Over the last two decades, semantic ontologies have been developed to represent manufacturing data across various domains. These ontologies constitute the knowledge base of manufacturing management systems, which primarily focus on optimizing the manufacturing process and improving its resilience. The ontologies developed in the Industry 4.0 domain are heterogeneous, hindering the interoperability of machines, devices, and applications composing manufacturing systems. Consequently, a demand arises for an ontology that provides common vocabularies to represent the data domains inherent to Industry 4.0. A global Industry 4.0 ontology must be easily reusable in different application contexts. This paper presents I40GO: a global ontology tailored to the Industry 4.0 domain. I40GO structures in layers and modules the knowledge represented in the Industry 4.0 most relevant ontologies. The MODDALS methodology is followed to classify knowledge into different layers. This methodology classifies ontology knowledge into common, variant, and application-specific layers following a similar approach to that of Software Product Lines (SPL). I40GO assists ontology engineers in developing domain-specific ontologies for manufacturing systems and enhances interoperability among applications. This work provides an overview of I40GO, emphasizing its development methodology and its modular and layered structure. Furthermore, it demonstrates the reuse of the I40GO ontology within an Industry 4.0 use case—an architecture for context-aware workflow management.

Smart Manufacturing Systems (SMS) are software systems that identify opportunities for automating manufacturing operations by using Internet of Things (IoT) devices and services connected to machines. An active challenge of SMS is to satisfy the ever-changing conditions of industries, supply networks, and customer needs. To operate effectively, SMS should be flexible enough to perform automatic or semi-automatic adjustments to manufacturing processes in response to unexpected changes, a feature called context awareness. Recent advances in interpreting context data in the semantic web have permitted SMS to understand the active situation of manufacturing processes. This paper presents a literature analysis of context-aware workflow management approaches in the smart manufacturing domain, with a particular focus on semantic web-based approaches published from 2015 to 2022. A Systematic Literature Review (SLR) methodology was applied to analyze the state-of-the-art via the PICOC method. The contributions of this work are (1) an SLR about context-aware workflow management for smart manufacturing systems focusing on semantic web-based approaches, (2) a systematic taxonomy to break down the approaches in conformity based on content and main workflow management function area, and (3) identification of opportunities for improvement in technical features such as context awareness, use case implementation, tools employed, licensing, security, and scalability. A novel architecture and components are also proposed to address the identified active challenges.

This paper presents an industrial scenario that simulates a Manufacturing as a Service system for the execution of remote production orders built upon the implementation of emerging Asset Administration Shell (AAS) capabilities and International Data Space connectors. Static and dynamic information from industrial assets (presses and laser cutting machines) are modelled with new AAS submodels and the result is stored in an AAS manager/registration system. A manufacturing orchestrator discovers assets through the registry and completes production orders. The AAS registry allows the selection of assets with capabilities to perform tasks and also shares the AAS catalogue available in the system. The catalogue is shared with external parties through Data Space Connectors. Third party companies can launch manufacturing orders remotely using the same connectors. The paper validates the implementation of AAS components and IDS connectors in a manufacturing context where remote production orders can be securely activated.

Microservice Architectures have increasingly become popular in Industry 4.0 as they allow heterogeneous systems to interact, reduce the complexity in the management of individual components, and support distributed deployments. The integration of those distributed services into orchestrated production processes is performed by workflow managers. Next generation workflow managers must overcome a number of challenges when operating in microservice architectures and IoT environments. To overcome these challenges (heterogeneity, high dynamism, edge deployment or scalability), we propose a workflow manager alternative built in Node-RED. Node-RED provides instruments for the development of IoT systems and leverages the edge computing paradigm. This solution is deployable in embedded systems, is able to load and execute business processes by means of BPMN recipes and enables the integration of other frameworks and architectures.

The new paradigm of the Industry 4.0 centers on the digitalization of assets to realize a new industrial revolution. Standardization and interoperability are key for the successful implementation of this digitalization strategy. Among the different standardization and interoperability initiatives, Asset Administration Shell (AAS) proposes a standardized electronic representation of industrial assets enabling Digital Twins and interoperability between automated industrial systems and Cyber Physical System (CPS). In this context, Mondragon Corporation has launched several initiatives to boost the digitalization of its industries. Although implementation of the AAS in real industrial scenarios is not widespread, Mondragon Corporation has identified this initiative as a key enabler for manufacturing companies within its group. This paper presents a case study on the application of the AAS in an industrial context. The AAS initiative is implemented through integrating a Machine Tooling ecosystem with a robotic arm. This implementation facilitates the discovery and integration of grinding machines with other components or machines in a production plant, validating the AAS in a manufacturing scenario.