Hasil untuk "Manufactures"

M. Elahinia, M. Hashemi, Majid Tabesh et al.

M. Yang, P. Hong, Sachin B. Modi

Lawrence X. Yu

Dr K Mpofu

Lin Zhang, Yongliang Luo, F. Tao et al.

W. Bilkey, Erik B. Nes

R. Hayes, S. Wheelwright

Pius Achanga, E. Shehab, R. Roy et al.

K. Knorr-Cetina

Dazhong Wu, D. Rosen, Lihui Wang et al.

Cloud-based design manufacturing (CBDM) refers to a service-oriented networked product development model in which service consumers are enabled to configure, select, and utilize customized product realization resources and services ranging from computer-aided engineering software to reconfigurable manufacturing systems. An ongoing debate on CBDM in the research community revolves around several aspects such as definitions, key characteristics, computing architectures, communication and collaboration processes, crowdsourcing processes, information and communication infrastructure, programming models, data storage, and new business models pertaining to CBDM. One question, in particular, has often been raised: is cloud-based design and manufacturing actually a new paradigm, or is it just "old wine in new bottles"? To answer this question, we discuss and compare the existing definitions for CBDM, identify the essential characteristics of CBDM, define a systematic requirements checklist that an idealized CBDM system should satisfy, and compare CBDM to other relevant but more traditional collaborative design and distributed manufacturing systems such as web- and agent-based design and manufacturing systems. To justify the conclusion that CBDM can be considered as a new paradigm that is anticipated to drive digital manufacturing and design innovation, we present the development of a smart delivery drone as an idealized CBDM example scenario and propose a corresponding CBDM system architecture that incorporates CBDM-based design processes, integrated manufacturing services, information and supply chain management in a holistic sense. We present a new paradigm in digital manufacturing and design innovation, namely cloud-based design and manufacturing (CBDM).We identify the common key characteristics of CBDM.We define a requirement checklist that any idealized CBDM system should satisfy.We compare CBDM with other relevant but more traditional collaborative design and distributed manufacturing systems.We describe an idealized CBDM application example scenario.

M. Askari, D. Hutchins, P. Thomas et al.

Abstract Metamaterials exhibit properties beyond those exhibited by conventional materials in conventional scenarios. These have been investigated both theoretically and experimentally at length. In many cases the underpinning physical understanding of metamaterials has greatly preceded our ability to manufacture constituent structures. However, the development of additive manufacturing techniques gives new possibilities for the fabrication of complex metamaterial structures, many of which cannot be realised through conventional fabrication methods. The literature to date contains contributions from a diverse group of researchers from the physical sciences, mathematics, and manufacturing technology in the creation of metamaterials for electromagnetic, acoustic and mechanical applications. It is proposed that additive manufacturing holds the key to realise the capabilities of this vibrant research community and permit the creation of new paradigms in fundamental structures but also exploitation through application. For this purpose, a literature review is presented which identifies key advances in metamaterials alongside additive manufacturing and proposes new opportunities for researchers to work together through intra/inter disciplinary research to realise structures which exhibit extraordinary behaviour(s). This review represents a comprehensive account of the state-of-the-art in the production of such metamaterials using additive manufacturing methods and highlights areas, which, based on trends observed in the literature, are worthy of further research and require a coordinated effort on behalf of the afore mentioned disciplines in order to advance the state-of-the-art.

X. Tan, Y. Tan, C. Chow et al.

H. Kagermann, W. Wahlster, Johannes Helbig et al.

J. Halloran

D. Loterie, Paul Delrot, C. Moser

In tomographic volumetric additive manufacturing, an entire three-dimensional object is simultaneously solidified by irradiating a liquid photopolymer volume from multiple angles with dynamic light patterns. Though tomographic additive manufacturing has the potential to produce complex parts with a higher throughput and a wider range of printable materials than layer-by-layer additive manufacturing, its resolution currently remains limited to 300 µm. Here, we show that a low-étendue illumination system enables the production of high-resolution features. We further demonstrate an integrated feedback system to accurately control the photopolymerization kinetics over the entire build volume and improve the geometric fidelity of the object solidification. Hard and soft centimeter-scale parts are produced in less than 30 seconds with 80 µm positive and 500 µm negative features, thus demonstrating that tomographic additive manufacturing is potentially suitable for the ultrafast fabrication of advanced and functional constructs. Tomographic additive manufacturing produces complex parts with a wide range of printable materials but remains limited in terms of resolution. Here, the authors tune the étendue of the light source and accurately control the photopolymerization kinetics using an integrated feedback system, leading to the fabrication of high resolution features.

Kai Ding, F. Chan, Xudong Zhang et al.

Smart manufacturing is the core idea of the fourth industrial evolution. For a smart manufacturing shop floor, real-time monitoring, simulation and prediction of manufacturing operations are vital to improve the production efficiency and flexibility. In this paper, the Cyber-Physical System (CPS) and Digital Twin technologies are introduced to build the interconnection and interoperability of a physical shop floor and corresponding cybershop floor. A Digital Twin-based Cyber-Physical Production System (DT-CPPS) is further established, and the configuring mechanism, operating mechanism and real-time data-driven operations control of DT-CPPS are discussed in detail. It is expected that DT-CPPS will provide the basis for shop floors to march towards smart manufacturing.

Ji Zhou, Yanhong Zhou, Baicun Wang et al.

Abstract 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.

N. Tuptuk, S. Hailes

A revolution in manufacturing systems is underway: substantial recent investment has been directed towards the development of smart manufacturing systems that are able to respond in real time to changes in customer demands, as well as the conditions in the supply chain and in the factory itself. Smart manufacturing is a key component of the broader thrust towards Industry 4.0, and relies on the creation of a bridge between digital and physical environments through Internet of Things (IoT) technologies, coupled with enhancements to those digital environments through greater use of cloud systems, data analytics and machine learning. Whilst these individual technologies have been in development for some time, their integration with industrial systems leads to new challenges as well as potential benefits. In this paper, we explore the challenges faced by those wishing to secure smart manufacturing systems. Lessons from history suggest that where an attempt has been made to retrofit security on systems for which the primary driver was the development of functionality, there are inevitable and costly breaches. Indeed, today's manufacturing systems have started to experience this over the past few years; however, the integration of complex smart manufacturing technologies massively increases the scope for attack from adversaries aiming at industrial espionage and sabotage. The potential outcome of these attacks ranges from economic damage and lost production, through injury and loss of life, to catastrophic nation-wide effects. In this paper, we discuss the security of existing industrial and manufacturing systems, existing vulnerabilities, potential future cyber-attacks, the weaknesses of existing measures, the levels of awareness and preparedness for future security challenges, and why security must play a key role underpinning the development of future smart manufacturing systems.

A. Shahzad, I. Lazoglu

Chunquan Li, Yaqiong Chen, Y. Shang

Abstract Under the trend of economic globalization, intelligent manufacturing has attracted a lot of attention from academic and industry. Related enabling technologies make manufacturing industry more intelligent. As one of the key technologies in artificial intelligence, big data driven analysis improves the market competitiveness of manufacturing industry by mining the hidden knowledge value and potential ability of industrial big data, and helps enterprise leaders make wise decisions in various complex manufacturing environments. This paper provides a theoretical analysis basis for big data-driven technology to guide decision-making in intelligent manufacturing, fully demonstrating the practicability of big data-driven technology in the intelligent manufacturing industry, including key advantages and internal motivation. A conceptual framework of intelligent decision-making based on industrial big data-driven technology is proposed in this study, which provides valuable insights and thoughts for the severe challenges and future research directions in this field.