A generic tri-model-based approach for product-level digital twin development in a smart manufacturing environment

Smart manufacturing, as an emerging manufacturing paradigm, leverages massive in-context data from manufacturing systems for intelligent decision makings. In such context, Cyber-Physical Systems (CPS) play a key role in digitizing manufacturing systems and integrating multiple systems together for collaborative works. Amongst different levels of smartness and connectedness of CPS, Digital Twin (DT), as an exact digital copy of a physical object or system including its properties and relationship with the environment, has a significant impact on realizing smart manufacturing. A DT constantly synchronizes with its physical system and provides real-time high-fidelity simulations of the system and offers ubiquitous control over the system. Despite its great advantages, few works have been discussed about DT reference models, let alone a generic manner to establish it for smart manufacturing. Aiming to fill the gap, this research introduces a generic CPS system architecture for DT establishment in smart manufacturing with a novel tri-model-based approach (i.e. digital model, computational model and graph-based model) for product-level DT development. The tri-model works concurrently to simulate real-world physical behaviour and characteristics of the digital model. To validate the proposed architecture and approach, a case study of an open source 3D printer DT establishment is further conducted. Conclusions and future works are also highlighted to provide insightful knowledge to both academia and industries at last.     

A review on the characteristics of cyber-physical systems for the future smart factories

The emergence of new technologies is providing new ways to compete in the current context of changeable and unpredictable market requirements. The focus of this paper is on Cyber-Physical Systems (CPSs), as one of the most promising transformative technological concept of such a context, thus considered by literature as the building blocks of future smart factories. However, CPSs are still in their conceptualization phase. To this end, much literature effort has been put on their technological characterization, while there is a lack of knowledge on the operations management characterization to manage such new systems. To contribute in this latter direction, this paper reviews literature in order to distinguish between technological characteristics of CPSs and operations management characteristics to build future CPS-based smart factories. This paper remarks the need for research on operations management characteristics as these may be the ones actually leading operations managers to the concrete implementation of CPS-based factories in manufacturing.     

Augmented Reality-assisted Intelligent Window for Cyber-Physical Machine Tools

Aiming to advance current machine tools to a higher level of intelligence and autonomy, this paper presents a new generation of machine tools, i.e. Cyber-Physical Machine Tool (CPMT), inspired by the recent advances in Cyber-Physical Systems (CPS). CPMT refers to a CPS-enabled machine tool that integrates physical machine tool and machining processes with computation and networking capabilities. Augmented Reality (AR) is used to enable intuitive and efficient human-machine interactions between humans and CPMT. An AR-assisted Intelligent Window for CPMT is proposed. The Intelligent Window is essentially an advanced Human-Machine Interface (HMI) which provides users with intuitive interactions with CPMT. The proposed Intelligent Window consists of four main functional modules, Real-time Control, AR-enabled Process Monitoring, AR-enabled Machining Simulation, and Process Optimization. An AR-assisted Intelligent Window for an EMCO Concept 105 milling machine is developed making use of a touch-screen computer. The advantages and potentials of CPS and AR in manufacturing are discussed based on the experience gained from the experiments.     

Design and management of image processing pipelines within CPS: Acquired experience towards the end of the FitOptiVis ECSEL Project

Cyber-Physical Systems (CPSs) are dynamic and reactive systems interacting with processes, environment and, sometimes, humans. They are often distributed with sensors and actuators, characterized for being smart, adaptive, predictive and react in real-time. Indeed, image- and video-processing pipelines are a prime source for environmental information for systems allowing them to take better decisions according to what they see. Therefore, in FitOptiVis, we are developing novel methods and tools to integrate complex image- and video-processing pipelines. FitOptiVis aims to deliver a reference architecture for describing and optimizing quality and resource management for imaging and video pipelines in CPSs both at design- and run-time. The architecture is concretized in low-power, high-performance, smart components, and in methods and tools for combined design-time and run-time multi-objective optimization and adaptation within system and environment constraints.     

The AXIOM platform for next-generation cyber physical systems

Cyber-Physical Systems (CPSs) are widely used in many applications that require interactions between humans and their physical environment. These systems usually integrate a set of hardware-software components for optimal application execution in terms of performance and energy consumption. The AXIOM project (Agile, eXtensible, fast I/O Module), presented in this paper, proposes a hardware-software platform for CPS coupled with an easy parallel programming model and sufficient connectivity so that the performance can scale-up by adding multiple boards. AXIOM supports a task-based programming model based on OmpSs and leverages a high-speed, inexpensive communication interface called AXIOM-Link. The board also tightly couples the CPU with reconfigurable resources to accelerate portions of the applications. As case studies, AXIOM uses smart video surveillance, and smart home living applications.     

Human Digital Twin in the context of Industry 5.0

Human-centricity, a core value of Industry 5.0, places humans in the center of production. It leads to the prioritization of human needs, spanning from health and safety to self-actualization and personal growth. The concept of the Human Digital Twin (HDT) is proposed as a critical method to realize human-centricity in smart manufacturing systems towards Industry 5.0. HDTs are digital representations of humans, aiming to change the practice of human-system integration by coupling humans’ characteristics directly to the system design and its performance. In-depth analysis, critical insights, and application guidelines of HDT are essential to realize the concept of Industry 5.0 in practice and evolve the smart manufacturing paradigm in modern factories. However, the investigation on the development of HDT to evolve humans’ roles and develop humans to their full potential is limited to date. Recent studies are rarely geared towards designing a standardized framework and architecture of HDT for diverse real-world applications. Thus, this work aims to close this research gap by carrying out a comprehensive survey on HDT in the context of Industry 5.0, summarizing the ongoing evolution, and proposing a proper connotation of HDT, before discussing the conceptual framework and system architecture of HDT and analyzing enabling technologies and industrial applications. This work provides guidance on possible avenues as well as challenges for the further development of HDT and its related concepts, allowing humans to reach their potential and accommodating their diverse needs in the futuristic smart manufacturing systems shaped by Industry 5.0.     

Fuzzy harmony search based optimal control strategy for wireless cyber physical system with industry 4.0

Journal of Intelligent Manufacturing - Recently, Industry 4.0 facilitates implementing several modular smart factories particularly the Cyber-Physical System. Due to enhanced growth in the...     

A secure and improved two factor authentication scheme using elliptic curve and bilinear pairing for cyber physical systems

Multimedia Tools and Applications - In order to equip the technical fraternity with smart technologies, the past few years have witnessed increasing usage of Cyber-Physical Systems (CPS) in...     

In-process machine vision monitoring of tool wear for Cyber-Physical Production Systems

The monitoring of tool wear in machining process is becoming a crucial element in the modern production systems to predict the tool lifespan, and consequently the ideal point to replace it, still remains a challenge up to now. On the other hand, Cyber-Physical Systems (CPSs) have attracted researchers in many areas, especially in manufacturing, and they are playing a key role in the integration of heterogeneous software and hardware components. In this paper, an in-process machine vision monitoring of tool wear is integrated into a production system based on the CPS approach. Thereby, a methodology of four phases is proposed, whose goal basically is to raise the requirements, validate the integration, develop the decentralized architecture and finally prove the efficiency, robustness, and capabilities that only cyber-physical systems can bring to a production system. The feasibility and effectiveness of the proposed monitoring system for in-process tool wear is validated in a CNC drilling machining process.     

Multi-agent reinforcement learning for online scheduling in smart factories

Rapid advances in sensing and communication technologies connect isolated manufacturing units, which generates large amounts of data. The new trend of mass customization brings a higher level of disturbances and uncertainties to production planning. Traditional manufacturing systems analyze data and schedule orders in a centralized architecture, which is inefficient and unreliable for the overdependence on central controllers and limited communication channels. Internet of things (IoT) and cloud technologies make it possible to build a distributed manufacturing architecture such as the multi-agent system (MAS). Recently, artificial intelligence (AI) methods are used to solve scheduling problems in the manufacturing setting. However, it is difficult for scheduling algorithms to process high-dimensional data in a distributed system with heterogeneous manufacturing units. Therefore, this paper presents new cyber-physical integration in smart factories for online scheduling of low-volume-high-mix orders. First, manufacturing units are interconnected with each other through the cyber-physical system (CPS) by IoT technologies. Attributes of machining operations are stored and transmitted by radio frequency identification (RFID) tags. Second, we propose an AI scheduler with novel neural networks for each unit (e.g., warehouse, machine) to schedule dynamic operations with real-time sensor data. Each AI scheduler can collaborate with other schedulers by learning from their scheduling experiences. Third, we design new reward functions to improve the decision-making abilities of multiple AI schedulers based on reinforcement learning (RL). The proposed methodology is evaluated and validated in a smart factory by real-world case studies. Experimental results show that the new architecture for smart factories not only improves the learning and scheduling efficiency of multiple AI schedulers but also effectively deals with unexpected events such as rush orders and machine failures.     

Semantic communications between distributed cyber-physical systems towards collaborative automation for smart manufacturing

Machine-to-machine (M2M) communication is a crucial technology for collaborative manufacturing automation in the Industrial Internet of Things (IIoT)-empowered industrial networks. The new decentralized manufacturing automation paradigm features ubiquitous communication and interoperable interactions between machines. However, peer-to-peer (P2P) interoperable communications at the semantic level between industrial machines is a challenge. To address this challenge, we introduce a concept of Semantic-aware Cyber-Physical Systems (SCPSs) based on which manufacturing devices can establish semantic M2M communications. In this work, we propose a generic system architecture of SCPS and its enabling technologies. Our proposed system architecture adds a semantic layer and a communication layer to the conventional cyber-physical system (CPS) in order to maximize compatibility with the diverse CPS implementation architecture. With Semantic Web technologies as the backbone of the semantic layer, SCPSs can exchange semantic messages with maximum interoperability following the same understanding of the manufacturing context. A pilot implementation of the presented work is illustrated with a proof-of-concept case study between two semantic-aware cyber-physical machine tools. The semantic communication provided by the SCPS architecture makes ubiquitous M2M communication in a network of manufacturing devices environment possible, laying the foundation for collaborative manufacturing automation for achieving smart manufacturing. Another case study focusing on decentralized production control between machines in a workshop also proved the merits of semantic-aware M2M communication technologies.     

Dependency-based security risk assessment for cyber-physical systems

A cyber-physical attack is a security breach in cyber space that impacts on the physical environment. The number and diversity of such attacks against Cyber-Physical Systems (CPSs) are increasing at impressive rates. In times of Industry 4.0 and Cyber-Physical Systems, providing security against cyber-physical attacks is a serious challenge which calls for cybersecurity risk assessment methods capable of investigating the tight interactions and interdependencies between the cyber and the physical components in such systems. However, existing risk assessment methods do not consider this specific characteristic of CPSs. In this paper, we propose a dependency-based, domain-agnostic cybersecurity risk assessment method that leverages a model of the CPS under study that captures dependencies among the system components. The proposed method identifies possible attack paths against critical components of a CPS by taking an attacker’s viewpoint and prioritizes these paths according to their risk to materialize, thus allowing the defenders to define efficient security controls. We illustrate the workings of the proposed method by applying it to a case study of a CPS in the energy domain, and we highlight the advantages that the proposed method offers when used to assess cybersecurity risks in CPSs.

     

A method of NC machine tools intelligent monitoring system in smart factories

The construction of effectual connection to bridge the gap between physical machine tools and upper software applications is one of the inherent requirements for smart factories. The difficulties in this issue lies in the lack of effective and appropriate means for real-time data acquisition, storage and processing in monitoring and the post workflows. The rapid advancements in Internet of things (IoT) and information technology have made it possible for the realization of this scheme, which have become an important module of the concepts such as “Industry 4.0”, etc. In this paper, a framework of bi-directional data and control flows between various machine tools and upper-level software system is proposed, within which several key stumbling blocks are presented, and corresponding solutions are subsequently deeply investigated and analyzed. Through monitoring manufacturing big data, potential essential information are extracted, providing useful guides for practical production and enterprise decision-making. Based on the integrated model, an NC machine tool intelligent monitoring and data processing system in smart factories is developed. Typical machine tools, such as Siemens series, are the main objects for investigation. The system validates the concept and performs well in the complex manufacturing environment, which will be a beneficial attempt and gain its value in smart factories.     

Meta-inventory

In an Industry 4.0 Factory, physical entities such as humans, machines and materials are digitized into digital twins (DT) with smart IoT (Internet of Things) devices resulting in Cyber-Physical Production Systems (CPPS). Real-time data analytics builds up traceability and visibility, not only in the physical domain but also cyber space. This paper adds a new concept of cyber-physical inventory or simply meta-inventory to Industry 4.0 CPPS. In addition to physical items, their digital twins are considered as part of production inventory. Traceability and visibility enabled by digital twin can significantly reduce complexity and uncertainties (e.g. lead times and variability) while achieving resilience in case of major disturbances. The CPPS factory hedges the risks through meta-inventory without incurring cost for holding inventory digitally. After reflecting upon the developments of production inventory management corresponding to the evolutionary history of manufacturing systems to Industry 4.0, the paper presents the meta-inventory paradigm within a simple Industry 4.0 compliant supply chain. The factory, the supplier, and the transport implement a VMI (vendor-managed inventory) strategy. Two well-known basic EOQ (Economic Order Quantity) and EPQ (Economic Production Quantity or production Lot Sizing) problems are extended to demonstrate and quantify the impacts of using meta-inventory on the supply chain and the member enterprise. The analyses allow us to unfold key perspectives in more complex production and supply chain systems for further research.     

基于多视图的信息物理融合系统体系结构研究

美国将信息-物理融合系统（Cyber-Physical Systems,CPS）列为八个重要信息技术领域之首,CPS亦成为国内当前研究热点。通过对国内外CPS研究现状分析,针对系统、功能和技术三个视图角度,提出了三种新型基于视图的CPS体系结构,说明了三种视图间的关系,利用微电网CPS实例论证了基于不同视图的CPS体系结构分析方法的优点。同时,对于CPS未来的研究方向进行了展望。     

Control of Cyber-Physical-Systems with logic specifications: A formal methods approach

There is a growing interest in Industry and Academia in large scale composite systems where a number of physical processes are interfaced with intelligent units that control them and govern their interactions. Examples are manufacturing plants, drone swarms, and autonomous connected cars. These systems belong to the category of Cyber-Physical Systems (CPS), where the physical components interact with the digital world. Designing and managing CPS safely and securely is extremely difficult given their heterogeneity, i.e., the presence of multiple physical and logical domains, and their scale, i.e., number of components and of interconnections. To cope with these difficulties, mathematical approaches have been proposed but few if any can deal with all the above mentioned challenges. This tutorial surveys our work based on formal methods, which attempts at dealing with the heterogeneity and complexity of CPS.     

Blockchain-based application for the traceability of complex assembly structures

Production processes are becoming increasingly complex and decentralized, spanning across multiple manufacturing areas, factories, and even countries. Thereby is the development of a holistic and data-continuous traceability system, which provides transparency of records for the entire manufacturing flow from raw materials to the final product, one of the main challenges. While some solutions already address traceability issues in supply chain management and manufacturing, customized and volatile industries, in which each product features a unique configuration, are still lacking appropriate models and proofs of concept. In this research, we develop a decentralized blockchain application called TokenTrail, which focuses on the specific traceability requirements of multi-hierarchical assembly structures. The proposed blockchain architecture is based on a consortium Ethereum network and a Proof of Authority consensus, which delivers a trusted and shared database within an economic processing framework. To overcome the challenges of managing liaison transformations, we develop an assembly token manager based on the semi-fungible ERC 1155 token, which enables a direct representation of complex assembly processes and structures containing unique parts and batches within one smart contract. A user-friendly front-end application simplifies the querying of the recorded traceability data and visualizes existing dependencies.     

From ethics to standards – A path via responsible AI to cyber-physical production systems

The central claim of the paper is that the development and control of Cyber-Physical Production Systems (CPPS) requires a systematic approach to handle and include explicit ethical considerations. Since the contribution of artificial intelligence (AI) technologies, and of agent-based models in particular, was instrumental in the evolution of CPPSs, approaches of ethical AI should be endorsed in CPPS development by design. The paper discusses recent advances for ethical AI and suggests a pathway from ethical norms towards standards. As it is argued, taking the responsible AI approach is promising when tackling the main ethic-related challenges of Cyber-Physical Production Systems. We expose a number of dilemmas to be resolved so that AI systems incorporated in CPPS cause no damages either in humans, equipment or in the environment and increase the trust in the users of current and future AI technologies.     

A digital-twin visualized architecture for Flexible Manufacturing System

The new generation of industrial 4.0 intelligent manufacturing system consists of Human-Cyber-Physical System (HCPS), integrating human with cyber and physical systems. In manufacturing, a digital-twin visualization architecture is to solve the human-machine interaction problem that concerns digital-twin modeling on the Cyber-Physical (C-P) side and on the Human-Cyber side. Although there are many related research and applications, there lacks attention in terms of full life cycle functional services and lightweight architecture. This paper presents a general architecture of digital-twin visualization for flexible manufacturing systems (FMS). How the digital-twin C-P modeling of multi-source heterogeneous information can be described is investigated and how the 3D visualized human-machine interaction with digital-twin scenario information is explored in the proposed architecture. Besides, the visualization method of high-value information, relating to the life cycle planning, design, debugging and service stages, is studied and discussed thoroughly. Also, a digital-twin modeling concept of "Geometric information (G)-Historical samples (H)-Object attribute (O)-Snapshot collection (S)-Topology constraint (T)" (GHOST) is proposed, and methods for developing virtual digital-twin scenes architecture are presented. Based on the proposed modeling concept of GHOST for digital-twin, prototypes have been developed for the general platform of digital-twin RESTful services and the cross-platform general visual mock-up software. Experimental results show that this method is effective in the FMS lifecycle in various aspects.