A Multi Agent System architecture to implement Collaborative Learning for social industrial assets

The `Industrial Internet of Things' aims to connect industrial assets with one another and subsequently bene t from the data that is generated, and shared, among these assets. In recent years, the extensive instrumentation of machines and the advancements in Information Communication Technologies are re-shaping the role of assets in our industrial systems. An emerging paradigm here is the concept of `social assets': assets that collaborate with each other in order to improve system performance. Cyber-Physical Systems (CPS) are formed by embedding the assets with computing capabilities and linking them with their cyber models. These are known as the `Digital Twins' of the assets, and form the backbone of social assets. Collaboration among assets, by allowing them to share and analyse data from other assets can make embedded computing algorithms more accurate, robust and reliable. This paper proposes a Multi Agent System (MAS) architecture for collaborative learning, and presents the fi ndings of an implementation of this architecture for a prognostics problem. Collaboration among assets is performed by calculating inter-asset similarity during operating condition to identify `friends' and sharing operational data within these clusters of friends. The architecture described in this paper also presents a generic model for the Digital Twins of assets. Prognostics is demonstrated for the C-MAPSS turbofan engine degradation simulated data-set (Saxena and Goebel (2008))

Local Digital Twin-based control of a cobot-assisted assembly cell based on Dispatching Rules

In the context of an increasing digitalization of production processes, Digital Twins (DT) are emerging as new simulation paradigm for manufacturing, which leads to potential advances in the production planning and control of production systems. In particular, DT can support production control activities thanks to the bidirectional connection in near real-time with the modeled system. Research on DT for production planning and control of automated systems is already ongoing, but manual and semi-manual systems did not receive the same attention. In this paper, a novel framework focused on a local DT is proposed to control a cobot-assisted assembly cell. The DT replicates the behavior of the cell, providing accurate predictions of its performances in alternative scenarios. Then, building on these predicted estimates, the controller selects, among different dispatching rules, the most appropriate one to pursue different performance objectives. This has been proven beneficial through a simulation assessment of the whole assembly line considered as testbed

Standardization Framework for Sustainability from Circular Economy 4.0

The circular economy (CE) is widely known as a way to implement and achieve sustainability, mainly due to its contribution towards the separation of biological and technical nutrients under cyclic industrial metabolism. The incorporation of the principles of the CE in the links of the value chain of the various sectors of the economy strives to ensure circularity, safety, and efficiency. The framework proposed is aligned with the goals of the 2030 Agenda for Sustainable Development regarding the orientation towards the mitigation and regeneration of the metabolic rift by considering a double perspective. Firstly, it strives to conceptualize the CE as a paradigm of sustainability. Its principles are established, and its techniques and tools are organized into two frameworks oriented towards causes (cradle to cradle) and effects (life cycle assessment), and these are structured under the three pillars of sustainability, for their projection within the proposed framework. Secondly, a framework is established to facilitate the implementation of the CE with the use of standards, which constitute the requirements, tools, and indicators to control each life cycle phase, and of key enabling technologies (KETs) that add circular value 4.0 to the socio-ecological transition

Innovation Policy Roadmapping for the Future Finnish Smart City Digital Twins : Towards Finland National Digital Twin Programme

Smart City Digital Twins (SCDTs) emerge as a transforming concept with the ability to redefine the future of cities in the fast-paced evolving landscape of urban development. This qualitative futures research explores thoroughly into the complex interaction of socio-technical dynamics in the Finnish setting, investigating the several ways SCDTs might revolutionise urban spaces and create resilience. By utilizing Innovation Policy Roadmapping (IPRM) method for the first time on SCDTs, it reveals the diverse capacities of SCDTs across domains such as urban planning, scenario developing, What-IF analysis, and public involvement through a rigorous examination of academic literature and multi-level analysis of expert interviews. The research emphasises the critical role of policymakers and sectoral actors in building an environment that allows Finnish SCDTs to survive in the face of technological improvements. Furthermore, it emphasises the convergence of SCDTs and Futures Studies approaches, giving a visionary path to adaptable and forward-thinking urban futures. The contributions of this study extend beyond the scope of Finnish SCDTs, giving inspiration for sustainable smart city transformations, potential foundational insights towards Finland National Digital Twin Programme and paving the way for the incorporation of futures studies methodologies and digital twins to mitigate uncertainties and create resilient urban futures. Longitudinal impact assessments, real-time citizen-centric foresight applications via SCDT, and the investigation of SCDTs' role in disaster mitigation and social well-being are among the identified future research directions, providing a comprehensive roadmap for leveraging SCDTs as transformative tools for building sustainable urban futures

Implementation of a holistic digital twin solution for design prototyping and virtual commissioning

Industry 4.0 has ushered in a new era of digital manufacturing and in this context, digital twins are considered as the next wave of simulation technologies. The development and commissioning of Cyber Physical Systems (CPS) is taking advantage of these technologies to improve product quality while reducing costs and time to market. However, existing practices of virtual design prototyping and commissioning require the cooperation of domain specific engineering fields. This involves considerable effort as development is mostly carried out in different departments using vendor specific simulation tools. There is still no integrated simulation environment commercially available, in which all engineering disciplines can work collaboratively. This presents a major challenge when interlinking virtual models with their physical counterparts. This paper therefore addresses these challenges by implementing a holistic and vendor agnostic digital twin solution for design prototyping and commissioning practices. The solution was tested in an industrial use case, in which the digital twin effectively prototyped cost-efficient solar assembly lines

Towards a Cyber-Physical Manufacturing Cloud through Operable Digital Twins and Virtual Production Lines

In last decade, the paradigm of Cyber-Physical Systems (CPS) has integrated industrial manufacturing systems with Cloud Computing technologies for Cloud Manufacturing. Up to 2015, there were many CPS-based manufacturing systems that collected real-time machining data to perform remote monitoring, prognostics and health management, and predictive maintenance. However, these CPS-integrated and network ready machines were not directly connected to the elements of Cloud Manufacturing and required human-in-the-loop. Addressing this gap, we introduced a new paradigm of Cyber-Physical Manufacturing Cloud (CPMC) that bridges a gap between physical machines and virtual space in 2017. CPMC virtualizes machine tools in cloud through web services for direct monitoring and operations through Internet. Fundamentally, CPMC differs with contemporary modern manufacturing paradigms. For instance, CPMC virtualizes machining tools in cloud using remote services and establish direct Internet-based communication, which is overlooked in existing Cloud Manufacturing systems. Another contemporary, namely cyber-physical production systems enable networked access to machining tools. Nevertheless, CPMC virtualizes manufacturing resources in cloud and monitor and operate them over the Internet. This dissertation defines the fundamental concepts of CPMC and expands its horizon in different aspects of cloud-based virtual manufacturing such as Digital Twins and Virtual Production Lines. Digital Twin (DT) is another evolving concept since 2002 that creates as-is replicas of machining tools in cyber space. Up to 2018, many researchers proposed state-of-the-art DTs, which only focused on monitoring production lifecycle management through simulations and data driven analytics. But they overlooked executing manufacturing processes through DTs from virtual space. This dissertation identifies that DTs can be made more productive if they engage directly in direct execution of manufacturing operations besides monitoring. Towards this novel approach, this dissertation proposes a new operable DT model of CPMC that inherits the features of direct monitoring and operations from cloud. This research envisages and opens the door for future manufacturing systems where resources are developed as cloud-based DTs for remote and distributed manufacturing. Proposed concepts and visions of DTs have spawned the following fundamental researches. This dissertation proposes a novel concept of DT based Virtual Production Lines (VPL) in CPMC in 2019. It presents a design of a service-oriented architecture of DTs that virtualizes physical manufacturing resources in CPMC. Proposed DT architecture offers a more compact and integral service-oriented virtual representations of manufacturing resources. To re-configure a VPL, one requirement is to establish DT-to-DT collaborations in manufacturing clouds, which replicates to concurrent resource-to-resource collaborations in shop floors. Satisfying the above requirements, this research designs a novel framework to easily re-configure, monitor and operate VPLs using DTs of CPMC. CPMC publishes individual web services for machining tools, which is a traditional approach in the domain of service computing. But this approach overcrowds service registry databases. This dissertation introduces a novel fundamental service publication and discovery approach in 2020, OpenDT, which publishes DTs with collections of services. Experimental results show easier discovery and remote access of DTs while re-configuring VPLs. Proposed researches in this dissertation have received numerous citations both from industry and academia, clearly proving impacts of research contributions

Digital Transformation in the Ornamental Stone Industry: Case Studies on Industry 4.0 and Digital Twins

Funding program "LISBOA-01-0247-FEDER-046083" for this R&D scholarship.The rapid evolution of Industry 4.0 technologies has ushered in a new era in manufacturing systems, with Digital Twins leading the way. These virtual replicas offer invaluable opportunities for simulating and optimizing new manufacturing processes, and their most transformative impact may lie in the creation of these digital models. This research unifies the main key concepts of four separate studies, all of which explore the application of Digital Twins in the ornamental stone industry. Industry 4.0 systems and their technologies have directly influenced the ornamental stone industry, addressing both the effects on mineral resources and energy consumption in daily operations. In addition, research and development initiatives seek to make this industry more efficient and sustainable, addressing crucial issues such as economic growth, environmental impact, and social welfare. The increasing digitization of manufacturing systems and their integration with digital models has played a key role in this process, enabling the replication of shop floor operations and the optimization of material use. The application of Digital Twins, which are virtual replicas of physical systems, has been explored in an ornamental stone manufacturing company. These digital models have demonstrated the ability to save time and resources during prototype design, as well as offering continuous diagnostics and optimization throughout production. It is important to note that the implementation of Digital Twins requires care due to technical challenges, but their adoption promises to significantly impact business value, despite the initial complexities. Managing stone cutting devices with Digital Twins presents real challenges in the ornamental stone industry, but it also paves the way for greater precision, efficiency, and cost savings. These digital models enable real-time monitoring, predictive maintenance, and virtual simulations. This study explores different approaches to connecting physical cutting machines to their respective Digital Twins, evaluating criteria such as communication speed, security, scalability, and cost. The results of this analysis provide valuable information for implementing Digital Twins in the stone cutting industry

A New Concept of Digital Twin Supporting Optimization and Resilience of Factories of the Future

In the context of Industry 4.0, a growing use is being made of simulation-based decision-support tools commonly named Digital Twins. Digital Twins are replicas of the physical manufacturing assets, providing means for the monitoring and control of individual assets. Although extensive research on Digital Twins and their applications has been carried out, the majority of existing approaches are asset specific. Little consideration is made of human factors and interdependencies between different production assets are commonly ignored. In this paper, we address those limitations and propose innovations for cognitive modeling and co-simulation which may unleash novel uses of Digital Twins in Factories of the Future. We introduce a holistic Digital Twin approach, in which the factory is not represented by a set of separated Digital Twins but by a comprehensive modeling and simulation capacity embracing the full manufacturing process including external network dependencies. Furthermore, we introduce novel approaches for integrating models of human behavior and capacities for security testing with Digital Twins and show how the holistic Digital Twin can enable new services for the optimization and resilience of Factories of the Future. To illustrate this approach, we introduce a specific use-case implemented in field of Aerospace System Manufacturing.The present work was developed under the EUREKA–ITEA3 Project CyberFactory#1 (ITEA-17032), co-funded by Project CyberFactory#1PT (ANI|P2020 40124), from FEDER Funds through NORTE2020 program and from National Funds through FCT under the project UID/EEA/00760/2019 and by the Federal Ministry of Education and Research (BMBF, Germany, funding No. 01IS18061C).info:eu-repo/semantics/publishedVersio