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1.
The research objective of this work is to enhance the perception of, sensing in, and control of smart manufacturing systems (SMS) by leveraging active sensor systems within smart products during the manufacturing phase. Smart manufacturing utilizes rich process data, usually collected by the SMS (e.g., machine tools), to enable accurate tracking and monitoring of individual products throughout the process chain. However, until now, the to-be-manufactured product itself has not contributed to the sensing and compilation of product and process data. More specifically, data measured from the product’s structure during its own fabrication. In this paper, we discuss and evaluate the opportunity to actively use the capabilities of smart products within a SMS in terms of technical and economic feasibility. This opportunity emerged only recently with the advancements in smart products engineering. In this research, we developed a smart product prototype and evaluated it on a SMS testbed (CPlab) with eight distinct, fully-connected manufacturing processes. The results of the conducted experiments show the possibility to uniquely identify two distinct ‘fingerprints’ of manufacturing processes solely based on data provided by sensors within the smart product itself. The sensor data was collected directly from the smart product before manufacture was completed, yet after the intended sensor functionality during the product’s use phase was activated. The capability to automatically, accurately, and reliably identify process signatures and even inform the optimization of manufacturing parameters creates new opportunities for improvements in quality, scheduling, and seamless transparency across the whole value chain. 相似文献
2.
Digital twins can achieve hardware-in-the-loop simulation of both physical equipment and cyber model, which could be used to avoid the considerable cost of manufacturing system reconfiguration if the design deficiencies are found in the deployment process of the traditional irreversible design approach. Based on the digital twin technology, a quad-play CMCO (i.e., Configuration design-Motion planning-Control development-Optimization decoupling) design architecture is put forward for the design of the flow-type smart manufacturing system in the Industry 4.0 context. The iteration logic of the CMCO design model is expounded. Two key enabling technologies for enabling the customized and software-defined design of flow-type smart manufacturing systems are presented, including the generalized encapsulation of the quad-play CMCO model and the digital twin technique. A prototype of a digital twin-based manufacturing system design platform, named Digital Twin System, is presented based on the CMCO model. The digital twin-based design platform is verified with a case study of the hollow glass smart manufacturing system. The result shows that the Digital Twin System-based design approach is feasible and efficient. 相似文献
3.
中国是世界上人口最多的国家,同时又是发展中国家,专家称2015年中国碳排量将达欧美总和,这是非常惊人的,所以国际上流传"防祸",中国变成了祸害全球的崛起大国。温总理在哥本哈根会议上的声明,意味着我国政府自主承诺的2020年单位GDP碳排放比2005年减少40%至45%的目标即将付诸实施。"低碳生活"这个名词也将加速地进入普通民众的日常生活视野,以创新为最终目标的设计领域的设计师如何更新观念,用低碳变设计去倡导低碳生活,引导着时代潮流的审美趋向是本文讨论的主题。 相似文献
4.
Smart manufacturing offers a high level of adaptability and autonomy to meet the ever-increasing demands of product mass customization. Although digitalization has been used on the shop floor of modern factory for decades, some manufacturing operations remain manual and humans can perform these better than machines. Under such circumstances, a feasible solution is to have human operators collaborate with computational intelligence (CI) in real time through augmented reality (AR). This study conducts a systematic review of the recent literature on AR applications developed for smart manufacturing. A classification framework consisting of four facets, namely interaction device, manufacturing operation, functional approach, and intelligence source, is proposed to analyze the related studies. The analysis shows how AR has been used to facilitate various manufacturing operations with intelligence. Important findings are derived from a viewpoint different from that of the previous reviews on this subject. The perspective here is on how AR can work as a collaboration interface between human and CI. The outcome of this work is expected to provide guidelines for implementing AR assisted functions with practical applications in smart manufacturing in the near future. 相似文献
5.
Manufacturers expect the extra value of Industry 4.0 as the world is experiencing digital transformation. Studies have proved the potential of the Internet of Things (IoT) for reducing cost, improving efficiency, quality, and achieving data-oriented predictive maintenance services. Collecting a wide range of real-time data from products and the environment requires smart sensors, reliable communications, and seamless integration. IoT, as a critical Industry 4.0 enabler emerges smart home appliances for higher customer satisfaction, energy efficiency, personalisation, and advanced Big data analytics. However, established factories with limited resources are facing challenges to change the longstanding production lines and meet customer’s requirements. This study aims to fulfil the gaps by transforming conventional home appliances to IoT-enabled smart systems with the ability to integrate into a smart home system. An industry-led case study demonstrates how to turn conventional appliances to smart products and systems (SPS) by utilising the state-of-the-art Industry 4.0 technologies. 相似文献
6.
Within the scheduling framework, the potential of digital twin (DT) technology, based on virtualisation and intelligent algorithms to simulate and optimise manufacturing, enables an interaction with processes and modifies their course of action in time synchrony in the event of disruptive events. This is a valuable capability for automating scheduling and confers it autonomy. Automatic and autonomous scheduling management can be encouraged by promoting the elimination of disruptions due to the appearance of defects, regardless of their origin. Hence the zero-defect manufacturing (ZDM) management model oriented towards zero-disturbance and zero-disruption objectives has barely been studied. Both strategies combine the optimisation of production processes by implementing DTs and promoting ZDM objectives to facilitate the modelling of automatic and autonomous scheduling systems. In this context, this particular vision of the scheduling process is called smart manufacturing scheduling (SMS). The aim of this paper is to review the existing scientific literature on the scheduling problem that considers the DT technology approach and the ZDM model to achieve self-management and reduce or eliminate the need for human intervention. Specifically, 68 research articles were identified and analysed. The main results of this paper are to: (i) find methodological trends to approach SMS models, where three trends were identified; i.e. using DT technology and the ZDM model, utilising other enabling digital technologies and incorporating inherent SMS capabilities into scheduling; (ii) present the main SMS alignment axes of each methodological trend; (iii) provide a map to classify the literature that comes the closest to the SMS concept; (iv) discuss the main findings and research gaps identified by this study. Finally, managerial implications and opportunities for further research are identified. 相似文献
7.
This paper provides a fundamental research review of Reconfigurable Manufacturing Systems (RMS), which uniquely explores the state-of-the-art in distributed and decentralized machine control and machine intelligence. The aim of this review is to draw objective answers to two proposed research questions, relating to: (1) reconfigurable design and industry adoption; and (2) enabling present and future state technology. Key areas reviewed include: (a) RMS – fundamentals, design rational, economic benefits, needs and challenges; (b) Machine Control – modern operational technology, vertical and horizontal system integration, advanced distributed and decentralized control; (c) Machine Intelligence – distributed and decentralized paradigms, technology landscape, smart machine modelling, simulation, and smart reconfigurable synergy. Uniquely, this paper establishes a vision for next-generation Industry 4.0 manufacturing machines, which will exhibit extraordinary Smart and Reconfigurable (SR*) capabilities. 相似文献
9.
The increasing importance of automation and smart capabilities for factories and other industrial systems has led to the concept of Industry 4.0 (I4.0). This concept aims at creating systems that improve the vertical and horizontal integration of production through (i) comprehensive and intelligent automation of industrial processes, (ii) informed and decentralized real-time decision making, and (iii) stringent quality requirements that can be monitored at any time. The I4.0 infrastructure, supported in many cases by robots, sensors, and algorithms, demands highly skilled workers able to continuously monitor the quality of both the items to be produced and the underlying production processes.While the first attempts to develop smart factories and enhance the digital transformation of companies are under way, we need adequate methods to support the identification and specification of quality attributes that are relevant to I4.0 systems. Our main contribution is to provide a refined version of the ISO 25010 quality model specifically tailored to those qualities demanded by I4.0 needs. This model aims to provide actionable support for I4.0 software engineers that are concerned with quality issues. We developed our model based on an exhaustive analysis of similar proposals using the design science method as well as expertise from seasoned engineers in the domain. We further evaluate our model by applying it to two important I4.0 reference architectures further clarifying its application. 相似文献
10.
Production (throughput) bottlenecks are the critical stations defining and constraining the overall productivity of a system. Effective and timely identification of bottlenecks provide manufacturers essential decision input to allocate limited maintenance and financial resources for throughput improvement. However, identifying throughput bottleneck in industry is not a trivial task. Bottlenecks are usually non-static (shifting) among stations during production, which requires dynamic bottleneck detection methods. An effective methodology requires proper handling of real-time production data and integration of factory physics knowledge. Traditional data-driven bottleneck detection methods only focus on serial production lines, while most production lines are complex. With careful revision and examination, those methods can hardly meet practical industrial needs. Therefore, this paper proposes a systematic approach for bottleneck detection for complex manufacturing systems with non-serial configurations. It extends a well-recognized bottleneck detection algorithm, “the Turning Point Method”, to complex manufacturing systems by evaluating and proposing appropriate heuristic rules. Several common industrial scenarios are evaluated and addressed in this paper, including closed loop structures, parallel line structures, and rework loop structures. The proposed methodology is demonstrated with a one-year pilot study at an automotive powertrain assembly line with complex manufacturing layouts. The result has shown a successful implementation that greatly improved the bottleneck detection capabilities for this manufacturing system and led to an over 30% gain in Overall Equipment Effectiveness (OEE). 相似文献
11.
In the Industry 4.0 era, manufacturers strive to remain competitive by using advanced technologies such as collaborative robots, automated guided vehicles, augmented reality support and smart devices. However, only if these technological advancements are integrated into their system context in a seamless way, they can deliver their full potential to a manufacturing organization. This integration requires a system architecture as a blueprint for positioning and interconnection of the technologies. For this purpose, the HORSE framework, resulting from the HORSE EU H2020 project, has been developed to act as a reference architecture of a cyber-physical system to integrate various Industry 4.0 technologies and support hybrid manufacturing processes, i.e., processes in which human and robotic workers collaborate. The architecture has been created using design science research, based on well-known software engineering frameworks, established manufacturing domain standards and practical industry requirements. The value of a reference architecture is mainly established by application in practice. For this purpose, this paper presents the application and evaluation of the HORSE framework in 10 manufacturing plants across Europe, each with its own characteristics. Through the physical deployment and demonstration, the framework proved its goal to be basis for the well-structured design of an operational smart manufacturing cyber-physical system that provides horizontal, cross-functional management of manufacturing processes and vertical control of heterogeneous technologies in work cells. We report on valuable insights on the difficulties to realize such systems in specific situations. The experiences form the basis for improved adoption, further improvement and extension of the framework. In sum, this paper shows how a reference architecture framework supports the structured application of Industry 4.0 technologies in manufacturing environments that so far have relied on more traditional digital technology. 相似文献
12.
Currently, ways to approach the design of Cyber Physical Systems in Industry 4.0 are under development. Emerging concepts of Smart Factories require the development of specialized knowledge; new working methods are also needed to manage the transition from conventional industry to industry 4.0. To achieve this objective, fractal theory could provide the appropriate knowledge and tools. Fractal systems applied to manufacturing have been widely used over the last decades to design complex adaptive systems: it allows the introduction of resilience requirements (capacity to react to changes in a turbulent environment) and to reduce the complexity of its structure, operation and management. In order to know the potential and the possibility of applying fractal theory to the design of systems in Industry 4.0, this article reviews the publications that develop fractal systems for manufacturing engineering. The review includes contributions published between 1985 (approximate date of the first works on the theory applied to manufacturing engineering) and 2019. The objective is to gather those strategies, methodologies and successful case studies that can be useful for the approaches of Industry 4.0 and to define a set of future lines of work for the adaptation of the fractal theory to the new challenges posed by Industry 4.0. 相似文献
13.
Today, data science presents immense opportunities by turning raw data into manufacturing intelligence in data-driven manufacturing that aims to improve operational efficiency and product quality together with reducing costs and risks. However, manufacturing firms face difficulties in managing their data science endeavors for reaping these potential benefits. Maturity models are developed to guide organizations by providing an extensive roadmap for improvement in certain areas. Therefore, this paper seeks to address this problem by proposing a theoretically grounded Data Science Maturity Model (DSMM) for manufacturing organizations to assess their existing strengths and weaknesses, perform a gap analysis, and draw a roadmap for continuous improvements in their progress towards data-driven manufacturing. DSMM comprises six maturity levels from “Not Performed” to” Innovating” and twenty-eight data science processes categorized under six headings: Organization, Strategy Management, Data Analytics, Data Governance, Technology Management, and Supporting. The applicability and usefulness of DSMM are validated through multiple case studies conducted in manufacturing organizations of various sizes, industries, and countries. The case study results indicate that DSMM is applicable in different settings and is able to reflect the organizations’ current data science maturity levels and provide significant insights to improve their data science capabilities. 相似文献
14.
The introduction of modern technologies in manufacturing is contributing to the emergence of smart (and data-driven) manufacturing systems, known as Industry 4.0. The benefits of adopting such technologies can be fully utilized by presenting optimization models in every step of the decision-making process. This includes the optimization of maintenance plans and production schedules, which are two essential aspects of any manufacturing process. In this paper, we consider the real-time joint optimization of maintenance planning and production scheduling in smart manufacturing systems. We have considered a flexible job shop production layout and addressed several issues that usually take place in practice. The addressed issues are: new job arrivals, unexpected due date changes, machine degradation, random breakdowns, minimal repairs, and condition-based maintenance (CBM). We have proposed a real-time optimization-based system that utilizes a modified hybrid genetic algorithm, an integrated proactive-reactive optimization model, and hybrid rescheduling policies. A set of modified benchmark problems is used to test the proposed system by comparing its performance to several other optimization algorithms and methods used in practice. The results show the superiority of the proposed system for solving the problem under study. The results also emphasize the importance of the quality of the generated baseline plans (i.e., initial integrated plans), the use of hybrid rescheduling policies, and the importance of rescheduling times (i.e., reaction times) for cost savings. 相似文献
15.
As an emerging IT-driven business paradigm, smart product-service system (Smart PSS), which offers not only the smart, connected product (SCP) but also its generated service as a solution bundle, has become a vital research topic. Many research efforts have been devoted to constructing the conceptual design framework by considering SCPs and services simultaneously. However, the following critical issues in Smart PSS conceptual design have not been well addressed: how to improve the solution of Smart PSS in the conceptual design stage to meet user emotional requirements. Aiming to fill the gap, this work proposes a conceptual design method for Smart PSS from the perspective of analyzing user-generated emotions/feelings. Specifically, the relevant traditional products are identified, and their public review data is used to analyze user emotions/feelings in user-product interaction. Interactive emotion board as a new design tool is presented to organize the user-generated emotions/feelings, associated design elements, and the potential design points of the initial solution. And the analytic hierarchy process (AHP) is utilized to evaluate the improved solution. To ensure the efficiency of the analysis process, the self-organizing map (SOM) algorithm is utilized in the process of clustering product samples and Kansei words. A case study of smart electric bicycle service system (SEBSS) design is used to demonstrate the performance of the proposed method. Based on the case study, the proposed approach appears effective in helping with Smart PSS conceptual design. 相似文献
16.
AbstractSmart, interconnected products are transforming the industry. Construction is a paradigmatic example of digital transformation incorporating systems to improve safety and productivity. This research uses the lens of the Viable System Model (VSM) to design smart products that adhere to the organization strategy in disruptive transformations. Our design science research involving a construction group defines the necessary and sufficient conditions for the smart system cohesion and endurance in changing environments. For theory, we propose a product-level adoption of VSM. For practice, we assist managers in creating viable smart products for their industry 4.0 strategy. 相似文献
17.
Smart manufacturing is arriving. It promises a future of mass-producing highly personalized products via responsive autonomous manufacturing operations at a competitive cost. Of utmost importance, smart manufacturing requires end-to-end integration of intra-business and inter-business manufacturing processes and systems. Such end-to-end integration relies on standards-compliant and interoperable interfaces between different manufacturing stages and systems. In this paper, we present a comprehensive review of the current landscape of manufacturing automation standards, with a focus on end-to-end integrated manufacturing processes and systems towards mass personalization and responsive factory automation. First, we present an authentic vision of smart manufacturing and the unique needs for next-generation manufacturing automation. A comprehensive review of existing standards for enabling manufacturing process automation and manufacturing system automation is presented. Subsequently, focusing on meeting changing demands of efficient production of highly personalized products, we detail several future-proofing manufacturing automation scenarios via integrating various existing standards. We believe that existing automation standards have provided a solid foundation for developing smart manufacturing solutions. Faster, broader and deeper implementation of smart manufacturing automation can be anticipated via the dissemination, adoption, and improvement of relevant standards in a need-driven approach. 相似文献
18.
Present paper envisages the need for an innovative operations planning system to handle the challenges and opportunities offered by next industrial revolution called Industry 4.0 or smart manufacturing. In specific, to embrace the increasing level of automation in manufacturing industries, the obligation of joint consideration of multiple operations functions is realized. On the other hand, quick response to dynamic conditions created by machine failures, change in demand, uncertainty in supply, etc., is important in captivating the advantages of the digitization in industries. Easing out the computational complexity, imposed by the integration of multiple functions, therefore, becomes an important aspect of next generation manufacturing planning systems. Consequently, in this paper, an agent-based approach is engineered around the opportunities offered by modern digital factory viz., intelligence at the shop-floor and ubiquity of wireless communications. While intelligence at shop-floor allows distributing the decision-making tasks to various functional agents, the communication among the agents makes it feasible to incite integrated view through the coordination agent. The approach is demonstrated for a representative industrial environment of an automotive plant. Further, comparison over conventional approaches, computational comparison, effect of degree of integration, and performance of the approach under dynamic conditions are investigated. Finally, the approach is comprehensively evaluated to analyze its robustness and implications in various manufacturing settings. This extensive investigation shows that the proposed operations planning system has capability to apprehend the benefits from next generation intelligent factory. 相似文献
19.
Industry 4.0, an initiative from Germany, has become a globally adopted term in the past decade. Many countries have introduced similar strategic initiatives, and a considerable research effort has been spent on developing and implementing some of the Industry 4.0 technologies. At the ten-year mark of the introduction of Industry 4.0, the European Commission announced Industry 5.0. Industry 4.0 is considered to be technology-driven, whereas Industry 5.0 is value-driven. The co-existence of two Industrial Revolutions invites questions and hence demands discussions and clarifications. We have elected to use five of these questions to structure our arguments and tried to be unbiased for the selection of the sources of information and for the discussions around the key issues. It is our intention that this article will spark and encourage continued debate and discussion around these topics. 相似文献
20.
Changing production systems and product requirements can trace their origin in volatile customer behaviour and evolving product requirements. This dynamic nature of customer requirements has been described as a constantly moving target, thus presenting a significant challenge for several aspects of product development. To deal with this constant and sometimes unpredictable product evolution, cyber physical production systems (CPPS) that employ condition monitoring, self-awareness and reconfigurability principles, have to be designed and implemented. This research contributes a CPPS design approach that proactively provides the required CPPS design knowledge. This approach aims to minimise or avoids future consequences and disruptions on the CPPS. This knowledge needs to be provided at the right time whilst not being intrusive to the production system designer’s cognitive activity. To effectively deal with the complexity of the cyber physical production system design activity with a manual method would lead to a time consuming, and complex support tool which is hard to implement, and difficult to use. The CPPS design approach has therefore been implemented in a prototype digital factory tool. This paper describes in detail the system requirements and system architecture for this tool. In order to establish the effectiveness of the proposed approach for designing cyber physical production systems, the prototype digital factory tool has been evaluated with a case study and a number of semi-structured interviews with both industrial and scientific stakeholders. The encouraging results obtained from this research evaluation have shown that such an approach for supporting the CPPS design activity makes stakeholders aware of their decision consequences and is useful in practice. This result can lead the way for the development and integration of such knowledge-based decision-making approaches within state-of-the-art digital factory and Computer Aided Engineering Design (CAED) tools. 相似文献