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Parallel manufacturing for Industrial Metaverses:A New Paradigm in smart manufacturing

IEEE/CAA Journal of Automatica Sinica 2022年第12期9卷 2063-2070页

作者： Jing Yang Xiaoxing Wang Yandong Zhao The State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Beijing SANBODY Technology Company LtdBeijing 214000China North Automatic Control Technology Institute Taiyuan 030006China

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(Io M) 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 Flex Vega.

关键词： Parallel manufacturing Digital Workers CPSS smart manufacturing Industrial Metaverses

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smart manufacturing and Intelligent manufacturing:A Comparative Review

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Engineering 2021年第6期7卷 738-757页

作者： Baicun Wang Fei Tao Xudong Fang Chao Liu Yufei Liu Theodor Freiheit State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical EngineeringZhejiang UniversityHangzhou 310027China Department of Mechanical Engineering University of MichiganAnn ArborMI 48109USA Chinese Academy of Engineering Beijing 100088China School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China School of Mechanical Engineering Xi’an Jiaotong UniversityXi’an 710049China School of Engineering Cardiff UniversityCardiff CF243AAUK

The application of intelligence to manufacturing has emerged as a compelling topic for researchers and industries around the world.However,different terminologies,namely smart manufacturing(SM)and intelligent manufacturing(IM),have been applied to what may be broadly characterized as a similar paradigm by some researchers and practitioners.While SM and IM are similar,they are not identical.From an evolutionary perspective,there has been little consideration on whether the definition,thought,connotation,and technical development of the concepts of SM or IM are consistent in the literature.To address this gap,the work performs a qualitative and quantitative investigation of research literature to systematically compare inherent differences of SM and IM and clarify the relationship between SM and IM.A bibliometric analysis of publication sources,annual publication numbers,keyword frequency,and top regions of research and development establishes the scope and trends of the currently presented research.Critical topics discussed include origin,definitions,evolutionary path,and key technologies of SM and IM.The implementation architecture,standards,and national focus are also discussed.In this work,a basis to understand SM and IM is provided,which is increasingly important because the trend to merge both terminologies rises in Industry 4.0 as intelligence is being rapidly applied to modern manufacturing and human–cyber–physical systems.

关键词： smart manufacturing Intelligent manufacturing Industry 4.0 Human–cyber–physical system(HCPS)

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AI-Based Modeling and Data-Driven Evaluation for smart manufacturing Processes

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IEEE/CAA Journal of Automatica Sinica 2020年第4期7卷 1026-1037页

作者： Mohammadhossein Ghahramani Yan Qiao Meng Chu Zhou Adrian O’Hagan James Sweeney the University College Dublin BelfieldDublin 4Ireland the Institute of Systems Engineering Macao University of Science and TechnologyMacaoChina the Helen and John C.Hartmann Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA the Royal College of Surgeons in Ireland Dublin 8Ireland

smart manufacturing refers to optimization techniques that are implemented in production operations by utilizing advanced analytics approaches. With the widespread increase in deploying industrial internet of things(IIOT) sensors in manufacturing processes, there is a progressive need for optimal and effective approaches to data management.Embracing machine learning and artificial intelligence to take advantage of manufacturing data can lead to efficient and intelligent automation. In this paper, we conduct a comprehensive analysis based on evolutionary computing and neural network algorithms toward making semiconductor manufacturing smart.We propose a dynamic algorithm for gaining useful insights about semiconductor manufacturing processes and to address various challenges. We elaborate on the utilization of a genetic algorithm and neural network to propose an intelligent feature selection algorithm. Our objective is to provide an advanced solution for controlling manufacturing processes and to gain perspective on various dimensions that enable manufacturers to access effective predictive technologies.

关键词： Artificial intelligence(AI) cyber physical systems feature selection genetic algorithms(GA) industrial internet of things(IIOT) machine learning neural network(NN) smart manufacturing

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A multi-agent architecture for scheduling in platform-based smart manufacturing systems

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Frontiers of Information Technology & Electronic Engineering 2019年第11期20卷 1465-1492页

作者： Yong-kui LIU Xue-song ZHANG Lin ZHANG Fei TAO Li-hui WANG Center for Intelligent manufacturing Systems and Robots School of Mechano-Electronic EngineeringXidian UniversityXi'an 710071China College of Computer Science and Technology Jilin UniversityChangchun 130012China School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China Department of Production Engineering KTH Royal Institute of TechnologyStockholm 11428Sweden

During the past years, a number of smart manufacturing concepts have been proposed, such as cloud manufacturing, Industry 4.0, and Industrial Internet. One of their common aims is to optimize the collaborative resource configuration across enterprises by establishing platforms that aggregate distributed resources. In all of these concepts, a complete manufacturing system consists of distributed physical manufacturing systems and a platform containing the virtual manufacturing systems mapped from the physical ones. We call such manufacturing systems platform-based smart manufacturing systems (PSMSs). A PSMS can therefore be regarded as a huge cyber-physical system with the cyber part being the platform and the physical part being the corresponding physical manufacturing system. A significant issue for a PSMS is how to optimally schedule the aggregated resources. Multi-agent technology provides an effective approach for solving this issue. In this paper we propose a multi-agent architecture for scheduling in PSMSs, which consists of a platform-level scheduling multi-agent system (MAS) and an enterprise-level scheduling MAS. Procedures, characteristics, and requirements of scheduling in PSMSs are presented. A model for sched-uling in a PSMS based on the architecture is proposed. A case study is conducted to demonstrate the effectiveness of the proposed architecture and model.

关键词： Platform smart manufacturing Multi-agent Scheduling

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Training for smart manufacturing using a mobile robot-based production line

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Frontiers of Mechanical Engineering 2021年第2期16卷 249-270页

作者： Shuting WANG Liquan JIANG Jie MENG Yuanlong XIE Han DING School of Mechanical Science and Engineering Huazhong University of Science and TechnologyWuhan 430074China

Practice experimentation that integrates the manufacturing processes and cutting-edge technologies of smart manufacturing (SM) is essential for future academic and applied engineering personnel. The broadening efficacy of hands-on experience in SM engineering education has been manifested. In this regard, a reference practical system is proposed in this study for hands-on training in SM crucial advancements. The system constructs a mobile robot-based production line (MRPL) to increase participants’ interest in theoretical learning and professional skills. The MRPL-based reference system includes the comprehensive principles and processes involved in modern SM factories from warehousing to logistics, processing, and testing. With key features of modularity, integrability, customizability, and open architecture, this system has a threefold objective. First, it is an interdisciplinary subject that enables students to translate classroom learning into authentic practices, thus facilitating knowledge synthesis and training involvements. Second, it offers effective support to cultivate the attributions and behavioral competencies of SM talents, such as perseverance, adaptability, and cooperation. Third, it promotes students’ capacities for critical thinking and problem solving so that they can deal with the difficulties that physical systems have and motivates them to pursue careers with new syllabi, functions, and process techno-logies. The received positive evaluations and assessments confirm that this MRPL-based reference system is beneficial for modern SM talent training in higher engineering education.

关键词： smart manufacturing hands-on experience engineering education mobile robot-based production line

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Comprehensive Survey of the Landscape of Digital Twin Technologies and Their Diverse Applications

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工程与科学中的计算机建模(英文) 2024年第1期138卷 125-165页

作者： Haiyu Chen Haijian Shao Xing Deng Lijuan Wang Xia Wang School of Computer Jiangsu University of Science and TechnologyZhenjiang212003China Department of Electrical and Computer Engineering University of Nevada Las VegasLas VegasNV89154USA Department of Electrical and Computer Engineering National University of Singapore117583Singapore

The concept of the digital twin,also known colloquially as the DT,is a fundamental principle within Industry 4.0 framework.In recent years,the concept of digital siblings has generated considerable academic and practical interest.However,academia and industry have used a variety of interpretations,and the scientific literature lacks a unified and consistent definition of this term.The purpose of this study is to systematically examine the definitional landscape of the digital twin concept as outlined in scholarly literature,beginning with its origins in the aerospace domain and extending to its contemporary interpretations in the manufacturing industry.Notably,this investigationwill focus on the research conducted on Industry 4.0 and smartmanufacturing,elucidating the diverse applications of digital twins in fields including aerospace,intelligentmanufacturing,intelligent transportation,and intelligent cities,among others.

关键词： Digital twins Industry 4.0 smart manufacturing digital thread modeling simulation

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The Industrial Internet of Things:A Competitive Advantage in the Era of smart manufacturing

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政治经济学季刊 2023年第1期 135-155页

作者： Bai GAO Lan ZHU Department of Sociology Duke University Institute of Quantitative&Technological Economics CASS

At present,the Chinese manufacturing industry’s competitive advantage is facing multiple challenges.China’s“hexagon diagram”industrial policy,which has promoted the competitive advantage of Chinese companies in the era of globalization,encompasses these six strategies:enhancing factor supply,building infrastructure,improving institutional environments,enlarging market size,promoting industrial clustering,and encouraging competition.The hexagon model of industrial policy has gained China entry into many industries and increased their competitive advantage by lowering production cost and creating full-scope value chains.Lately,this competitive advantage is facing significant challenges from globalization reversal,trade wars,and the technological revolution.Relative to anti-globalization and trade wars,however,the most profound challenge facing China’s manufacturing industry is the rise of the Industrial Internet of Things and smart manufacturing.China needs to upgrade its hexagon diagram industrial policy to keep up with new developments in the Industrial Internet of Things in today’s era of smart manufacturing.

关键词： industrial policy competitive advantage Industrial Internet of Things smart manufacturing

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smart Process manufacturing for Formulated Products

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Engineering 2019年第6期5卷 1003-1009页

作者： James Litster Ian David L Bogle Department of Chemical and Biological Engineering University of SheffieldSheffield S102TNUK Centre for Process Systems Engineering Department of Chemical EngineeringUniversity College LondonLondon WC1E 6BTUK

We outline the smart manufacturing challenges for formulated products, which are typically multicom- ponent, structured, and multiphase. These challenges predominate in the food, pharmaceuticals, agricul- tural and specialty chemicals, energy storage and energetic materials, and consumer goods industries, and are driven by fast-changing customer demand and, in some cases, a tight regulatory framework. This paper discusses progress in smart manufacturing namely, digitalization and the use of large data- sets with predictive models and solution- nding algorithms in these industries. While some progress has been achieved, there is a strong need for more demonstration of model-based tools on realistic prob- lems in order to demonstrate their bene ts and highlight any systemic weaknesses.

关键词： smart manufacturing Formulated products Pharmaceuticals Modeling Supply chain integration Uncertainty

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Data Analytics and Machine Learning for smart Process manufacturing: Recent Advances and Perspectives in the Big Data Era

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Engineering 2019年第6期5卷 1010-1016页

作者： Chao Shang Fengqi You Department of Automation Tsinghua UniversityBeijing 100084China Robert Frederick Smith School of Chemical and Biomolecular Engineering Cornell UniversityIthacaNY 14853USA

Safe, ef cient, and sustainable operations and control are primary objectives in industrial manufacturing processes. State-of-the-art technologies heavily rely on human intervention, thereby showing apparent limitations in practice. The burgeoning era of big data is in uencing the process industries tremendously, providing unprecedented opportunities to achieve smart manufacturing. This kind of manufacturing requires machines to not only be capable of relieving humans from intensive physical work, but also be effective in taking on intellectual labor and even producing innovations on their own. To attain this goal, data analytics and machine learning are indispensable. In this paper, we review recent advances in data analytics and machine learning applied to the monitoring, control, and optimization of industrial processes, paying particular attention to the interpretability and functionality of machine learning mod- els. By analyzing the gap between practical requirements and the current research status, promising future research directions are identi ed.

关键词： Big data Machine learning smart manufacturing Process systems engineering

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Chengdu’s smart manufacturing in the New Era:Achievements and Optimization

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Contemporary Social Sciences 2022年第3期7卷 57-69页

作者： Sun Genjin Gao Rui Liu Ying Business and Tourism School Sichuan Agricultural UniversityYa’an625014China

The promotion of smart manufacturing is a crucial move that will enable Chengdu to build a major growth pole for high-quality national development and shape a new growth powerhouse.In recent years,Chengdu’s capability has made marked progress in smart manufacturing,significantly enhancing the capability for independent innovation,gradually optimizing development factor allocations,relentlessly strengthening pilot and demonstration effects,and constantly emerging collaborative service platforms.The development of Chengdu’s smart manufacturing can be summarized in four points:taking a holistic approach to top-down design,raising policy incentives for innovative talents,creating a favorable environment for industrial innovation,and building service platforms to promote innovation.In the new era,Chengdu should continue to explore the best possible path to smart manufacturing and target high-quality development of local manufacturing industries by making the following efforts:a)optimizing top-down design for better policy guidance;b)focusing on technological innovation to improve corresponding innovation capability;c)forging service platforms to break barriers to development;d)strengthening lateral communications to promote collaborative transformation;and e)building leading enterprises and brands native to Chengdu.

关键词： smart manufacturing achievements experience optimization Chengdu

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