Tõnis Raamets, Jüri Majak, Kristo Karjust, Kashif Mahmood · 5 authors
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. Müller
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.
Larissa Krämer, Pascal Kaiser, Jonathan Kajewski, Moritz Roidl
Reversing the digital twin concept to control the physical entity with its virtual entity is an emerging field of research to ensure that the physical components of a production system mirror the simulation precisely. We present a novel concept for the integration of blockchain-based smart contracting into the reverse digital twin of a hybrid production. In this approach, a simulation model monitors and controls the physical system to increase transparency and to enable dynamic simulation of scenarios. To further increase transparency in the digital twin, the financial flow of the production processes is integrated by smart contracting. This ensures tamper-proof documentation of processes and can decrease information asymmetry between the multiple stakeholders interacting within a hybrid production. To validate the concept, we conduct a case study with the implemented digital twin to show its capabilities.
As part of TIV's DHW on Vehicle 5.0, this letter introduces a novel concept, Logistics 5.0, to address high complexities in logistics Cyber-Physical-Social Systems (CPSS). Building upon the theory of parallel intelligence and leveraging advanced technologies and methods such as blockchain, scenarios engineering, Decentralized Autonomous Organizations and Operations (DAOs), Logistics 5.0 promises to accelerate the paradigm shift towards intelligent and sustainable logistics. First, the parallel logistics framework is proposed, and the logistics ecosystem is discussed. Then, the human-oriented operating systems (HOOS) are suggested to providing intelligent Logistics 5.0 solutions. Logistics 5.0 serves as a critical catalyst in realizing the “6S” objectives, i.e. Safety, Security, Sustainability, Sensitivity, Service, and Smartness, within the logistics industry.
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 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.
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 cyber–physical–social 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 “Artificial systems”, “Computational experiments” and “Parallel execution” (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.
Products and materials traceability is essential in modern manufacturing, where the production must meet certain standards that range from Quality Control (QC) to the quality of the used materials. In this environment, blockchain applications allow certifying data provenience and subsequent modification, offering trust and security along the entire supply chain. Nonetheless, the design and the development of such applications are usually performed manually and, thus, subject to errors.In this paper, we propose a methodology allowing to automatically generate smart contracts starting from a SysML model. This approach allows easing the integration of blockchain applications in a production system: by abstracting the implementations with models, it is possible to generate smart contracts for different blockchains, connecting to multiple production environments.We applied the proposed methodology on a real manufacturing system, assessing the quality of a case-study production.
B Kohl, M. Krüger, 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.
Open access
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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 system’s 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.
Bastian Pokorni, Erdem Geleç, Stephan Verhasselt, Stefanie Findeisen
Pharmaceutical packaging processes are changing drastically in their characteristics from low order-mix with high volumes to a situation with a high order-mix with low volumes due to increasing individualization of products. This requires highly flexible automation concepts on the one hand and very flexible work organizations on the other. As part of Industry 4.0, a wide range of technologies are being researched and implemented. The work organization in the production of the future remains insufficiently researched. The performance potential of self-directed and agile teams has been confirmed in the field of knowledge work. The importance of self-direction and autonomous work teams has been emphasized in lean manufacturing in the past, but there is a lack of practical examples of how such organizational forms can look and which potentials can be realized regarding productivity, flexibility and employee satisfaction. Based on concepts of decentralized decision-making, shop-floor workers are empowered to take responsibility for the organization and control of processes. This paper presents a case study in which the planning and implementation of self-directed and agile teams in production was realized.
Radhya Sahal, Saeed Hamood Alsamhi, Kenneth N. Brown, Donna O’Shea · 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.