中小制造企业发展先进制造技术现状及对策研究

制造技术创新是制造企业生存和发展的前提和保障，结合中国国情，具体分析中国中小制造企业运用先进制造技术的现状，从宏观和微观层面提出了促进中小制造企业技术创新，发展运用先进制造技术的方法和措施。     

Toward New-Generation Intelligent Manufacturing

Intelligent manufacturing is a general concept that is under continuous development. It can be categorized into three basic paradigms: digital manufacturing, digital-networked manufacturing, and new-generation intelligent manufacturing. New-generation intelligent manufacturing represents an in-depth integration of new-generation artificial intelligence (AI) technology and advanced manufacturing technology. It runs through every link in the full life-cycle of design, production, product, and service. The concept also relates to the optimization and integration of corresponding systems; the continuous improvement of enterprises’ product quality, performance, and service levels; and reduction in resources consumption. New-generation intelligent manufacturing acts as the core driving force of the new industrial revolution and will continue to be the main pathway for the transformation and upgrading of the manufacturing industry in the decades to come. Human-cyber-physical systems (HCPSs) reveal the technological mechanisms of new-generation intelligent manufacturing and can effectively guide related theoretical research and engineering practice. Given the sequential development, cross interaction, and iterative upgrading characteristics of the three basic paradigms of intelligent manufacturing, a technology roadmap for “parallel promotion and integrated development” should be developed in order to drive forward the intelligent transformation of the manufacturing industry in China.     

复杂系统（产品）集成制造工程的技术研究与应用

基于现代集成制造的理念、方法、技术、工具,结合实践,提出并研究了一种能改善复杂产品研发时间（T）、质量（Q）、成本（C）、服务（S）的系统工程——称为“复杂系统（产品）集成制造工程”（简称COSIME）。阐述了它的内涵、系统框架和技术体系,并给出了其中具有特点的6类关键技术的阶段研究成果,包括基于人制管理及有关先进制造模式的复杂产品集成制造系统的经营管理模式;基于项目管理理念的异地企业间并行工程方法与技术;复杂产品虚拟样机工程方法与技术;基于分布仿真技术的复杂产品概念设计与性能评估系统;复杂产品质量控制与     

The Future of Manufacturing: A New Perspective

Many articles have been published on intelligent manufacturing, most of which focus on hardware, software, additive manufacturing, robotics, the Internet of Things, and Industry 4.0. This paper provides a different perspective by examining relevant challenges and providing examples of some less-talked-about yet essential topics, such as hybrid systems, redefining advanced manufacturing, basic building blocks of new manufacturing, ecosystem readiness, and technology scalability. The first major challenge is to (re-)define what the manufacturing of the future will be, if we wish to: ① raise public awareness of new manufacturing’s economic and societal impacts, and ② garner the unequivocal support of policy-makers. The second major challenge is to recognize that manufacturing in the future will consist of systems of hybrid systems of human and robotic operators; additive and subtractive processes; metal and composite materials; and cyber and physical systems. Therefore, studying the interfaces between constituencies and standards becomes important and essential. The third challenge is to develop a common framework in which the technology, manufacturing business case, and ecosystem readiness can be evaluated concurrently in order to shorten the time it takes for products to reach customers. Integral to this is having accepted measures of “scalability” of non-information technologies. The last, but not least, challenge is to examine successful modalities of industry–academia–government collaborations through public–private partnerships. This article discusses these challenges in detail.     

Design for manufacturing and assembly/disassembly: joint design of products and production systems

Design for Manufacturing, Assembly, and Disassembly is important in today’s production systems because if this aspect is not considered, it could lead to inefficient operations and excessive material usage, both of which have a significant impact on manufacturing cost and time. Attention to this topic is important in achieving the target standards of Industry 4.0 which is inclusive of material utilisation, manufacturing operations, machine utilisation, features selection of the products, and development of suitable interfaces with information communication technologies (ICT) and other evolving technologies. Design for manufacturing (DFM) and Design for Assembly (DFA) have been around since the 1980’s for rectifying and overcoming the difficulties and waste related to the manufacturing as well as assembly at the design stage. Furthermore, this domain includes a decision support system and knowledge base with manufacturing and design guidelines following the adoption of ICT. With this in mind, ‘Design for manufacturing and assembly/disassembly: Joint design of products and production systems’, a special issue has been conceived and its contents are elaborated in detail. In this paper, a background of the topics pertaining to DFM, DFA and related topics seen in today’s manufacturing systems are discussed. The accepted papers of this issue are categorised in multiple sections and their significant features are outlined.     

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.     

Cyber–Physical Production Systems for Data-Driven,Decentralized, and Secure Manufacturing—A Perspective

With the concepts of Industry 4.0 and smart manufacturing gaining popularity, there is a growing notion that conventional manufacturing will witness a transition toward a new paradigm, targeting innovation, automation, better response to customer needs, and intelligent systems. Within this context, this review focuses on the concept of cyber–physical production system (CPPS) and presents a holistic perspective on the role of the CPPS in three key and essential drivers of this transformation: data-driven manufacturing, decentralized manufacturing, and integrated blockchains for data security. The paper aims to connect these three aspects of smart manufacturing and proposes that through the application of data-driven modeling, CPPS will aid in transforming manufacturing to become more intuitive and automated. In turn, automated manufacturing will pave the way for the decentralization of manufacturing. Layering blockchain technologies on top of CPPS will ensure the reliability and security of data sharing and integration across decentralized systems. Each of these claims is supported by relevant case studies recently published in the literature and from the industry; a brief on existing challenges and the way forward is also provided.     

敏捷制造中基于AI及Internet技术的电火花加工工艺决策

以电火花加工为例提出了一种实现敏捷制造技术的新方法。这种方法基于人工智能及因特网技术与传统制造技术的集成，通过网络化电火花加工工艺智能决策系统的软件以低成本远程快速的使用设计制造专家知识，从而实现设计制造的敏捷化。专家知识包括电火花加工工艺参数的智能选择及推理。通过因特网选择不同应用的加工条件与专家进行动态交互。本文得出结论，基于人工智能技术及因特网的敏捷制造技术的未来应用具有潜在的利益。     

用系统的观点应用和发展先进制造技术

对先进制造技术的系统性特点进行了分析 ,认为随着先进制造技术开始发展成为多学科交叉融合一体化的新一代制造科学 ,在应用与发展先进制造技术不能孤立看待某一先进制造技术 ,不应以追求技术的高新为目的 ,必须用全面、整体和系统的观点及手段对其进行处理 ,协调系统内部的各个要素 ,追求总体优化 ,必须重视技术、组织、管理及人的综合 .先进制造技术必须以人为中心 ,其运作需要先进的制造模式相匹配     

Intelligent Manufacturing in the Context of Industry 4.0: A Review

Our next generation of industry—Industry 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 IoT, 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.     

Alternative production strategies based on the comparison of additive and traditional manufacturing technologies

Additive manufacturing technology has been evolving for several years. New material options, better processing speeds and greater autonomy are some of the characteristics of this technology that are still under research. However, in its current state, many commercially available 3D printers are competing with traditional manufacturing techniques in the fabrication of end-use products. In this paper, different additive manufacturing technologies are compared with injection moulding in a real-world case study. The comparison is conducted in terms of lead time and total production cost. From the case under study, it becomes obvious that none of the additive manufacturing technologies examined is yet able to practically replace injection moulding for medium- and high production volumes. However, when considering low-volume production, both rapid tooling and additive manufacturing may offer an alternative that could result into shorter lead times and decreased total production costs. In addition, the introduction of Additive Manufacturing in a producer’s production portfolio can increase flexibility, reduce warehousing costs and assist the company towards the adoption of a mass customisation business strategy.     

Knowledge-driven digital twin manufacturing cell towards intelligent manufacturing

Rapid advances in new generation information technologies, such as big data analytics, internet of things (IoT), edge computing and artificial intelligence, have nowadays driven traditional manufacturing all the way to intelligent manufacturing. Intelligent manufacturing is characterised by autonomy and self-optimisation, which proposes new demands such as learning and cognitive capacities for manufacturing cell, known as the minimum implementation unit for intelligent manufacturing. Consequently, this paper proposes a general framework for knowledge-driven digital twin manufacturing cell (KDTMC) towards intelligent manufacturing, which could support autonomous manufacturing by an intelligent perceiving, simulating, understanding, predicting, optimising and controlling strategy. Three key enabling technologies including digital twin model, dynamic knowledge bases and knowledge-based intelligent skills for supporting the above strategy are analysed, which equip KDTMC with the capacities of self-thinking, self-decision-making, self-execution and self-improving. The implementing methods of KDTMC are also introduced by a thus constructed test bed. Three application examples about intelligent process planning, intelligent production scheduling and production process analysis and dynamic regulation demonstrate the feasibility of KDTMC, which provides a practical insight into the intelligent manufacturing paradigm.     

An examination of the use of manufacturing technologies and performance implications in US plants with different export intensities

¹ 1. Current address for Cigdem Ataseven is Operations and Supply Chain Management Department, Monte Ahuja College of Business, Cleveland State University, Cleveland, OH 44114, USA. In theory, competition improves productivity and performance; trade liberalisation, which increases imports/exports, brings more competition. Using two large-scale survey responses from over 1000 manufacturers collected during two different time periods while US exports were growing in an environment of trade liberalisation, this study examines the effectiveness of technologies, over time, in manufacturing plants with varying export intensities. We find manufacturing technology use increases with exports and exporters report significant gains in plant performance over time. The study considers hard technologies (i.e. technologies involving capital-intensive equipment in manufacturing operations) and soft technologies (i.e. technologies involving planning and administrative components) to understand the distinct dynamic impact of the use of these technologies among plants exporting with varying intensities. Manufacturing plants are categorised into high, medium, and non-exporting based on the plant’s exports as a percent of total output. The results of this study indicates that exporters engage in more skilled use of these technologies than non-exporters. Further, exporters not only have higher skilled use of manufacturing technologies from non-exporters, but they also expand the scope of technologies that they skillfully employ in their operations. We find that over the course of liberalised trade regime, medium exporters get closer to high exporters in their skilled use of manufacturing technologies providing evidence of learning effect from exporting. Finally, higher skilled use of manufacturing technologies by high exporters translates into lower rejects and shorter lead times. However, non-exporters were not able to gain similar benefits from using manufacturing technologies.     

Simulation in the design and operation of manufacturing systems: state of the art and new trends

As the industrial requirements change at a rapid pace due to the drastic evolution of technology, the necessity of quickly investigating potential system alternatives towards a more efficient manufacturing system design arises more intensely than ever. Manufacturing systems simulation has proven to be a powerful tool for designing and evaluating a manufacturing system due to its low cost, quick analysis, low risk and meaningful insight that it may provide, improving thus the understanding of the influence of each component. Simulation comprises an indispensable set of IT tools and methods for the successful implementation of digital manufacturing. It allows experimentation and validation of product, process, and system design and configuration. This paper investigates the major historical milestones in the evolution of manufacturing systems simulation technologies and examines recent industrial and research approaches in key fields of manufacturing. It describes how the urge towards digitalisation of manufacturing in the context of the 4th Industrial revolution has shaped simulation in the design and operation of manufacturing systems and reviews the new approaches that have arisen in the literature. Particular focus is given to technologies in the digitalised factories of the future that are gaining ground in industrial applications simulation, offering multiple advantages.     

Industry 4.0: state of the art and future trends

Rapid advances in industrialisation and informatisation methods have spurred tremendous progress in developing the next generation of manufacturing technology. Today, we are on the cusp of the Fourth Industrial Revolution. In 2013, amongst one of 10 ‘Future Projects’ identified by the German government as part of its High-Tech Strategy 2020 Action Plan, the Industry 4.0 project is considered to be a major endeavour for Germany to establish itself as a leader of integrated industry. In 2014, China’s State Council unveiled their ten-year national plan, Made-in-China 2025, which was designed to transform China from the world’s workshop into a world manufacturing power. Made-in-China 2025 is an initiative to comprehensively upgrade China’s industry including the manufacturing sector. In Industry 4.0 and Made-in-China 2025, many applications require a combination of recently emerging new technologies, which is giving rise to the emergence of Industry 4.0. Such technologies originate from different disciplines including cyber-physical Systems, IoT, cloud computing, Industrial Integration, Enterprise Architecture, SOA, Business Process Management, Industrial Information Integration and others. At this present moment, the lack of powerful tools still poses a major obstacle for exploiting the full potential of Industry 4.0. In particular, formal methods and systems methods are crucial for realising Industry 4.0, which poses unique challenges. In this paper, we briefly survey the state of the art in the area of Industry 4.0 as it relates to industries.     

Intelligent and integrated RFID (Ⅱ-RFID) system for improving traceability in manufacturing

In the wake of globalization,many modern manufacturing companies in Norway have come under intense pressure caused by increased competition,stricter government regulation,and customer demand for higher value at low cost in a short time.Manufacturing companies need traceability,which means a real-time view into thenproduction processes and operations.Radio frequency identification(RFID) technology enables manufacturing companies to gain instant traceability and visibility because it handles manufactured goods,materials and processes transparently.RFID has become an important driver in manufacturing and supply chain activities.However,there is still a challenge in effectively deploying RFID in manufacturing.This paper describes the importance for Norwegian manufacturing companies to implement RFID technology,and shows how the intelligent and integrated RFID(n-RFID) system,which has been developed in the Knowledge Discovery Laboratory of Norwegian University of Science and Technology,provides instant traceability and visibility into manufacturing processes.It supports the Norwegian manufacturing industries survive and thrive in global competition.The future research work will focus on the field of RFID data mining to support decision-making process in manufacturing.     

复杂型材智能加工制造系统的设计

目的 实现新一代信息技术背景下传统铝门窗幕墙型材加工行业的转型升级,以应对复杂型材加工制造存在的成本高、工序繁多等诸多挑战。方法 根据型材加工工艺流程及该行业定制化生产的特点,提出一种涵盖网上下单、订单自动处理、机床智能加工生产的复杂型材智能加工制造系统架构,重点针对自主开发的门窗幕墙型材一站式加工智能机床,研发出一套复杂型材智能加工制造系统。结果 研究了加工信息数字化模型、工艺数据库等关键技术。通过工艺数据库的构建,实现了自动编程系统的搭建。结合Web Service与XML技术,研发出订单自助处理系统、机床智能操作管理系统及其与ERP系统的集成互连,打通了生产各环节之间的技术壁垒,形成了复杂型材一体化加工工艺。结论 实际测试表明,经复杂型材智能加工制造系统一体化制造的复杂型材从接受订单到产品加工完成只需40分钟,大幅提高了生产效率和产品质量,降低了加工成本。本研究为复杂型材智能加工及其他传统制造行业的转型升级提供了有益的借鉴和参考。     

Additive Manufacturing of Precision Biomaterials

Biomaterials play a critical role in modern medicine as surgical guides, implants for tissue repair, and as drug delivery systems. The emerging paradigm of precision medicine exploits individual patient information to tailor clinical therapy. While the main focus of precision medicine to date is the design of improved pharmaceutical treatments based on “-omics” data, the concept extends to all forms of customized medical care. This includes the design of precision biomaterials that are tailored to meet specific patient needs. Additive manufacturing (AM) enables free-form manufacturing and mass customization, and is a critical enabling technology for the clinical implementation of precision biomaterials. Materials scientists and engineers can contribute to the realization of precision biomaterials by developing new AM technologies, synthesizing advanced (bio)materials for AM, and improving medical-image-based digital design. As the field matures, AM is poised to provide patient-specific tissue and organ substitutes, reproducible microtissues for drug screening and disease modeling, personalized drug delivery systems, as well as customized medical devices.