Tsvetelina Ivanova, L Koleva, Idilia Batchkova, G Kolev
Abstract Reliable vacuum control in high-precision installations such as the Electron Beam Melting and Refining (EBMR) plant requires an intelligent architecture that integrates physical subsystems and cyber entities under an adaptive control framework. This paper proposes a multi-agent system (MAS) representation of the EBMR vacuum creation subsystem, developed using the Organizational Multi-Agent Systems Engineering (O-MaSE) methodology. The model unites the IEC 61512 (S88) batch-process standard with the IEC 61499 distributed-control architecture to form a modular and interoperable cyber-physical system (CPS). Each pump, valve, and sensor is modelled as an autonomous agent with defined goals, roles, and communication protocols. The O-MaSE-based design enhances scalability, fault tolerance, and system adaptability, enabling decentralized decision-making and efficient vacuum regulation. The integrated case study demonstrates that MAS-based CPS design substantially improves the responsiveness and resilience of EBMR operations, supporting the principles of Industry 4.0.
This study advances toward establishing the theoretical foundations of Industry 6.0 by developing a comprehensive framework that integrates artificial intelligence (AI), decentralized control systems, and cyberâphysical production environments for intelligent, sustainable, and adaptive manufacturing. The research employs a tri-modal methodology (deductive, inductive, and abductive reasoning) to construct a theoretical architecture grounded in five interdependent constructs: advanced technology integration, decentralized organizational structures, mass customization and sustainability strategies, cultural transformation, and innovation enhancement. Unlike prior conceptualizations of Industry 6.0, the proposed framework explicitly emphasizes the cyclical feedback between innovation and organizational design, as well as the role of cultural transformation as a binding element across technological, organizational, and strategic domains. The resulting framework demonstrates that AI-driven decentralized control systems constitute the cornerstone of Industry 6.0, enabling autonomous real-time decision-making, predictive zero-defect manufacturing, and strategic organizational agility through distributed intelligent control architectures. This work contributes foundational theory and actionable guidance for transitioning from centralized control paradigms to AI-driven distributed intelligent manufacturing control systems, establishing a conceptual foundation for the emerging Industry 6.0 paradigm.
Mariia Deinega Mariia Deinega, Theodoros Dounas, Daniel Hall Daniel Hall, Hico McDonald Hico McDonald · 6 authors
Decentralized project delivery in architecture faces challenges related to transparency, accountability, and role definition. This paper explores the application of Soulbound Tokens (SBTs) as a governance and record-keeping mechanism within decentralized autonomous organizations (DAOs). Using a systematic review of practice, the paper identifies five opportunities for SBTs, proposes an operating framework for SBTs with respect to record-keeping (e.g., skills, contributions) and project governance (e.g, voting power, reputation), and describes one case of technical implementation of SBTs. Future research can improve on this technical implementation or develop additional decentralised applications for SBT skills verification and governance mechanisms.
The evolving manufacturing landscape, characterized by increasing demands for customization, flexibility, and efficiency, requires adaptive systems that can respond in an autonomous and dynamic way. Bio-inspired Adaptive Manufacturing Systems, or Bio-AMS, provide a novel answer by integrating principles from nature that include self-organization, resilience, and adaptability. This paper covers the development and application of Bio-AMS, in which biological inspiration from organisms and ecosystems was applied to create systems that would adapt to varying production demands, disturbances, and resource constraints. In this manner, Bio-AMS enhances both the efficiency and scalability of systems with decentralized control, adaptive feedback, and MAS architectures. Simulations show that Bio-AMS outperforms conventional systems in terms of minimizing downtime and maximizing productivity. Case studies demonstrate their capability for autonomous adaptation to disruption and therefore operational resilience. Future work focuses on refining the algorithms, integrating advanced technologies like AI and IoT, and taking the applications to various industries.
Ranjit Kannappan, Julien Hatin, E. Bertin, Noël Crespi
The Digital Product Passport (DPP) is a key enabler of the European Unionâs vision for a circular economy. Achieving the full potential of DPP requires addressing the challenges of traditional product lifecycle systems (PLM). Traditional PLM focuses on streamlining data management and decision making. However, their centralized architecture limits transparent, crossorganizational collaboration, impacting the circular economy efforts. This paper proposes a blockchain based framework, tailored to support DPP implementation by enabling the creation and sharing of lifecycle data using digital twin technology. The proposed architecture implements two types of digital twins - Component Digital Twin and Product Digital Twin modeled using the Asset Administration Shell (AAS) standard to ensure interoperability. The architecture leverages Ethereum smart contracts for blockchain interaction and IPFS for off-chain decentralized storage. Two approaches for secure data sharing are implemented: Direct and Signature-based data sharing. Performance evaluation shows low latency for key operations like twin creation (167 ms) and data sharing (64 ms). By leveraging decentralization in DPPs, the proposed framework fosters collaboration, transparency, and circular economy practices, empowering stakeholders to access and share critical product data throughout the lifecycle.
Against the backdrop of the Industry 5.0 transformation, traditional centralized Manufacturing Execution Systems (MES) struggle to meet the complex demands of flexible and small-batch production. This paper proposes a four-level distributed architecture oriented toward human-centric manufacturing, encompassing Manufacturing Units (UMS), Production Lines (PL-MES), Workshops (MOM), and Factories (MOM), supporting dynamic reconfiguration and resource optimization from the unit level to cross-factory operations. Building on this, the study designs a smart factory operating system architecture based on âcloud-edge-endâ collaboration, achieving vertical integration of equipment, systems, and business processes through an industrial internet platform. Leveraging intelligent connectivity gateways and modular microservices, the architecture constructs a decentralized decision-making chain. Innovatively integrating multi-agent systems and federated learning, the architecture significantly enhances the autonomous decision-making capabilities of manufacturing units and cross-level collaboration efficiency. Additionally, the system supports rapid deployment for small and medium-sized enterprises through low-code toolchains, lowering technical barriers. This research provides a theoretical framework and technical pathway for human-machine collaborative manufacturing in the Industry 5.0 era, driving the paradigm shift of manufacturing systems from rigid control to ecological self-organization, laying a solid foundation for the sustainable development of future smart factories
Lean Six Sigma 4.0 (LSS 4.0) represents a transformative evolution of Lean Six Sigma, integrating Industry 4.0 technologies to drive smart manufacturing excellence. By leveraging Artificial Intelligence (AI), the Internet of Things (IoT), Digital Twins, and Big Data Analytics, LSS 4.0 enables realtime decision-making, predictive intelligence, and autonomous process optimization, enhancing efficiency, agility, and resilience in modern industrial environments. This paper introduces a conceptual framework for LSS 4.0, redefining the DMAIC (Define-Measure-Analyze-Improve-Control) methodology through IoT-driven process monitoring, AI-powered predictive analytics, and digital twin simulations. This transformation shifts manufacturing from reactive control to predictive and autonomous optimization, reducing variability, defects, and waste while maximizing productivity, resource efficiency, and sustainability. By leveraging data-driven decision-making, intelligent automation, and predictive maintenance, the framework enhances process reliability, prevents defects, and improves operational performance. Despite its advantages, LSS 4.0 presents challenges, including technological complexity, workforce upskilling, and organizational resistance. This study underscores the critical role of leadership-driven digital transformation, AI-augmented decision-making, and targeted skill development in fostering an innovation-driven manufacturing culture. Additionally, blockchain for secure supply chain traceability, augmented reality (AR) for enhanced humanmachine collaboration, and edge computing for decentralized intelligence are explored as key enablers of LSS 4.0âs full potential. Leadership commitment, cross-functional collaboration, and AI-driven Lean workflows are identified as essential success factors. Aligning digital transformation strategies with Lean principles and fostering a culture of continuous innovation is crucial for realizing LSS 4.0âs full benefits. Finally, this study highlights future research directions, emphasizing Industry 5.0 advancements such as human-centric automation, collaborative robotics, and sustainable smart manufacturingâkey drivers in building adaptive, intelligent, and resilient industrial ecosystems.
Larissa KrÀmer, Patrick Stuckmann-Blumenstein, Pascal Kaiser, Michael Henke · 6 authors
Enhancing transparency in production processes, especially in shared manufacturing, relies heavily on sharing data. Information asymmetries and coordination problems between parties with conflicting interests pose a challenge in this multi-stakeholder interaction. Blockchain technology with smart contracting can be a solution due to its immutable data and decentralised data storage features. Designing and executing blockchain in industrial applications is a highly intricate task that requires extensive testing, expertise, and proficiency. This paper is the first to propose a holistic simulation model for evaluating the impact of smart contracting on shared manufacturing, including a novel approach to simulated smart contracting in time-lapse for Ethereum-based networks. The introduced model guides the design and implementation process of blockchain applications in shared manufacturing to address this challenge. A systematic literature review establishes ten design process requirements and ten smart contract functions. The implementation is developed based on the design benchmarks of three Ethereum-based frameworks to investigate the simulation model's respective feasibility and scalability. The simulation model validation demonstrates our approach's suitability for simulating smart contracting in shared manufacturing within a hybrid production. It enables fast and scalable simulations, offering an innovative approach to extensively testing blockchain applications before their introduction to ongoing industrial operations.
Do Hai Son, Nguyen Danh Hao, Tran Thi Thuy Quynh, Le Quang Minh
Decentralized applications (DApps) have gained prominence with the advent of blockchain technology, particularly Ethereum, providing trust, transparency, and traceability. However, challenges such as rising transaction costs and block confirmation delays hinder their widespread adoption. In this paper, we present our DApp named W2E - Workout to Earn, a mobile DApp incentivizing exercise through tokens and NFT awards. This application leverages the well-known ERC-20 and ERC-721 token standards of Ethereum. Additionally, we deploy W2E into various Ethereum-based networks, including Ethereum testnets, Layer 2 networks, and private networks, to survey gas efficiency and execution time. Our findings highlight the importance of network selection for DApp deployment, offering insights for developers and businesses seeking efficient blockchain solutions. This is because our experimental results are not only specific for W2E but also for other ERC-20 and ERC-721-based DApps.
The globalization of products and markets increases the distance between the origin of products and consumers. This leads to a condition where customers don't have information about the origins of their products. Thus, traceability has become an essential sub-system of manufacturing supply chain management. However, due to globalization and complexity of supply chain interactions among the suppliers and manufacturing enterprises, it is hard to pinpoint the exact contributions of different actors in a supply chain. Integrated supply network structure with suitable visibility and usage of real time data transfer is another area of great importance. This paper focuses on how NFT (Non-Fungible Token) coupled with smart contracts could utilize blockchain to make it easier to track the products in a supply chain. Explaining how NFT's could help in tracking the contributions of different stakeholders in a supply chain by tracking the product throughout the entire process of sourcing, production, and sale by using a digital twin. In a manufacturing supply chain enabled by NFT Technology, whenever raw materials are transferred and processed through the supply chain, an NFT would be attached to its digital twin which will capture the created values. Each NFT can easily and uniquely be known by its data stored. Data would be updated based on real time information and will enable the stakeholders to trace the product information about how much each company has contributed to the produced products. The data stored in the form of a smart contract in the blockchain prevents the data being entered from being destroyed, eliminated, or changed without permission. Thus, there is a secure data flow among different stakeholders.
Today's manufacturing companies have begun to increasingly use digital tools to increase their company production efficiency, to ensure a low-price level, high quality, and fast delivery time of the product or service in the conditions of increasing competition in the globalized economy. An important part of improving the company's efficiency indicators is the ever-more relevant organization of transport operations on the production floor and the digitization and automation of these processes. More and more companies have adopted or plan to do so in the near future with autonomous mobile robots (AMR) to manage production logistics. The rapid development of the Internet of Things (IoT) and the advanced hardware and control software of AMR enable autonomous operations in dynamic environments, which gives them the ability to communicate and negotiate independently with other resources, such as machines and systems, and thus decentralize decision-making in production processes. Decentralized decision-making allows the system to dynamically respond to changes in system state and environment. Such developments have affected traditional planning and control methods and decision-making processes, but also place greater demands on the software used and integrated Artificial Intelligence (AI) algorithms for the execution of these decisions. In this study, we provide an overview of how to pilot the integration of an AMR system with AI functionality in the production logistics of the food industry using the concept of a 3D virtual factory. The paper proposes an approach for the performance analysis of AMR for the transportation of goods on the production factory floor, which is based on 3D layout creation and simulation, monitoring of key performance indicators (KPIs), and integration of AI for proactive decision-making in production planning. The relevance and feasibility of the proposed approach are demonstrated by a food industry case study.
Nikolai Kazantsev, Oleksii Petrovskyi, Julian M. MĂŒller
Rapid market changes call for demand-driven collaborations in manufacturing, which trigger supply chain evolution to more distributed supply structures. This paper explores the system dynamics of the largest European aerospace manufacturer's supply chain. We conceptualise a manufacturing ecosystem by observing the impacts of supplier development, digital platforms, smart contracting, and Industry 4.0 on demand-driven collaborations in time. We contribute to the literature on ecosystem strategy, particularly for regulated industries, by disclosing the role of demand-driven collaborations in supporting the ecosystems' growth. We provide manufacturing firms with an open-access tool to exemplify their ecosystem development and produce initial training datasets for AI/ML algorithms, supporting further analytics.
This paper proposes a framework to automate the generation of traceable and protected documentation of complex assembly processes. The final assembly in aviation, automotive, and appliances industries is a rigorous process that has limited capabilities of full traceability associated with: (1) the parts installed, (2) their fabrication processes, and (3) the assembly work. This is also the case for each of its sub-assemblies. The thousands of parts forming a hierarchy of sub-assemblies that are dynamically accumulated to compose the final assembly make full traceability a challenging feat that is almost unsurmountable. Such full traceability along the entire supply chain requires considerable cost and effort since it must be based on documentation of most assembled parts, assembly tasks, and inspection tasks that compose the full assembled product. In addition, security measures are needed to prevent hostile hacking and unauthorized approach to the assembly documentation throughout the entire supply chain. The related documentation and repeated verifications require considerable effort and have many chances for human errors. So, automating these processes has great value. This article expounds a framework that harnesses blockchain and smart-contract technology to offer automated traceable and protected documentation of the assembly process. For this purpose, we expand the concept of a Bill-Of-Assembly (BOA) to incorporate data from the bill of materials (BOM), the associated assembly activities, the associated activitiesâ specification parameters and materials, and the associated assembly resources (machines and/or operators). The paper defines the operation of the BOA with blockchain and smart-contract technology, for attaining full traceability, safety, and security, for the entire assembled product. Future research could extend the proposed approach to facilitate the usage of the BOA data structure in constructing a digital twin of the entire simulated system.
Spyridon Georg Koustas, Max Jalowski, Tobias Reichenstein, Sascha Julian Oks
As manufacturing firms increasingly utilize IIoT technologies and sensor data is integrated into their processes, centralized architectures and the expansion of value creation networks pose challenges regarding data integrity and transparency. This offers an application context for the blockchain and smart contract technologies. Through a design science research approach a prototypical instantiation in form of a private Ethereum blockchain with RFID sensors as data inputs and ERC-721 tokens is created. The artifact is implemented into an industrial demonstrator, where the immutable tracking of workpieces during their production processes and the algorithm execution time are evaluated in trial runs.
In cyberâphysicalâsocial systems, smart manufacturing has to overcome challenges, such as uncertainty, diversity, complexity in modeling, long-delayed responses to market changes, and human engineer dependency. DeFACT is a framework of parallel manufacturing in ManuVerse where the Decentralized Autonomous Organization-based interactions between parallel workers consisting of robotic, digital, and human workers are elaborated to transform from professional division to real-virtual division. In DeFACT, human workers are only responsible for 5% physical and mental work that is complex and creative, and the robotic and digital workers can take care of the rest. The perceptual and cognitive intelligence of digital workers are intensified by a manufacturing foundation model (MF-PC), where calibration and certification (C&C), and verification and validation (V&V) guarantee not only the accuracy of task models, but also the interpretability and controllability of feature learning. As a case study, the workflow of customized shoes of SANBODY Technology Company is illustrated to show how DeFACT breaks the time and space constraints, avoids production waste caused by aesthetic discrepancies with consumers, and truly realizes flexible manufacturing.
During the past decades, the global manufacturing industries have been reshaped by the rapid development of advanced technologies, such as cyber-physical systems, Internet of Things, artificial intelligence (AI), machine learning, cloud/edge computing, smart sensing, advanced robotics, blockchain/distributed ledger technology, etc [...]
The final assembly of appliances and automotive industries is a rigorous process, that has limited capabilities of full traceability associated with: (1) the parts installed, (2) their fabrication processes, and (3) the assembly work. This is also the case for each of its sub-assemblies The many parts and sub-assemblies that compose the final assembly, make full traceability a challenging fit, that is almost unsurmountable. Such full traceability along the entire supply-chain is not existent today and must be based on documentation of most assembled parts, assembly tasks, and inspection tasks that compose the full assembled product. In addition, security measures are needed to prevent hostile hacking and unauthorized approach to the assembly documentation throughout the entire supply chain. The related documentation and repeated verifications require considerable effort and have many chances for human errors. So, automating these processes has a great value. This article expounds a framework that harnesses block-chain and smart-contract technology to offer traceable and protected documentation of the assembly process. We expand the concept of a Bill-Of-Assembly (BOA) to incorporate data from the bill of materials (BOM), the associated assembly activities, the associated activities’ specification parameters and materials, and the associated assembly resources (machines and/or operators). The paper defines the operation of the BOA with blockchain and smart-contract technology, for attaining full traceability, safety, and security, for the entire assembled product. Future research could extend the proposed approach to facilitate the usage of the BOA data structure in constructing a digital twin of the entire simulated system.
Briefing: To tackle the complexity of human and social factors in manufacturing systems, parallel manufacturing for industrial metaverses is proposed as a new paradigm in smart manufacturing for effective and efficient operations of those systems, where Cyber-Physical-Social Systems (CPSSs) and the Internet of Minds (IoM) are regarded as its infrastructures and the âArtificial systemsâ, âComputational experimentsâ and âParallel executionâ (ACP) method is its methodological foundation for parallel evolution, closed-loop feedback, and collaborative optimization. In parallel manufacturing, social demands are analyzed and extracted from social intelligence for product R&D and production planning, and digital workers and robotic workers perform the majority of the physical and mental work instead of human workers, contributing to the realization of low-cost, high-efficiency and zero-inventory manufacturing. A variety of advanced technologies such as Knowledge Automation (KA), blockchain, crowdsourcing and Decentralized Autonomous Organizations (DAOs) provide powerful support for the construction of parallel manufacturing, which holds the promise of breaking the constraints of resource and capacity, and the limitations of time and space. Finally, the effectiveness of parallel manufacturing is verified by taking the workflow of customized shoes as a case, especially the unmanned production line named FlexVega.
B Kohl, M. KrĂŒger, Tobias Dietl, Michael Lechner · 8 authors
Abstract Digitalization in the metal forming industry needs to be improved to achieve the goals set by Industrie 4.0. Distributed-Ledger-Technology (DLT) has been identified as a promising foundation for tackling the underlying problem of consistent information exchange. DLT-based solutions have already been developed, but none explicitly covers the roll forming use-case. This paper presents a Hyperledger-Fabric-based blockchain network to fill this research gap. This network is specifically designed for the roll forming industry, while still aiming to meet general information exchange requirements. The roll forming use-case is divided into the material supply chain and a design/simulation workflow. Participants and parameters in these two data transfer chains have been validated with the help of industry experts. Running the conceptualized network on an on-premise server has allowed for a detailed evaluation. Feedback provided by experts of the roll forming industry shows the potential of the presented network. Furthermore, the presented solution covers requirements often neglected by existing approaches like large data handling, compatibility to existing interfaces, secure communication, and access rights definition. In summary, this paper provides a pioneering implementation and evaluation of a DLT-based solution for the roll forming industry and, therefore, a foundation for the next steps towards Industrie 4.0.
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Digital Transformation in Industry
Additive Manufacturing and 3D Printing Technologies
The Line-less Mobile Assembly System paradigm (short LMAS) provides necessitated flexibility, especially for large-scale products as customer demands for individualized products persist and product life-cycles remain short. To make LMAS advantages operationally usable in an industrial context, it requires a suitable control system to connect multi-purpose assembly resources and to autonomously configure transient assembly stations. Therefore this paper reviews the relevant literature to identify the necessary components of such architectures. Depending on the control systemâs intention and use cases, the properties of the organizational paradigm, an adequate ontology and basic design patterns regarding the distribution and order-relationships of the system entities are to be defined conceptually. For the implementation, a role model, an interaction model, and a data model are required. Due to the utilization of mobile multipurpose resources and the possibility of factory shopfloor reconfiguration by transient stations, existing approaches do not meet LMAS inherent properties. Consequently, we present a suitable decentralized multi-agent control system approach.
Radhya Sahal, Saeed Hamood Alsamhi, Kenneth N. Brown, Donna OâShea · 6 authors
Digital twins (DTs) is a promising technology in the revolution of the industry and essential for Industry 4.0. DTs play a vital role in improving distributed manufacturing, providing up-to-date operational data representation of physical assets, supporting decision-making, and avoiding the potential risks in distributed manufacturing systems. Furthermore, DTs need to collaborate within distributed manufacturing systems to predict the risks and reach consensus-based decision-making. However, DTs collaboration suffers from single failure due to attack and connection in a centralized manner, data interoperability, authentication, and scalability. To overcome the above challenges, we have discussed the major high-level requirements for the DTs collaboration. Then, we have proposed a conceptual framework to fulfill the DTs collaboration requirements by using the combination of blockchain, predictive analysis techniques, and DTs technologies. The proposed framework aims to empower more intelligence DTs based on blockchain technology. In particular, we propose a concrete ledger-based collaborative DTs framework that focuses on real-time operational data analytics and distributed consensus algorithms. Furthermore, we describe how the conceptual framework can be applied using smart transportation system use cases, i.e., smart logistics and railway predictive maintenance. Finally, we highlighted the future direction to guide interested researchers in this interesting area.
Abstract Currently, inconsistent software versions lead to massive challenges for many car manufacturers. This is partly because within the product lifecycle management and the software engineering process, there is no correct handling of software versions for the âdata entryâ (installation of software on the ECU) of the vehicles. Furthermore, there are currently major challenges for many vehicle manufacturers to ensure transparency, integrity and full traceability of SW data status vis-Ă -vis the legislator. To counteract these challenges, new solutions in the field of vehicle engineering are to be developed based on a new platform called âCarEngChainNetâ and Blockchain technology. On the basis of the âCarEngChainNetâ platform, new main and sub-chain chains will be developed that allow tamper-proof SW data management (Peer to Peer and crypto technology) across the entire PLM chain with new methods such as model-based systems engineering of the requirement, function and integration of the SW components in different areas of vehicle development. The aim is to develop new transmission chains of vehicles with individually packaged software artefacts (e.g. ECU software) that can be securely transmitted from server to server into the vehicle.