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.

The manufacturing industry is constantly seeking innovative solutions to optimize machining processes. However, there is a lack of efficient digital platforms that fully meet the flexibility, service composition, and affordability needs of the manufacturing industry, in particular for small and mediumsized enterprises (SMEs). This paper introduces the OptiTwin platform, a novel data-driven system designed to enhance machining process optimization for SMEs. The OptiTwin platform was developed with a focus on data acquisition, management, and analysis based on data driven models. The functionalities of the platform were validated through a drilling use case at Mondragon University's high-performance machining laboratory, demonstrating its effectiveness in real-time tool condition monitoring. The results showcase the potential of OptiTwin in optimizing machining processes and empowering SMEs with data-driven insights for enhanced productivity and quality assurance.

Abstract Smart Product–Service Systems (S-PSS) represent an innovative business model that integrates intelligent products with advanced digital capabilities and corresponding e-services. The user experience (UX) within an S-PSS is heavily influenced by the customization of services and customer empowerment. However, conventional UX analysis primarily focuses on the design stage and may not adequately respond to the evolving user needs during the usage stage and how to exploit the data surrounding the use of S-PSS. To overcome these limitations, this article introduces a practical framework for developing Adaptive User Interfaces within S-PSS. This framework integrates ontologies and Context-aware recommendation systems, with user interactions serving as the primary data source, facilitating the development of adaptive user interfaces. One of the main contributions of this work lies on the integration of various components to achieve the creation of Adaptive User Interfaces for digital services. A case study of a smart device app is presented, to demonstrate the practical implementation of the framework, with a hands-on development approach, considering technological aspects and utilizing appropriate tools. The results of the evaluation of the recommendation engine show that using a context-aware approach improves the precision of recommendations. Furthermore, pragmatic aspects of UX, such as usefulness and system efficiency, are evaluated with participants with an overall positive impact on the use of the smart device.

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.

The establishment of collaborative AI pipelines, in which multiple organizations share their data and models, is often complicated by lengthy data governance processes and legal clarifications. Data sovereignty solutions, which ensure data is being used under agreed terms and conditions, are promising to overcome these problems. However, there is limited research on their applicability in AI pipelines. In this study, we extended an existing AI pipeline at Mondragon Corporation, in which sensor data is collected and subsequently forwarded to a data quality service provider with a data sovereignty component. By systematically reflecting and generalizing our experiences during the twelve-month action research project, we formulated ten lessons learned, four benefits, and three barriers to data-sovereign AI pipelines that can inform further research and custom implementations. Our results show that a data sovereignty component can help reduce existing barriers and increase the success of collaborative data science initiatives.

Smart-product service systems are a business strategy that combines product and service into one value proposition. The user experience of digital services and the smart product can be a clear differentiator among competitors to achieve economically sustainable solutions. Hence, offering a more personalized experience is an important aspect of S-PSS. This paper aims to provide a theoretical framework for a context-aware user experience in S-PSS by providing adaptive and personalized services to the users according to their needs in a given context, by exploiting the digital capabilities of smart products and referring to the use of recommendation systems. The paper presents an application scenario using a smart-wearable as an example of a product-oriented PSS to better describe the framework and each component while stating the future 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.

Workflow management is implemented in manufacturing at many levels. The nature of processes varies at each level, hindering the use of a standard modeling or implementation solution. The creation of a flexible workflow management framework that overarches the heterogeneous business process levels is challenging. Still, one of the promises of the Industry 4.0 initiative is precisely this: to provide easy-to-use models and solutions that enable efficient execution of enterprise targets. By addressing this challenge, this article proposes a workflow execution model that integrates information and control flows of these levels while keeping their hierarchy. The overall model builds on the Business Process Model and Notation (BPMN) for modeling at the enterprise level and recipe modeling based on colored Petri net (CPN) at the production level. Models produced with both alternatives are implemented and executed in a framework supported by an Enterprise Service Bus (ESB). Loosely coupled, late-bound system elements are connected through the Arrowhead framework, which is built upon the Service-Oriented Architecture (SOA) concept. To prove its feasibility, this article presents the practical application of the model via an automotive production scenario.Note to Practitioners—The methodology detailed in this article can serve as a basis for experts who are dealing with industrial workflows. Reacting to the requirements of Industry 4.0, i.e., the virtualization, decentralization, modularity, real-time capability, and service orientation, this article provides a concept that can answer all the defined criteria. First, it adopts a new two-level approach to workflow management, which makes the understanding and control of workflows easier, enhancing transparency. Furthermore, it demonstrates how—even completely different—applications and modeling languages can be integrated into a Service-Oriented Architecture (SOA). The presented composition and the used tools are all tried and tested. Behind the solution described in this article, there is a genuine, working code wherewith the presented end-to-end workflow management can be achieved. Following the methodology detailed in this article, the readers can construct their workflow management composition. In order to report on the performance of the created solution, this article presents different measurement compositions that allow the investigation of the essential components separately, demonstrating the scalability and temporal parameters.

Immersed in the digital era and fully experiencing the changes introduced by the new industrial revolution of the so-called Industry 4.0, there are still many aspects of industrial digitization to resolve. Interoperability among devices and machines is one of the challenges. Sensors, components and machines from different vendors work as independent silos offering large amounts of heterogeneous data which relational capabilities are not fully exploited. Quick development, deployment and testing of new software solutions that take advantage of those data is another important matter. The requirements in terms of equipment resources and engineering efforts is high when planning new implementations. Platforms that enable the efficient application of those solutions at the right level (machine, edge, plant or cloud) are also necessary.(p)(/p)This paper presents an industrial case study on the application of the Arrowhead framework. The framework is implemented in the Machine Tooling ecosystem and enables the integration of grinding machines with other sensors, components or machines. Different software engineering tools offered with Arrowhead are used to design new solutions in Cyber-Physical System and Internet of Things in Industry 4.0 and make them Arrowhead compliant, for fast deployment of platforms and applications (Dockers) or for testing purposes (Management tool). Finally, the potential of agile construction of new applications is analysed by providing an Human-Machine Interface at machine level and the provision of services for data consumption at cloud level.

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.

The heterogeneity of energy ontologies hinders the interoperability between ontology-based energy management applications to perform a large-scale energy management. Thus, there is the need for a global ontology that provides common vocabularies to represent the energy subdomains. A global energy ontology must provide a balance of reusability–usability to moderate the effort required to reuse it in different applications. This paper presents DABGEO: a reusable and usable global ontology for the energy domain that provides a common representation of energy domains represented by existing energy ontologies. DABGEO can be reused by ontology engineers to develop ontologies for specific energy management applications. In contrast to previous global energy ontologies, it follows a layered structure to provide a balance of reusability–usability. In this work, we provide an overview of the structure of DABGEO and we explain how to reuse it in a particular application case. In addition, the paper includes an evaluation of DABGEO to demonstrate that it provides a balance of reusability–usability.

Workflow management is realised in manufacturing at the Enterprise- and Production (workstation) levels. The characteristics of business processes in these levels differ enough to prevent the adoption of a conventional modelling or implementation solution. The conceptualisation of an adequate and adaptable workflow management model that over-arches the heterogeneous business process levels is challenging. However, one of the encouragements of Industry 4.0 is to provide easy-to-use models and solutions that enable the effective implementation of production goals.The current paper addresses this challenge and - as a proof-of-concept - it demonstrates how the goals mentioned above can be achieved by combining the different manufacturing process models. To accomplish this, a workflow control engine needs to be designed and standardised respectively. Arrowhead is an IIoT (Industrial IoT) framework that dynamically and flexibly supports automated manufacturing processes following Industry 4.0 expectations. This paper describes how its workflow management system, i.e., the Workflow Choreographer implements automated production. Furthermore, it details what the functions of this system are and how it applies and combines BPMN and CPN in practice to provide a solution for the given challenge.To verify its feasibility, the paper showcases a demo application of the concept as well.

Global ontologies must provide a balance of reusabilityusability to minimize the ontology reuse effort in different applications.To achieve this balance, ontology design methods focus on designing layered ontologies that classify into abstraction layers the common domain knowledge (reused by most applications) and the variant domain knowledge (reused by specific application types).This classification is performed from scratch by domain experts and ontology engineers.Hence, the design of reusable and usable ontologies that represent complex domains takes a lot of effort.Considering how common and variant software features are classified when designing Software Product Lines (SPLs), we argue that SPL engineering techniques can facilitate the domain knowledge classification taking as reference existing ontologies.In this paper, we show the experiences of applying SPL and ontology design techniques in combination to design a reusable and usable global ontology for the energy domain.Domain experts and ontology engineers evaluated the proposed method.The results show that SPL engineering techniques enable a systematic and accurate domain knowledge classification, thus saving ontology design effort.

This paper presents the implementation of a reference architecture for Cyber Physical Systems (CPS) to support Condition Based Maintenance (CBM) of industrial assets. The article focuses on describing how the MANTIS Reference Architecture is implemented to support predictive maintenance of clutch-brake assets fleet, and includes the data analysis techniques and algorithms implemented at platform level to facilitate predictive maintenance activities. These technologies are (1) Root Cause Analysis powered by Attribute Oriented Induction Clustering and (2) Remaining Useful Life powered by Time Series Forecasting. The work has been conducted in a real use case within the EU project MANTIS.

With buildings accounting for nearly 40 percent of global energy consumption, the improvement of energy efficiency in buildings and districts is a clear opportunity in the fight against climate change. This has led engineering practitioners as well as researchers to propose solutions for smart energy control in buildings. Simulation-based methods permits the early validation of engineering solutions for these smart energy control systems. However, since these solutions involve different engineering disciplines (e.g., software engineering, electrical engineering, etc.), different simulation tools might be employed. We propose a tool that interconnects EnergyPlus, one of the leading open-source tools for building energy simulation, with Crescendo, a tool for designing and modelling cyber-physical systems using formal methods. A preliminary evaluation suggests that the proposed solution enables the simulation between these two tools in an efficient manner.

Recent studies estimate that a high percentage of the entire software process cycle corresponds to its maintenance. There is a need to reduce the factors which trigger high maintenance costs due to software updates. This article presents a software engineering process to develop services that reduces maintenance costs. The process has been applied in the framework of the Arrowhead project and consists on refactoring parts of software developed in previous iterations and constructing new pieces according to a methodology. The methodology focuses on reducing software maintenance cost by improving its architectural design, facilitating code comprehension and code reusability, guaranteeing availability, identifying errors in the early stages of its development, and assuring requirement fulfilment before integrating the whole system. The article also analyses the benefits provided by each software engineering technique applied during the software engineering process.