Human Cyber Physical Systems(HCPSs)in the Context of New-Generation Intelligent Manufacturing

An intelligent manufacturing system is a composite intelligent system comprising humans,cyber systems,and physical systems with the aim of achieving specific manufacturing goals at an optimized level.This kind of intelligent system is called a human-cyber-physical system(HCPS).In terms of technology,HCPSs can both reveal technological principles and form the technological architecture for intelligent manufacturing.It can be concluded that the essence of intelligent manufacturing is to design,construct,and apply HCPSs in various cases and at different levels.With advances in information technology,intelligent manufacturing has passed through the stages of digital manufacturing and digital-networked manufacturing,and is evolving toward new-generation intelligent manufacturing(NGIM).NGIM is characterized by the in-depth integration of new-generation artificial intelligence(AI)technology(i.e.,enabling technology)with advanced manufacturing technology(i.e.,root technology);it is the core driving force of the new industrial revolution.In this study,the evolutionary footprint of intelligent manufacturing is reviewed from the perspective of HCPSs,and the implications,characteristics,technical frame,and key technologies of HCPSs for NGIM are then discussed in depth.Finally,an outlook of the major challenges of HCPSs for NGIM is proposed.

Smart Manufacturing and Intelligent Manufacturing:A Comparative Review

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.

Upgrading Pathways of Intelligent Manufacturing in China:Transitioning across Technological Paradigms

Intelligent technologies are leading to the next wave of industrial revolution in manufacturing.In developed economies,firms are embracing these advanced technologies following a sequential upgrading strategy-from digital manufacturing to smart manufacturing(digital-networked),and then to newgeneration intelligent manufacturing paradigms.However,Chinese firms face a different scenario.On the one hand,they have diverse technological bases that vary from low-end electrified machinery to leading-edge digital-network technologies;thus,they may not follow an identical upgrading pathway.On the other hand,Chinese firms aim to rapidly catch up and transition from technology followers to probable frontrunners;thus,the turbulences in the transitioning phase may trigger a precious opportunity for leapfrogging,if Chinese manufacturers can swiftly acquire domain expertise through the adoption of intelligent manufacturing technologies.This study addresses the following question by conducting multiple case studies:Can Chinese firms upgrade intelligent manufacturing through different pathways than the sequential one followed in developed economies?The data sources include semistructured interviews and archival data.This study finds that Chinese manufacturing firms have a variety of pathways to transition across the three technological paradigms of intelligent manufacturing in nonconsecutive ways.This finding implies that Chinese firms may strategize their own upgrading pathways toward intelligent manufacturing according to their capabilities and industrial specifics;furthermore,this finding can be extended to other catching-up economies.This paper provides a strategic roadmap as an explanatory guide to manufacturing firms,policymakers,and investors.

Opportunities and Challenges of Artificial Intelligence for Green Manufacturing in the Process Industry

Smart manufacturing is critical in improving the quality of the process industry. In smart manufacturing, there is a trend to incorporate different kinds of new-generation information technologies into process- safety analysis. At present, green manufacturing is facing major obstacles related to safety management, due to the usage of large amounts of hazardous chemicals, resulting in spatial inhomogeneity of chemical industrial processes and increasingly stringent safety and environmental regulations. Emerging informa- tion technologies such as arti cial intelligence (AI) are quite promising as a means of overcoming these dif culties. Based on state-of-the-art AI methods and the complex safety relations in the process industry, we identify and discuss several technical challenges associated with process safety: ① knowledge acquisition with scarce labels for process safety;② knowledge-based reasoning for process safety;③ accurate fusion of heterogeneous data from various sources;and ④ effective learning for dynamic risk assessment and aided decision-making. Current and future works are also discussed in this context.

AI-Based Modeling and Data-Driven Evaluation for Smart Manufacturing Processes

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.

Smart manufacturing of nonferrous metallurgical processes:Review and perspectives

The nonferrous metallurgical(NFM)industry is a cornerstone industry for a nation’s economy.With the development of artificial technologies and high requirements on environment protection,product quality,and production efficiency,the importance of applying smart manufacturing technologies to comprehensively percept production states and intelligently optimize process operations is becoming widely recognized by the industry.As a brief summary of the smart and optimal manufacturing of the NFM industry,this paper first reviews the research progress on some key facets of the operational optimization of NFM processes,including production and management,blending optimization,modeling,process monitoring,optimization,and control.Then,it illustrates the perspectives of smart and optimal manufacturing of the NFM industry.Finally,it discusses the major research directions and challenges of smart and optimal manufacturing for the NFM industry.This paper will lay a foundation for the realization of smart and optimal manufacturing in nonferrous metallurgy in the future.

Toward Intelligent Machine Tool

With the development of modern information technology-and particularly of the new generation of artificial intelligence(AI)technology-new opportunities are available for the development of the intelligent machine tool(IMT).Based on the three classical paradigms of intelligent manufacturing as defined by the Chinese Academy of Engineering,the concept,characteristics,and systemic structure of the IMT are presented in this paper.Three stages of machine tool evolution-from the manually operated machine tool(MOMT)to the IMT-are discussed,including the numerical control machine tool(NCMT),the smart machine tool(SMT),and the IMT.Furthermore,the four intelligent control principles of the IMT-namely,autonomous sensing and connection,autonomous learning and modeling,autonomous optimization and decision-making,and autonomous control and execution-are presented in detail.This paper then points out that the essential characteristic of the IMT is to acquire and accumulate knowledge through learning,and presents original key enabling technologies,including the instruction-domain-based analytical approach,theoretical and big-data-based hybrid modeling technology,and the double-code control method.Based on this research,an intelligent numerical control(INC)system and industrial prototypes of IMTs are developed.Three intelligent practices are conducted,demonstrating that the integration of the new generation of AI technology with advanced manufacturing technology is a feasible and convenient way to advance machine tools toward the IMT.

Towards Long Lifetime Battery:AI-Based Manufacturing and Management

Technologies that accelerate the delivery of reliable battery-based energy storage will not only contribute to decarbonization such as transportation electrification,smart grid,but also strengthen the battery supply chain.As battery inevitably ages with time,losing its capacity to store charge and deliver it efficiently.This directly affects battery safety and efficiency,making related health management necessary.Recent advancements in automation science and engineering raised interest in AI-based solutions to prolong battery lifetime from both manufacturing and management perspectives.This paper aims at presenting a critical review of the state-of-the-art AI-based manufacturing and management strategies towards long lifetime battery.First,AI-based battery manufacturing and smart battery to benefit battery health are showcased.Then the most adopted AI solutions for battery life diagnostic including state-of-health estimation and ageing prediction are reviewed with a discussion of their advantages and drawbacks.Efforts through designing suitable AI solutions to enhance battery longevity are also presented.Finally,the main challenges involved and potential strategies in this field are suggested.This work will inform insights into the feasible,advanced AI for the health-conscious manufacturing,control and optimization of battery on different technology readiness levels.

基于生成式AI的智能制造专业型应用框架研究与应用

智能制造是全面实现现代化的重要保障。随着人工智能技术的不断发展,如何利用生成式AI进一步促进智能制造质量与效率的提升是十分重要的研究课题。本文按照智能制造生命周期维度划分,结合生成式AI的基本思想和原理,提出了基于生成式AI的智能制造专业型应用框架(SMPF-GAI),并通过案例分析了生成式AI在智能制造中的应用潜力。该框架系统地整合了智能制造的关键阶段与生成式AI的核心原则,描绘了生成式AI在智能制造中的运行机理和应用路线。本研究为智能制造领域的研究人员、从业者和决策者提供了新的见解,为有效开展智能制造工作、提高智能制造建设水平提供理论支撑及方法指导。

智慧工厂数字孪生的设计与实现

以某典型的离散制造智慧工厂实验平台为对象,分析智慧工厂的系统组成。运用Flexsim三维建模仿真平台,建立智慧工厂的三维虚拟仿真模型;运用Socket通讯机制,建立智慧工厂物理模型和虚拟仿真模型之间的信息交互机制,实现智慧工厂物理模型和虚拟模型之间的实时双向互动,构建智慧工厂的数字孪生系统,为建立数字孪生提供有益的指导和借鉴。

基于智慧工厂模式的智能控制技术专业群综合实践平台建设

介绍校企合作建设智能控制技术专业群综合实践平台的方法和措施,平台以墨水瓶自动化生产线为基础,融合了工业机器人技术、PLC技术、智能相机技术、信息软件技术、机械设计技术、虚拟现实技术、工业互联网技术等,按照智慧工厂模式进行设计,体现了智能制造专业知识交叉融合的特点。平台的开发建设能够解决专业群面临的设备不足、课程内容与工厂实际脱节、综合应用能力培养方式欠缺等造成人才培养质量不高的诸多问题,为专业群复合型人才的培养提供了良好的实践条件。

预拌混凝土生产中智能感知技术的探索研究

目前预拌混凝土生产过程中原材料质量波动大、影响因素多,现有原材料和混合物料的质量检测/监测手段存在过程迟缓、检测结果严重滞后等问题,无法根据生产过程中物料变化进行及时干预或修正,难以保障混凝土质量的稳定性。因此,建立以原材料和混合料主要性质参数快速感知、在线监测和即时反馈的混凝土智能感知和调控体系势在必行。

烧结系统智能制造的发展现状与展望

为提升烧结工艺的智能制造水平,本文以大数据为核心线索,全面探讨了参数预测、图像识别、过程控制和系统平台等在烧结工艺中的应用。工艺参数预测有效提高了生产效率,保障了烧结矿质量;图像识别技术赋予机器“感知”能力,使其能够科学有效地对信息进行规划分类;烧结智能控制系统的水分、配料和终点控制应用显著提高了生产效率和产品质量,为烧结工艺智能化发展和钢铁工业可持续进步奠定了坚实基础;烧结大数据平台的实时数据分析确保数据的实时性与有效性,为企业烧结生产提供科学依据,提高了精准度和运行速度。在大数据的引导下,烧结工艺的智能制造水平得到显著提升,企业生产效率和烧结矿质量均得到有效保证。智能控制技术的快速发展使得钢铁工业迈向智能制造的转型升级成为必然趋势。

有源射频识别技术在物联网中的应用研究

有源射频识别(Radio Frequency Identification,RFID)技术作为一种高效、准确的自动识别技术,已经在供应链管理、库存控制、零售业等领域得到了广泛应用。通过对有源RFID技术进行全面分析,首先介绍了RFID技术的基本原理、系统组成以及标签和阅读器的工作原理;其次重点分析了有源RFID技术的关键技术,包括标签结构和原理、电源供应、通信协议,以及数据处理与存储;最后探讨了有源RFID技术在智能物流、智能制造、智慧城市等领域中的应用。

智能电表运营排产管理系统

目前电表从研发、生产到售后存在衔接不紧密、数据断层、审核缓慢和排产计划不合理等问题。针对该类实际问题,设计了一个集生产、设计、销售、智能生产排产和售后等功能于一体的综合性管理系统。应用信息化技术提高信息及时性,实现线上审批和信息共享,达到提升生产效率、提高产品质量的目的。所提出的研究成果对电表管理具有实际意义,对电表行业信息化发展具有参考价值,并为相关领域的研究提供一定的理论和实践基础。

纺织工业智能发展现状与展望

为全面了解人工智能技术在纺织工业的发展及应用情况,探究未来智能化发展的任务与目标,基于国内外纺织工业在数字化、网络化、智能化领域的最新发展现状,结合纺织行业的相关需求,分析了当前面临的技术挑战,总结了工业大数据、数字孪生、工业机器人、机器视觉和智能排产调度等当前纺织行业亟需的关键技术;介绍了全流程智能纺织生产线、纺织装备智能运维、纺织品智能检测等典型应用案例和生产模式。最后,总结了我国纺织工业智能化发展进程中仍待突破的核心技术及产业生态的发展方向,提出了发展新一代纺织智能制造系统,打造全产业链协同的纺织智能生态的两点思考与展望,为我国纺织工业智能化发展提供案例参考与技术指引。

汽车智能制造中的工业物联网技术

在工业4.0的背景下,汽车制造业正向着网络化、智能化的方向转型和发展,工业物联网是实现汽车智能制造的核心技术。本文阐述了汽车智能制造对工业物联网的要求,介绍了工业物联网的主要网络结构,研究了汽车制造物联网中的核心技术,并对今后的研究方向做了深入的探讨。

Data-driven engineering framework with AI algorithm of Ginkgo Folium tablets manufacturing

Smart manufacturing still remains critical challenges for pharmaceutical manufacturing.Here,an original data-driven engineering framework was proposed to tackle the challenges.Firstly,from sporadic indicators to five kinds of systematic quality characteristics,nearly 2,000,000 real-world data points were successively characterized from Ginkgo Folium tablet manufacturing.Then,from simplex to the multivariate system,the digital process capability diagnosis strategy was proposed by multivariate C_(pk)integrated Bootstrap-t.The C_(pk)of Ginkgo Folium extracts,granules,and tablets were discovered,which was 0.59,0.42,and 0.78,respectively,indicating a relatively weak process capability,especially in granulating.Furthermore,the quality traceability was discovered from unit to end-to-end analysis,which decreased from 2.17 to 1.73.This further proved that attention should be paid to granulating to improve the quality characteristic.In conclusion,this paper provided a data-driven engineering strategy empowering industrial innovation to face the challenge of smart pharmaceutical manufacturing.

智能机器人监测系统的设计研究

智能机器人具有工作效率高、反应灵敏精准、工作时间长等优势,而很多可视化监测和个性化定制的动作与服务地方特色产业是目前大力推广的一项人工智能技术。大量相关数据表明,相较于现在的志愿者服务和技术水平,机器人具有更加便捷、多功能、减少人力消耗等特点。在机器人的不断发展下,文章研究和讨论了一款智能机器人监测系统的设计。