Digital Twin Fidelity Requirements Model for Manufacturing

The Digital Twin (DT), including its sub-categories Digital Model (DM) and Digital Shadow (DS), is a promising concept in the context of Smart Manufacturing and Industry 4.0. With ongoing maturation of its fundamental technologies like Simulation, Internet of Things (IoT), Cyber-Physical Systems (CPS), Artificial Intelligence (AI) and Big Data, DT has experienced a substantial increase in scholarly publications and industrial applications. According to academia, DT is considered as an ultra-realistic, high-fidelity virtual model of a physical entity, mirroring all of its properties most accurately. Furthermore, the DT is capable of altering this physical entity based on virtual modifications. Fidelity thereby refers to the number of parameters, their accuracy and level of abstraction. In practice, it is questionable whether the highest fidelity is required to achieve desired benefits. A literary analysis of 77 recent DT application articles reveals that there is currently no structured method supporting scholars and practitioners by elaborating appropriate fidelity levels. Hence, this article proposes the Digital Twin Fidelity Requirements Model (DT-FRM) as a possible solution. It has been developed by using concepts from Design Science Research methodology. Based on an initial problem definition, DT-FRM guides through problem breakdown, identifying problem centric dependent target variables (1), deriving (2) and prioritizing underlying independent variables (3), and defining the required fidelity level for each variable (4). This way, DT-FRM enables its users to efficiently solve their initial problem while minimizing DT implementation and recurring costs. It is shown that assessing the appropriate level of DT fidelity is crucial to realize benefits and reduce implementation complexity in manufacturing

IEEE Access Special Section Editorial: Artificial Intelligence and Cognitive Computing for Communication and Network

With the rapid development of communication and network technologies, novel information services and applications are rapidly growing worldwide. Advanced communications and networks greatly enhance the user experience, and have a major impact on all aspects of people's lifestyles in terms of work, society, and the economy. Although advanced techniques have extensively improved users' quality of experience (QoE), they are not adequate to meet the various requirements of seamless wide-area coverage, high-capacity hot-spots, low-power massive-connections, low-latency and high-reliability, and other scenarios. Therefore, it is a great challenge to develop smart communications and networks that support optimized management, dynamic configuration, and feasible services

Openness of Digital Twins in Logistics – A Review

Openness is becoming increasingly important in scientific research and practice. It describes the phenomenon of sharing information with other internal or external stakeholders by using different technologies, e.g., cloud computing, distributed ledger, or digital twins. Hence, many researchers investigate and evaluate the openness of platforms. Alongside these platforms, digital twins are gaining influence in industrial processes. A digital twin is a virtual representation of a physical entity connected through a bi-directional data linkage. Its primary purpose is to visualize, analyze, and optimize production and logistics systems. Nevertheless, research shows a lack of knowledge in the domain of the openness of digital twins and that the topic has not been addressed adequately. To approach this research gap, this paper provides a review of literature-based work on digital twins focusing on logistical contexts. It aims to answer the question of how open digital twins are, depending on their use case, purpose, and status as digital twin or digital shadow. Through a comprehensive research approach, this paper provides researchers and practitioners with meaningful insights into the openness of digital twins

Abductive Design of BDI Agent-based Digital Twins of Organizations

For a Digital Twin - a precise, virtual representation of a physical counterpart - of a human-like system to be faithful and complete, it must appeal to a notion of anthropomorphism (i.e., attributing human behaviour to non-human entities) to imitate (1) the externally visible behaviour and (2) the internal workings of that system. Although the Belief-Desire-Intention (BDI) paradigm was not developed for this purpose, it has been used successfully in human modeling applications. In this sense, we introduce in this thesis the notion of abductive design of BDI agent-based Digital Twins of organizations, which builds on two powerful reasoning disciplines: reverse engineering (to recreate the visible behaviour of the target system) and goal-driven eXplainable Artificial Intelligence (XAI) (for viewing the behaviour of the target system through the lens of BDI agents). Precisely speaking, the overall problem we are trying to address in this thesis is to “Find a BDI agent program that best explains (in the sense of formal abduction) the behaviour of a target system based on its past experiences . To do so, we propose three goal-driven XAI techniques: (1) abductive design of BDI agents, (2) leveraging imperfect explanations and (3) mining belief-based explanations. The resulting approach suggests that using goal-driven XAI to generate Digital Twins of organizations in the form of BDI agents can be effective, even in a setting with limited information about the target system’s behaviour

TOWARD INTELLIGENT WELDING BY BUILDING ITS DIGITAL TWIN

To meet the increasing requirements for production on individualization, efficiency and quality, traditional manufacturing processes are evolving to smart manufacturing with the support from the information technology advancements including cyber-physical systems (CPS), Internet of Things (IoT), big industrial data, and artificial intelligence (AI). The pre-requirement for integrating with these advanced information technologies is to digitalize manufacturing processes such that they can be analyzed, controlled, and interacted with other digitalized components. Digital twin is developed as a general framework to do that by building the digital replicas for the physical entities. This work takes welding manufacturing as the case study to accelerate its transition to intelligent welding by building its digital twin and contributes to digital twin in the following two aspects (1) increasing the information analysis and reasoning ability by integrating deep learning; (2) enhancing the human user operative ability to physical welding manufacturing via digital twins by integrating human-robot interaction (HRI). Firstly, a digital twin of pulsed gas tungsten arc welding (GTAW-P) is developed by integrating deep learning to offer the strong feature extraction and analysis ability. In such a system, the direct information including weld pool images, arc images, welding current and arc voltage is collected by cameras and arc sensors. The undirect information determining the welding quality, i.e., weld joint top-side bead width (TSBW) and back-side bead width (BSBW), is computed by a traditional image processing method and a deep convolutional neural network (CNN) respectively. Based on that, the weld joint geometrical size is controlled to meet the quality requirement in various welding conditions. In the meantime, this developed digital twin is visualized to offer a graphical user interface (GUI) to human users for their effective and intuitive perception to physical welding processes. Secondly, in order to enhance the human operative ability to the physical welding processes via digital twins, HRI is integrated taking virtual reality (VR) as the interface which could transmit the information bidirectionally i.e., transmitting the human commends to welding robots and visualizing the digital twin to human users. Six welders, skilled and unskilled, tested this system by completing the same welding job but demonstrate different patterns and resulted welding qualities. To differentiate their skill levels (skilled or unskilled) from their demonstrated operations, a data-driven approach, FFT-PCA-SVM as a combination of fast Fourier transform (FFT), principal component analysis (PCA), and support vector machine (SVM) is developed and demonstrates the 94.44% classification accuracy. The robots can also work as an assistant to help the human welders to complete the welding tasks by recognizing and executing the intended welding operations. This is done by a developed human intention recognition algorithm based on hidden Markov model (HMM) and the welding experiments show that developed robot-assisted welding can help to improve welding quality. To further take the advantages of the robots i.e., movement accuracy and stability, the role of the robot upgrades to be a collaborator from an assistant to complete a subtask independently i.e., torch weaving and automatic seam tracking in weaving GTAW. The other subtask i.e., welding torch moving along the weld seam is completed by the human users who can adjust the travel speed to control the heat input and ensure the good welding quality. By doing that, the advantages of humans (intelligence) and robots (accuracy and stability) are combined together under this human-robot collaboration framework. The developed digital twin for welding manufacturing helps to promote the next-generation intelligent welding and can be applied in other similar manufacturing processes easily after small modifications including painting, spraying and additive manufacturing

On the requirements of digital twin-driven autonomous maintenance

Autonomy has become a focal point for research and development in many industries. Whilst this was traditionally achieved by modelling self-engineering behaviours at the component-level, efforts are now being focused on the sub-system and system-level through advancements in artificial intelligence. Exploiting its benefits requires some innovative thinking to integrate overarching concepts from big data analysis, digitisation, sensing, optimisation, information technology, and systems engineering. With recent developments in Industry 4.0, machine learning and digital twin, there has been a growing interest in adapting these concepts to achieve autonomous maintenance; the automation of predictive maintenance scheduling directly from operational data and for in-built repair at the systems-level. However, there is still ambiguity whether state-of-the-art developments are truly autonomous or they simply automate a process. In light of this, it is important to present the current perspectives about where the technology stands today and indicate possible routes for the future. As a result, this effort focuses on recent trends in autonomous maintenance before moving on to discuss digital twin as a vehicle for decision making from the viewpoint of requirements, whilst the role of AI in assisting with this process is also explored. A suggested framework for integrating digital twin strategies within maintenance models is also discussed. Finally, the article looks towards future directions on the likely evolution and implications for its development as a sustainable technolog

A review of Industry 4.0 potential to accelerate the transition to a Circular Economy

The actual model of consumption is challenging the planet capabilities to withstand the pressure generated from excess resource consumption. The continuous growth of population and the increment of the middle class in the world threats planet’s sustainability. The model of Circular Economy based on the optimization of resources trough the maximization of use and the reduction of waste, appears as the new key to solve the problem. However, the transition to this new system comprises a certain degree of complexity. At same time industry is confronting the fourth revolution also known as Industry 4.0, which intends to digitalize the whole industry through the use of technologies like the Internet of Things or Additive Manufacturing with the aim of optimizing the industrial systems. Along this work both terms of Circular Economy and Industry 4.0 are presented and analyzed in order to generate an adequate context for providing an analysis of how these emergent industrial technologies can accelerate the transition to this new model of economy. This work has the intention of serving as foundation for future research around a topic that offers a great potential and still remains underdeveloped.El modelo actual de consumo desafía las capacidades del planeta para soportar la presión generada por el excesivo consumo de recursos. El crecimiento continuo de la población y el incremento de la clase media en el mundo amenaza la sostenibilidad. El modelo de Economía Circular basado en la optimización de los recursos a través de la maximización de su uso y la reducción de residuos, aparece como la clave para solventar el problema. Sin embargo la transición hacia este nuevo sistema comprende cierto grado de complejidad. Al mismo tiempo la industria enfronta su cuarta revolución, también conocida como Industria 4.0, que propone digitalizar el total de la industria a través de tecnologías como el Internet de las cosas o la impresión 3D con el objeto de optimizar los sistemas industriales. En este trabajo se presentan ambos conceptos de Economía Circular e Industria 4.0 con el objeto de generar un contexto adecuado sobre el cual analizar cómo estas emergentes tecnologías industriales pueden ayudar a acelerar la transición hacia el nuevo modelo de economía. Con la intención de que este trabajo sirva como una buena base sobre la cual desarrollar futuras investigaciones alrededor de un tema que posee gran potencial y aun no se encuentra suficientemente desarrollado.Egungo kontsumo-ereduak erronka egiten die planetak baliabideen gehiegizko kontsumoak eragindako presioa jasateko dituen gaitasunei. Biztanleriaren etengabeko hazkundeak eta munduko klase ertainaren hazkundeak jasangarritasuna mehatxatzen du. Ekonomia zirkularraren eredua baliabideen optimizazioan oinarritzen da, erabilera maximizatuz eta hondakinak murriztuz, eta arazoa konpontzeko gakoa da. Hala ere, sistema berri horretaranzko trantsizioak konplexutasun maila bat hartzen du. Aldi berean, industriak aurre egiten dio bere laugarren iraultzari, Industria 4.0 izenaz ere ezagutzen dena, industriaren guztizkoa digitalizatzea proposatzen duena, Gauzen Internet edo 3D inprimaketa bezalako teknologien bidez, industria-sistemak optimizatzeko asmoz. Lan honetan, Ekonomia Zirkularra eta Industria 4.0 kontzeptuak aurkezten dira, testuinguru egoki bat sortzeko helburuarekin. Testuinguru horretan, industria-teknologia berri horiek ekonomia-eredu berrirako trantsizioa bizkortzen nola lagun dezaketen aztertzen da. Lan hau potentzial handia duen eta oraindik behar bezala garatuta ez dagoen gai baten inguruko etorkizuneko ikerketak garatzeko oinarri ona izan dadin

Flexible Automation and Intelligent Manufacturing: The Human-Data-Technology Nexus

This is an open access book. It gathers the first volume of the proceedings of the 31st edition of the International Conference on Flexible Automation and Intelligent Manufacturing, FAIM 2022, held on June 19 – 23, 2022, in Detroit, Michigan, USA. Covering four thematic areas including Manufacturing Processes, Machine Tools, Manufacturing Systems, and Enabling Technologies, it reports on advanced manufacturing processes, and innovative materials for 3D printing, applications of machine learning, artificial intelligence and mixed reality in various production sectors, as well as important issues in human-robot collaboration, including methods for improving safety. Contributions also cover strategies to improve quality control, supply chain management and training in the manufacturing industry, and methods supporting circular supply chain and sustainable manufacturing. All in all, this book provides academicians, engineers and professionals with extensive information on both scientific and industrial advances in the converging fields of manufacturing, production, and automation

Flexible Automation and Intelligent Manufacturing: The Human-Data-Technology Nexus

This is an open access book. It gathers the first volume of the proceedings of the 31st edition of the International Conference on Flexible Automation and Intelligent Manufacturing, FAIM 2022, held on June 19 – 23, 2022, in Detroit, Michigan, USA. Covering four thematic areas including Manufacturing Processes, Machine Tools, Manufacturing Systems, and Enabling Technologies, it reports on advanced manufacturing processes, and innovative materials for 3D printing, applications of machine learning, artificial intelligence and mixed reality in various production sectors, as well as important issues in human-robot collaboration, including methods for improving safety. Contributions also cover strategies to improve quality control, supply chain management and training in the manufacturing industry, and methods supporting circular supply chain and sustainable manufacturing. All in all, this book provides academicians, engineers and professionals with extensive information on both scientific and industrial advances in the converging fields of manufacturing, production, and automation

Proceedings of the Conference on Production Systems and Logistics: CPSL 2022

[no abstract available