments conducted with an Azure hierarchical edge setup with open-source Pronto data and Azure predictive maintenance data. Only < 12% of the actual data needs to be backfilled when the connection is restored without compromising data quality, saving huge bandwidth and processing time.

Composite asset or systems engineering requires integration and verification of the constituent asset models at the system level. It requires integrating relevant asset data and dynamical models of the individual assets which may be developed using diverse design and simulation tools by multiple vendors. Key integration challenges of lack of interoperability among corresponding data and simulation models, and separated, inflexible application deployments are addressed in this paper via application to a drive-motor-load system. A novel cloud-enabled drive-motor-load simulation platform based on the usage of Asset Administration Shells (AASs) for automatic data exchange and Functional Mockup Units (FMUs) for interoperable simulation is presented, together with an initial application of containerization for securing components. The solution enables higher quality and efficiency of engineering through simulation-based asset dimensioning, what-if scenario simulations, and simulation-based calibration and optimization as well as flexible, user-friendly, and more secure application deployment. An AAS-based model integration for linking various domain models and their parameters across design, configuration, and virtual commissioning, including application-side load FMUs via AAS Simulation Submodels at the composite system level, enables data interoperability while also serving as a building block of the future drive-motor-load system’s digital twin. The solution enables various applications across different product lifecycle phases such as pre-sales (drive) customer engagement, simulation-based calibration and design optimization during engineering, advanced system validations and testing in virtual commissioning, and continuous customer and OEM trainings during commissioning and operations.

A Metamodel-based Cyber-Physical Production System (CPPS) Integration Framework is presented for building comprehensive models of cyber-physical production systems with information integration from various software based on flexible interoperability alignments without requiring time-consuming and deadlock-prone semantic standardization efforts. The framework facilitates building of flexible, extensible networks of loosely coupled, multi-vendor production-level software, simulation, analytics, and visualization tools for holistic analyses, enabling collaborative innovation of production systems. At its core lies an AutomationML-based Information Metamodel which together with a simulation forms the production system's digital twin. The framework is illustrated through its application to mining production systems.

Since the introduction of distributed control systems in the 1970s the layout of the automation architecture has remained largely unchanged. There now is a wide gap between software used for consumer electronics and software used for industrial production processes with the former outperforming the latter by magnitudes. With the advent of Industrie 4.0 and the Industrial Internet of Things (IIoT) automation technology vendors have realized the imminent transformation and are now developing new software that may replace existing automation technology in the near future. The contribution of this article is (i) a collection of current trends that are expected to significantly change the way automation is implemented and (ii) the impact of those technological changes on control algorithms.