With the advent of Industry 4.0,marked by a surge in intelligent manufacturing,advanced sensors embedded in smart factories now enable extensive data collection on equipment operation.The analysis of such data is pivo...With the advent of Industry 4.0,marked by a surge in intelligent manufacturing,advanced sensors embedded in smart factories now enable extensive data collection on equipment operation.The analysis of such data is pivotal for ensuring production safety,a critical factor in monitoring the health status of manufacturing apparatus.Conventional defect detection techniques,typically limited to specific scenarios,often require manual feature extraction,leading to inefficiencies and limited versatility in the overall process.Our research presents an intelligent defect detection methodology that leverages deep learning techniques to automate feature extraction and defect localization processes.Our proposed approach encompasses a suite of components:the high-level feature learning block(HLFLB),the multi-scale feature learning block(MSFLB),and a dynamic adaptive fusion block(DAFB),working in tandem to extract meticulously and synergistically aggregate defect-related characteristics across various scales and hierarchical levels.We have conducted validation of the proposed method using datasets derived from gearbox and bearing assessments.The empirical outcomes underscore the superior defect detection capability of our approach.It demonstrates consistently high performance across diverse datasets and possesses the accuracy required to categorize defects,taking into account their specific locations and the extent of damage,proving the method’s effectiveness and reliability in identifying defects in industrial components.展开更多
In the context of intelligent manufacturing,machine tools,as core equipment,directly influence production efficiency and product quality through their operational reliability.Traditional maintenance methods for machin...In the context of intelligent manufacturing,machine tools,as core equipment,directly influence production efficiency and product quality through their operational reliability.Traditional maintenance methods for machine tools,often characterized by low efficiency and high costs,fail to meet the demands of modern manufacturing industries.Therefore,leveraging intelligent manufacturing technologies,this paper proposes a solution optimized for the diagnosis and maintenance of machine tool faults.Initially,the paper introduces sensor-based data acquisition technologies combined with big data analytics and machine learning algorithms to achieve intelligent fault diagnosis of machine tools.Subsequently,it discusses predictive maintenance strategies by establishing an optimized model for maintenance strategy and resource allocation,thereby enhancing maintenance efficiency and reducing costs.Lastly,the paper explores the architectural design,integration,and testing evaluation methods of intelligent manufacturing systems.The study indicates that optimization of machine tool fault diagnosis and maintenance in an intelligent manufacturing environment not only enhances equipment reliability but also significantly reduces maintenance costs,offering broad application prospects.展开更多
In this paper,we built a robot training platform using virtual simulation software,and the robot assembly,handling,and palletizing were realized.The workstation includes an industrial robot,gas control unit,track func...In this paper,we built a robot training platform using virtual simulation software,and the robot assembly,handling,and palletizing were realized.The workstation includes an industrial robot,gas control unit,track function module,assembly function module,palletizing function module,vision module,etc.,and robot movement is achieved through language programming.The platform provides conditions for the practical ability training of application-oriented talents.展开更多
As a significant field of research under the 14th Five Year Plan,intelligent manufacturing holds a special position in industrial upgrading and core technology independence.Intelligent manufacturing is an important su...As a significant field of research under the 14th Five Year Plan,intelligent manufacturing holds a special position in industrial upgrading and core technology independence.Intelligent manufacturing is an important support for building a“scientific and technological power”in the future.Cultivating creative interdisciplinary talents who can adapt to the development of intelligent manufacturing industry in the new era is of great significance for China to realize the modernization and autonomy of its industrial system.In view of the existing gap between the interdisciplinary talent training of intelligent manufacturing and the actual needs of the industry,this paper focuses on the concept of interdisciplinary construction.Based on the cycle improvement of professional construction,a curriculum system that integrates the interaction of interconnected projects is established.Through four specific measures,namely establishing the collaborative development system of intelligent manufacturing specialty,building a cycle diagnosis and reform system of intelligent manufacturing specialty,improving the curriculum system of integrated modular specialty group,and setting up an application scenario project-based curriculum,we hope to provide reference for the cross-training of compound intelligent manufacturing talents.展开更多
Our next generation of industry-lndustry 4.0-holds the promise of increased flexibility in manufacturing, along with mass customization, better quality, and improved productivity. It thus enables companies to cope wit...Our next generation of industry-lndustry 4.0-holds the promise of increased flexibility in manufacturing, along with mass customization, better quality, and improved productivity. It thus enables companies to cope with the challenges of producing increasingly individualized products with a short lead-time to market and higher quality. Intelligent manufacturing plays an important role in Industry 4.0. Typical resources are converted into intelligent objects so that they are able to sense, act, and behave within a smart environment. In order to fully understand intelligent manufacturing in the context of Industry 4.0, this paper provides a comprehensive review of associated topics such as intelligent manufacturing, Internet of Things （IoT）- enabled manufacturing, and cloud manufacturing. Similarities and differences in these topics are highlighted based on our analysis. We also review key technologies such as the loT, cyber-physical systems （CPSs）, cloud computing, big data analytics （BDA）, and information and communications technology （ICT） that are used to enable intelligent manufacturing. Next, we describe worldwide movements in intelligent manufacturing, including governmental strategic plans from different countries and strategic plans from major international companies in the European Union, United States, Japan, and China. Finally, we present current challenges and future research directions. The concepts discussed in this paper will spark new ideas in the effort to realize the much-anticipated Fourth Industrial Revolution.展开更多
With ever-increasing market competition and advances in technology, more and more countries are prioritizing advanced manufacturing technology as their top priority for economic growth. Germany announced the Industry ...With ever-increasing market competition and advances in technology, more and more countries are prioritizing advanced manufacturing technology as their top priority for economic growth. Germany announced the Industry 4.0 strategy in 2013. The US government launched the Advanced Manufacturing Partnership （AMP） in 2011 and the National Network for Manufacturing Innovation （NNMI） in 2014. Most recently, the Manufacturing USA initiative was officially rolled out to further ＂leverage existing resources... to nurture manufacturing innovation and accelerate commercialization＂ by fostering close collaboration between industry, academia, and government partners. In 2015, the Chinese government officially published a 10- year plan and roadmap toward manufacturing： Made in China 2025. In all these national initiatives, the core technology development and implementation is in the area of advanced manufacturing systems. A new manufacturing paradigm is emerging, which can be characterized by two unique features： integrated manufacturing and intelligent manufacturing. This trend is in line with the progress of industrial revolutions, in which higher efficiency in production systems is being continuously pursued. To this end, 10 major technologies can be identified for the new manufacturing paradigm. This paper describes the rationales and needs for integrated and intelligent manufacturing （i2M） systems. Related technologies from different fields are also described. In particular, key technological enablers, such as the Intemet of Things and Services （IoTS）, cyber-physical systems （CPSs）, and cloud computing are discussed. Challenges are addressed with applica- tions that are based on commercially available platforms such as General Electric （GE）＇s Predix and PTC＇s ThingWorx.展开更多
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 manufact...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.展开更多
Based on the analysis of the characteristics and operation status of the process industry,as well as the development of the global intelligent manufacturing industry,a new mode of intelligent manufacturing for the pro...Based on the analysis of the characteristics and operation status of the process industry,as well as the development of the global intelligent manufacturing industry,a new mode of intelligent manufacturing for the process industry,namely,deep integration of industrial artificial intelligence and the Industrial Internet with the process industry,is proposed.This paper analyzes the development status of the existing three-tier structure of the process industry,which consists of the enterprise resource planning,the manufacturing execution system,and the process control system,and examines the decision-making,control,and operation management adopted by process enterprises.Based on this analysis,it then describes the meaning of an intelligent manufacturing framework and presents a vision of an intelligent optimal decision-making system based on human–machine cooperation and an intelligent autonomous control system.Finally,this paper analyzes the scientific challenges and key technologies that are crucial for the successful deployment of intelligent manufacturing in the process industry.展开更多
Pandemics like COVID-19 have created a spreading and ever-higher healthy threat to the humans in the manufacturing system which incurs severe disruptions and complex issues to industrial networks.The intelligent manuf...Pandemics like COVID-19 have created a spreading and ever-higher healthy threat to the humans in the manufacturing system which incurs severe disruptions and complex issues to industrial networks.The intelligent manufacturing(IM)systems are promising to create a safe working environment by using the automated manufacturing assets which are monitored by the networked sensors and controlled by the intelligent decision-making algorithms.The relief of the production disruption by IM technologies facilitates the reconnection of the good and service flows in the network,which mitigates the severity of industrial chain disruption.In this study,we create a novel intelligent manufacturing framework for the production recovery under the pandemic and build an assessment model to evaluate the impacts of the IM technologies on industrial networks.Considering the constraints of the IM resources,we formulate an optimization model to schedule the allocation of IM resources according to the mutual market demands and the severity of the pandemic.展开更多
Applications of process systems engineering(PSE)in plants and enterprises are boosting industrial reform from automation to digitization and intelligence.For ethylene thermal cracking,knowledge expression,numerical mo...Applications of process systems engineering(PSE)in plants and enterprises are boosting industrial reform from automation to digitization and intelligence.For ethylene thermal cracking,knowledge expression,numerical modeling and intelligent optimization are key steps for intelligent manufacturing.This paper provides an overview of progress and contributions to the PSE-aided production of thermal cracking;introduces the frameworks,methods and algorithms that have been proposed over the past10 years and discusses the advantages,limitations and applications in industrial practice.An entire set of molecular-level modeling approaches from feedstocks to products,including feedstock molecular reconstruction,reaction-network auto-generation and cracking unit simulation are described.Multilevel control and optimization methods are exhibited,including at the operational,cycle,plant and enterprise level.Relevant software packages are introduced.Finally,an outlook in terms of future directions is presented.展开更多
Agile intelligent manufacturing is one of the new manufacturing paradigms that adapt to the fierce globalizing market competition and meet the survival needs of the enterprises, in which the management and control of ...Agile intelligent manufacturing is one of the new manufacturing paradigms that adapt to the fierce globalizing market competition and meet the survival needs of the enterprises, in which the management and control of the production system have surpassed the scope of individual enterprise and embodied some new features including complexity, dynamicity, distributivity, and compatibility. The agile intelligent manufacturing paradigm calls for a production scheduling system that can support the cooperation among various production sectors, the distribution of various resources to achieve rational organization, scheduling and management of production activities. This paper uses multi-agents technology to build an agile intelligent manufacturing-oriented production scheduling system. Using the hybrid modeling method, the resources and functions of production system are encapsulated, and the agent-based production system model is established. A production scheduling-oriented multi-agents architecture is constructed and a multi-agents reference model is given in this paper.展开更多
Based on production data and metallurgical theory, this study discusses the control of inclusions in Baosteel’s products from the perspective of three key processes: converting, refining, and continuous casting(CC).T...Based on production data and metallurgical theory, this study discusses the control of inclusions in Baosteel’s products from the perspective of three key processes: converting, refining, and continuous casting(CC).The control of converter slagging, refining oxygen blowing(OB),and CC are considered.Based on metallurgical theory, this research investigates the effects of refining OB decarburization and argon blowing in a tundish on the oxygen content of molten steel.Combining theory and practice is beneficial to the discovery of new ways to tackle existing problems and the development of intelligent manufacturing.展开更多
NC machining of parts and components is developing in the direction of automation and intelligence.In addition,the automatic production line puts forward higher requirements for the overall layout,technological proces...NC machining of parts and components is developing in the direction of automation and intelligence.In addition,the automatic production line puts forward higher requirements for the overall layout,technological process and production tempo.The layout and simulation of the production line can more directly reflect the problems that may occur in the operation of the production line,and solve these problems through scientific methods.This project uses VisualOne software to simulate the operation of the intelligent manufacturing production line,which can directly find the existing problems in the layout of the production line and the process of the craftsmen,and optimize the layout of the production line.In addition,it can provide reliable support for the equipment procurement and on-site construction of the intelligent manufacturing automation production line in the later stage.展开更多
The 5th Meeting of Sino-German Standardization Sub-working Group on Intelligent Manufacturing/Industry 4.0 was held in Hangzhou,capital of east China’s Zhejiang province from December 3 to 4,2017,bringing together ov...The 5th Meeting of Sino-German Standardization Sub-working Group on Intelligent Manufacturing/Industry 4.0 was held in Hangzhou,capital of east China’s Zhejiang province from December 3 to 4,2017,bringing together over 90 representatives of both countries.展开更多
The advancement of intelligent manufacturing in Dongguan puts forward new requirements for industrial talents,and the development of new productivity is bound to force enterprises and employees to make adaptive adjust...The advancement of intelligent manufacturing in Dongguan puts forward new requirements for industrial talents,and the development of new productivity is bound to force enterprises and employees to make adaptive adjustments.The upgrading of intelligent manufacturing is not only the upgrading of intelligent machines but also the upgrading of the human brain,which includes the reshaping and cultivation of industrial talents.Based on field research,this study analyzes the different characteristics of the traditional and the new intelligent manufacturing model,as well as summarizes the characteristics of industrial talents and the changing trend of talent demand in view of the intelligent manufacturing model in Dongguan.展开更多
The advancement of the intelligent manufacturing industry(IMI)represents the future direction for the world's manufactur-ing sector,offering a promising avenue to bolster national competitiveness and enhance indus...The advancement of the intelligent manufacturing industry(IMI)represents the future direction for the world's manufactur-ing sector,offering a promising avenue to bolster national competitiveness and enhance industrial manufacturing efficiency.In this study,we took the industrial robot industry(IRI)as a case study to elucidate the spatial distribution and interconnections of IMI from a geographical perspective,and the modified diamond model(DM)was used to analyze the influencing factors.Results show that:1)the spatial pattern of IRI with various investment attributes in different industrial chain links is generally similar,centered in the southeast.Key investment areas are in the east and south.The spatial distribution of China's IRI covers a multitude of provinces and obtains differ-ent scales of investment in different countries(regions).2)The spatial correlation between foreign investors and China's provincial-level administrative regions(PARs)forms a network,and the network of foreign-invested enterprises is more stable.Different countries(regions)have distinct location preferences in China,with significant spatial differences in correlation degrees.3)Overall,the interac-tion of these factors shapes the location decisions and correlation patterns of industrial robot enterprises.This study not only contributes to our theoretical knowledge of the industrial spatial structure and industrial economy but also offers valuable references and sugges-tions for national IMI planning and relevant industry investors.展开更多
Recently,the implementation of Industry 4.0 has become a new tendency,and it brings both opportunities and challenges to worldwide manufacturing companies.Thus,many manufacturing companies are attempting to find advan...Recently,the implementation of Industry 4.0 has become a new tendency,and it brings both opportunities and challenges to worldwide manufacturing companies.Thus,many manufacturing companies are attempting to find advanced technologies to launch intelligent manufacturing transformation.In this study,we propose a new model to measure the intelligent manufacturing readiness for the process industry,which aims to guide companies in recognizing their current stage and short slabs when carrying out intelligent manufacturing transformation.Although some models have already been reported to measure Industry 4.0 readiness and maturity,there are no models that are aimed at the process industry.This newly proposed model has six levels to describe different development stages for intelligent manufacturing.In addition,the model consists of four races,nine species,and 25 domains that are relevant to the essential businesses of companies’daily operation and capability requirements of intelligent manufacturing.Furthermore,these 25 domains are divided into 249 characteristic items to evaluate the manufacturing readiness in detail.A questionnaire is also designed based on the proposed model to help process-industry companies easily carry out self-diagnosis.Using the new method,a case including 196 real-world process-industry companies is evaluated to introduce the method of how to use the proposed model.Overall,the proposed model provides a new way to assess the degree of intelligent manufacturing readiness for process-industry companies.展开更多
Based on research into the applications of artificial intelligence （AI） technology in the manufacturing industry in recent years, we analyze the rapid development of core technologies in the new era of＇Internet plu...Based on research into the applications of artificial intelligence （AI） technology in the manufacturing industry in recent years, we analyze the rapid development of core technologies in the new era of＇Internet plus AI＇, which is triggering a great change in the models, means, and ecosystems of the manufacturing industry, as well as in the development of AI. We then propose new models, means, and forms of intelligent manufacturing, intelligent manufacturing system architecture, and intelligent man- ufactudng technology system, based on the integration of AI technology with information communications, manufacturing, and related product technology. Moreover, from the perspectives of intelligent manufacturing application technology, industry, and application demonstration, the current development in intelligent manufacturing is discussed. Finally, suggestions for the application of AI in intelligent manufacturing in China are presented.展开更多
As the next-generation manufacturing system,intelligent manufacturing enables better quality,higher productivity,lower cost,and increased manufacturing flexibility.The concept of sustainability is receiving increasing...As the next-generation manufacturing system,intelligent manufacturing enables better quality,higher productivity,lower cost,and increased manufacturing flexibility.The concept of sustainability is receiving increasing attention,and sustainable manufacturing is evolving.The digital twin is an emerging technology used in intelligent manufacturing that can grasp the state of intelligent manufacturing systems in real-time and predict system failures.Sustainable intelligent manufacturing based on a digital twin has advantages in practical applications.To fully understand the intelligent manufacturing that provides the digital twin,this study reviews both technologies and discusses the sustainability of intelligent manufacturing.Firstly,the relevant content of intelligent manufacturing,including intelligent manufacturing equipment,systems,and services,is analyzed.In addition,thesustainability of intelligent manufacturing is discussed.Subsequently,a digital twin and its application are introduced along with the development of intelligent manufacturing based on the digital twin technology.Finally,combined with the current status,the future development direction of intelligent manufacturing is presented.展开更多
Correctly coding materials and identifying characters marked on materials are very important for steel manufacturing industry to realize informatization management and intelligent manufacturing. However, the steel pro...Correctly coding materials and identifying characters marked on materials are very important for steel manufacturing industry to realize informatization management and intelligent manufacturing. However, the steel products manufacturing is often in a high temperature environment, and there are a lot of material storage and retrieval processes, workers are not easily close to the environment and complete tasks, so it is a great challenge to automatically mark and identify characters on the steel products. This paper presents a framework of label characters marking and management for steel materials, furthermore, a kind of marked characters online detection and tracking method has been provided based on machine vision. In addition, some experiments have been done in BaoSteel to mark characters on hot billets and recognize them in multi situations, and the results show that the proposed method is practical, and has provided a helpful exploration in obtaining accurate fundamental data for the intelligent manufacturing system in steelworks.展开更多
基金supported by the Natural Science Foundation of Heilongjiang Province(Grant Number:LH2021F002).
文摘With the advent of Industry 4.0,marked by a surge in intelligent manufacturing,advanced sensors embedded in smart factories now enable extensive data collection on equipment operation.The analysis of such data is pivotal for ensuring production safety,a critical factor in monitoring the health status of manufacturing apparatus.Conventional defect detection techniques,typically limited to specific scenarios,often require manual feature extraction,leading to inefficiencies and limited versatility in the overall process.Our research presents an intelligent defect detection methodology that leverages deep learning techniques to automate feature extraction and defect localization processes.Our proposed approach encompasses a suite of components:the high-level feature learning block(HLFLB),the multi-scale feature learning block(MSFLB),and a dynamic adaptive fusion block(DAFB),working in tandem to extract meticulously and synergistically aggregate defect-related characteristics across various scales and hierarchical levels.We have conducted validation of the proposed method using datasets derived from gearbox and bearing assessments.The empirical outcomes underscore the superior defect detection capability of our approach.It demonstrates consistently high performance across diverse datasets and possesses the accuracy required to categorize defects,taking into account their specific locations and the extent of damage,proving the method’s effectiveness and reliability in identifying defects in industrial components.
文摘In the context of intelligent manufacturing,machine tools,as core equipment,directly influence production efficiency and product quality through their operational reliability.Traditional maintenance methods for machine tools,often characterized by low efficiency and high costs,fail to meet the demands of modern manufacturing industries.Therefore,leveraging intelligent manufacturing technologies,this paper proposes a solution optimized for the diagnosis and maintenance of machine tool faults.Initially,the paper introduces sensor-based data acquisition technologies combined with big data analytics and machine learning algorithms to achieve intelligent fault diagnosis of machine tools.Subsequently,it discusses predictive maintenance strategies by establishing an optimized model for maintenance strategy and resource allocation,thereby enhancing maintenance efficiency and reducing costs.Lastly,the paper explores the architectural design,integration,and testing evaluation methods of intelligent manufacturing systems.The study indicates that optimization of machine tool fault diagnosis and maintenance in an intelligent manufacturing environment not only enhances equipment reliability but also significantly reduces maintenance costs,offering broad application prospects.
基金2023 Autonomous Region Level College Students Innovation and Entrepreneurship Training Plan Project:Virtual and Real Integration of Industrial Robot Training Room(Project number:S202311546109)2023 Industry-University Cooperative Education Project of the Ministry of Education(Project number:230801212255027)+2 种基金2023 Guangxi Higher Education Undergraduate Teaching Reform Project:Research and Practice of Mixed Teaching Reform of Industrial Robot Operation and Programming Based on the Integration of Industry and Education(Project number:2023JGA362)2022 Guangxi Vocational Education Teaching Reform Research Project:Construction and Practice of Integrated Curriculum Resources for Robot Engineering Majors Based on Industrial College(Project number:GXGZJG2022B076)2022 Guangxi Science and Technology Normal University Research Fund Project:Path Planning of Loading and Unloading Robot based on Optimal Energy Consumption(Project number:GXKS2022QN006).
文摘In this paper,we built a robot training platform using virtual simulation software,and the robot assembly,handling,and palletizing were realized.The workstation includes an industrial robot,gas control unit,track function module,assembly function module,palletizing function module,vision module,etc.,and robot movement is achieved through language programming.The platform provides conditions for the practical ability training of application-oriented talents.
基金This work was supported by the 2022 China University of Geosciences(Beijing)Disciplinary Development Research Fund Project“Research on Intelligent Manufacturing Talents Training Method Based on Interdisciplinary Integration”(Project Number:2022XK104).
文摘As a significant field of research under the 14th Five Year Plan,intelligent manufacturing holds a special position in industrial upgrading and core technology independence.Intelligent manufacturing is an important support for building a“scientific and technological power”in the future.Cultivating creative interdisciplinary talents who can adapt to the development of intelligent manufacturing industry in the new era is of great significance for China to realize the modernization and autonomy of its industrial system.In view of the existing gap between the interdisciplinary talent training of intelligent manufacturing and the actual needs of the industry,this paper focuses on the concept of interdisciplinary construction.Based on the cycle improvement of professional construction,a curriculum system that integrates the interaction of interconnected projects is established.Through four specific measures,namely establishing the collaborative development system of intelligent manufacturing specialty,building a cycle diagnosis and reform system of intelligent manufacturing specialty,improving the curriculum system of integrated modular specialty group,and setting up an application scenario project-based curriculum,we hope to provide reference for the cross-training of compound intelligent manufacturing talents.
文摘Our next generation of industry-lndustry 4.0-holds the promise of increased flexibility in manufacturing, along with mass customization, better quality, and improved productivity. It thus enables companies to cope with the challenges of producing increasingly individualized products with a short lead-time to market and higher quality. Intelligent manufacturing plays an important role in Industry 4.0. Typical resources are converted into intelligent objects so that they are able to sense, act, and behave within a smart environment. In order to fully understand intelligent manufacturing in the context of Industry 4.0, this paper provides a comprehensive review of associated topics such as intelligent manufacturing, Internet of Things （IoT）- enabled manufacturing, and cloud manufacturing. Similarities and differences in these topics are highlighted based on our analysis. We also review key technologies such as the loT, cyber-physical systems （CPSs）, cloud computing, big data analytics （BDA）, and information and communications technology （ICT） that are used to enable intelligent manufacturing. Next, we describe worldwide movements in intelligent manufacturing, including governmental strategic plans from different countries and strategic plans from major international companies in the European Union, United States, Japan, and China. Finally, we present current challenges and future research directions. The concepts discussed in this paper will spark new ideas in the effort to realize the much-anticipated Fourth Industrial Revolution.
文摘With ever-increasing market competition and advances in technology, more and more countries are prioritizing advanced manufacturing technology as their top priority for economic growth. Germany announced the Industry 4.0 strategy in 2013. The US government launched the Advanced Manufacturing Partnership （AMP） in 2011 and the National Network for Manufacturing Innovation （NNMI） in 2014. Most recently, the Manufacturing USA initiative was officially rolled out to further ＂leverage existing resources... to nurture manufacturing innovation and accelerate commercialization＂ by fostering close collaboration between industry, academia, and government partners. In 2015, the Chinese government officially published a 10- year plan and roadmap toward manufacturing： Made in China 2025. In all these national initiatives, the core technology development and implementation is in the area of advanced manufacturing systems. A new manufacturing paradigm is emerging, which can be characterized by two unique features： integrated manufacturing and intelligent manufacturing. This trend is in line with the progress of industrial revolutions, in which higher efficiency in production systems is being continuously pursued. To this end, 10 major technologies can be identified for the new manufacturing paradigm. This paper describes the rationales and needs for integrated and intelligent manufacturing （i2M） systems. Related technologies from different fields are also described. In particular, key technological enablers, such as the Intemet of Things and Services （IoTS）, cyber-physical systems （CPSs）, and cloud computing are discussed. Challenges are addressed with applica- tions that are based on commercially available platforms such as General Electric （GE）＇s Predix and PTC＇s ThingWorx.
基金supported by the International Postdoctoral Exchange Fellowship Program(20180025)National Natural Science Foundation of China(51703180)+2 种基金China Postdoctoral Science Foundation(2018M630191,2017M610634)Shaanxi Postdoctoral Science Foundation(2017BSHEDZZ73)Fundamental Research Funds for the Central Universities(xpt012020006,xjj2017024).
文摘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.
基金This research was supported by the National Natural Science Foundation of China(61991400,61991403,and 61991404)China Institute of Engineering Consulting Research Project(2019-ZD-12)the 2020 Science and Technology Major Project of Liaoning Province(2020JH1/10100008),China.
文摘Based on the analysis of the characteristics and operation status of the process industry,as well as the development of the global intelligent manufacturing industry,a new mode of intelligent manufacturing for the process industry,namely,deep integration of industrial artificial intelligence and the Industrial Internet with the process industry,is proposed.This paper analyzes the development status of the existing three-tier structure of the process industry,which consists of the enterprise resource planning,the manufacturing execution system,and the process control system,and examines the decision-making,control,and operation management adopted by process enterprises.Based on this analysis,it then describes the meaning of an intelligent manufacturing framework and presents a vision of an intelligent optimal decision-making system based on human–machine cooperation and an intelligent autonomous control system.Finally,this paper analyzes the scientific challenges and key technologies that are crucial for the successful deployment of intelligent manufacturing in the process industry.
基金the International Postdoctoral Exchange Fellowship Program(20180025).
文摘Pandemics like COVID-19 have created a spreading and ever-higher healthy threat to the humans in the manufacturing system which incurs severe disruptions and complex issues to industrial networks.The intelligent manufacturing(IM)systems are promising to create a safe working environment by using the automated manufacturing assets which are monitored by the networked sensors and controlled by the intelligent decision-making algorithms.The relief of the production disruption by IM technologies facilitates the reconnection of the good and service flows in the network,which mitigates the severity of industrial chain disruption.In this study,we create a novel intelligent manufacturing framework for the production recovery under the pandemic and build an assessment model to evaluate the impacts of the IM technologies on industrial networks.Considering the constraints of the IM resources,we formulate an optimization model to schedule the allocation of IM resources according to the mutual market demands and the severity of the pandemic.
基金The authors gratefully acknowledge the National Natural Science Foundation of China for its financial support(U1462206).
文摘Applications of process systems engineering(PSE)in plants and enterprises are boosting industrial reform from automation to digitization and intelligence.For ethylene thermal cracking,knowledge expression,numerical modeling and intelligent optimization are key steps for intelligent manufacturing.This paper provides an overview of progress and contributions to the PSE-aided production of thermal cracking;introduces the frameworks,methods and algorithms that have been proposed over the past10 years and discusses the advantages,limitations and applications in industrial practice.An entire set of molecular-level modeling approaches from feedstocks to products,including feedstock molecular reconstruction,reaction-network auto-generation and cracking unit simulation are described.Multilevel control and optimization methods are exhibited,including at the operational,cycle,plant and enterprise level.Relevant software packages are introduced.Finally,an outlook in terms of future directions is presented.
基金supported by Fundamental Research Funds for the Central Universities (No. N090403005)
文摘Agile intelligent manufacturing is one of the new manufacturing paradigms that adapt to the fierce globalizing market competition and meet the survival needs of the enterprises, in which the management and control of the production system have surpassed the scope of individual enterprise and embodied some new features including complexity, dynamicity, distributivity, and compatibility. The agile intelligent manufacturing paradigm calls for a production scheduling system that can support the cooperation among various production sectors, the distribution of various resources to achieve rational organization, scheduling and management of production activities. This paper uses multi-agents technology to build an agile intelligent manufacturing-oriented production scheduling system. Using the hybrid modeling method, the resources and functions of production system are encapsulated, and the agent-based production system model is established. A production scheduling-oriented multi-agents architecture is constructed and a multi-agents reference model is given in this paper.
文摘Based on production data and metallurgical theory, this study discusses the control of inclusions in Baosteel’s products from the perspective of three key processes: converting, refining, and continuous casting(CC).The control of converter slagging, refining oxygen blowing(OB),and CC are considered.Based on metallurgical theory, this research investigates the effects of refining OB decarburization and argon blowing in a tundish on the oxygen content of molten steel.Combining theory and practice is beneficial to the discovery of new ways to tackle existing problems and the development of intelligent manufacturing.
基金supported by 2019 Project of the 13th Five-year Plan of Fujian Education and Science(FJJKCGZ19-016)。
文摘NC machining of parts and components is developing in the direction of automation and intelligence.In addition,the automatic production line puts forward higher requirements for the overall layout,technological process and production tempo.The layout and simulation of the production line can more directly reflect the problems that may occur in the operation of the production line,and solve these problems through scientific methods.This project uses VisualOne software to simulate the operation of the intelligent manufacturing production line,which can directly find the existing problems in the layout of the production line and the process of the craftsmen,and optimize the layout of the production line.In addition,it can provide reliable support for the equipment procurement and on-site construction of the intelligent manufacturing automation production line in the later stage.
文摘The 5th Meeting of Sino-German Standardization Sub-working Group on Intelligent Manufacturing/Industry 4.0 was held in Hangzhou,capital of east China’s Zhejiang province from December 3 to 4,2017,bringing together over 90 representatives of both countries.
文摘The advancement of intelligent manufacturing in Dongguan puts forward new requirements for industrial talents,and the development of new productivity is bound to force enterprises and employees to make adaptive adjustments.The upgrading of intelligent manufacturing is not only the upgrading of intelligent machines but also the upgrading of the human brain,which includes the reshaping and cultivation of industrial talents.Based on field research,this study analyzes the different characteristics of the traditional and the new intelligent manufacturing model,as well as summarizes the characteristics of industrial talents and the changing trend of talent demand in view of the intelligent manufacturing model in Dongguan.
基金Under the auspices of the Natural Science Foundation Project of Heilongjiang Province(No.LH2019D009)。
文摘The advancement of the intelligent manufacturing industry(IMI)represents the future direction for the world's manufactur-ing sector,offering a promising avenue to bolster national competitiveness and enhance industrial manufacturing efficiency.In this study,we took the industrial robot industry(IRI)as a case study to elucidate the spatial distribution and interconnections of IMI from a geographical perspective,and the modified diamond model(DM)was used to analyze the influencing factors.Results show that:1)the spatial pattern of IRI with various investment attributes in different industrial chain links is generally similar,centered in the southeast.Key investment areas are in the east and south.The spatial distribution of China's IRI covers a multitude of provinces and obtains differ-ent scales of investment in different countries(regions).2)The spatial correlation between foreign investors and China's provincial-level administrative regions(PARs)forms a network,and the network of foreign-invested enterprises is more stable.Different countries(regions)have distinct location preferences in China,with significant spatial differences in correlation degrees.3)Overall,the interac-tion of these factors shapes the location decisions and correlation patterns of industrial robot enterprises.This study not only contributes to our theoretical knowledge of the industrial spatial structure and industrial economy but also offers valuable references and sugges-tions for national IMI planning and relevant industry investors.
基金Project supported by the National Key Research and Development Program of China(No.2019YFB1705004)。
文摘Recently,the implementation of Industry 4.0 has become a new tendency,and it brings both opportunities and challenges to worldwide manufacturing companies.Thus,many manufacturing companies are attempting to find advanced technologies to launch intelligent manufacturing transformation.In this study,we propose a new model to measure the intelligent manufacturing readiness for the process industry,which aims to guide companies in recognizing their current stage and short slabs when carrying out intelligent manufacturing transformation.Although some models have already been reported to measure Industry 4.0 readiness and maturity,there are no models that are aimed at the process industry.This newly proposed model has six levels to describe different development stages for intelligent manufacturing.In addition,the model consists of four races,nine species,and 25 domains that are relevant to the essential businesses of companies’daily operation and capability requirements of intelligent manufacturing.Furthermore,these 25 domains are divided into 249 characteristic items to evaluate the manufacturing readiness in detail.A questionnaire is also designed based on the proposed model to help process-industry companies easily carry out self-diagnosis.Using the new method,a case including 196 real-world process-industry companies is evaluated to introduce the method of how to use the proposed model.Overall,the proposed model provides a new way to assess the degree of intelligent manufacturing readiness for process-industry companies.
文摘Based on research into the applications of artificial intelligence （AI） technology in the manufacturing industry in recent years, we analyze the rapid development of core technologies in the new era of＇Internet plus AI＇, which is triggering a great change in the models, means, and ecosystems of the manufacturing industry, as well as in the development of AI. We then propose new models, means, and forms of intelligent manufacturing, intelligent manufacturing system architecture, and intelligent man- ufactudng technology system, based on the integration of AI technology with information communications, manufacturing, and related product technology. Moreover, from the perspectives of intelligent manufacturing application technology, industry, and application demonstration, the current development in intelligent manufacturing is discussed. Finally, suggestions for the application of AI in intelligent manufacturing in China are presented.
基金supported by the National Natural Science Foundation of China(Grant No.51675319)Shanghai Science and Technology Committee Project(Grant No.19511104702).
文摘As the next-generation manufacturing system,intelligent manufacturing enables better quality,higher productivity,lower cost,and increased manufacturing flexibility.The concept of sustainability is receiving increasing attention,and sustainable manufacturing is evolving.The digital twin is an emerging technology used in intelligent manufacturing that can grasp the state of intelligent manufacturing systems in real-time and predict system failures.Sustainable intelligent manufacturing based on a digital twin has advantages in practical applications.To fully understand the intelligent manufacturing that provides the digital twin,this study reviews both technologies and discusses the sustainability of intelligent manufacturing.Firstly,the relevant content of intelligent manufacturing,including intelligent manufacturing equipment,systems,and services,is analyzed.In addition,thesustainability of intelligent manufacturing is discussed.Subsequently,a digital twin and its application are introduced along with the development of intelligent manufacturing based on the digital twin technology.Finally,combined with the current status,the future development direction of intelligent manufacturing is presented.
基金supported by the National Natural Science Foundation of China(Grant Nos.51075252,61101177).
文摘Correctly coding materials and identifying characters marked on materials are very important for steel manufacturing industry to realize informatization management and intelligent manufacturing. However, the steel products manufacturing is often in a high temperature environment, and there are a lot of material storage and retrieval processes, workers are not easily close to the environment and complete tasks, so it is a great challenge to automatically mark and identify characters on the steel products. This paper presents a framework of label characters marking and management for steel materials, furthermore, a kind of marked characters online detection and tracking method has been provided based on machine vision. In addition, some experiments have been done in BaoSteel to mark characters on hot billets and recognize them in multi situations, and the results show that the proposed method is practical, and has provided a helpful exploration in obtaining accurate fundamental data for the intelligent manufacturing system in steelworks.
