Smart manufacturing systems for Industry 4.0: Conceptual framework, scenarios, and future perspectives

Zheng, Pai; wang, Honghui; Sang, Zhiqian; Zhong, Rong; Liu, Yongkui; Liu, Chao; Mubarok, Khamdi; Yu, Shiyong; Xu, Xun

doi:10.1007/s11465-018-0499-5

Cited by 719 publications

(352 citation statements)

References 54 publications

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“…Manufacturing (or production) systems have continuously been evolving under the influence of information and communication technology. As a result, a concept of new-generation manufacturing systems has been evolved, which is referred to as Industry 4.0, smart manufacturing, connected factory, Society 5.0, Made in China 2025, and alike [26][27][28][29][30][31][34][35][36][37][38][39][40]. The primary goal is to achieve an active collaboration among hardware devices (e.g., machine tools, robots, measuring instruments), software systems (CAD/CAM, ERP, and SCM systems), and human resources on a real-time basis by exchanging the required data, information, and knowledge [34][35][36][37][38][39][40].…”

Section: Fundamental Issues Regarding Manufacturing Knowledge Represementioning

confidence: 99%

Fundamental Issues of Concept Mapping Relevant to Discipline-Based Education: A Perspective of Manufacturing Engineering

Ullah

2019

Education Sciences

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This article addresses some fundamental issues of concept mapping relevant to discipline-based education. The focus is on manufacturing knowledge representation from the viewpoints of both human and machine learning. The concept of new-generation manufacturing (Industry 4.0, smart manufacturing, and connected factory) necessitates learning factory (human learning) and human-cyber-physical systems (machine learning). Both learning factory and human-cyber-physical systems require semantic web-embedded dynamic knowledge bases, which are subjected to syntax (machine-to-machine communication), semantics (the meaning of the contents), and pragmatics (the preferences of individuals involved). This article argues that knowledge-aware concept mapping is a solution to create and analyze the semantic web-embedded dynamic knowledge bases for both human and machine learning. Accordingly, this article defines five types of knowledge, namely, analytic a priori knowledge, synthetic a priori knowledge, synthetic a posteriori knowledge, meaningful knowledge, and skeptic knowledge. These types of knowledge help find some rules and guidelines to create and analyze concept maps for the purposes human and machine learning. The presence of these types of knowledge is elucidated using a real-life manufacturing knowledge representation case. Their implications in learning manufacturing knowledge are also described. The outcomes of this article help install knowledge-aware concept maps for discipline-based education.

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Section: Fundamental Issues Regarding Manufacturing Knowledge Represementioning

confidence: 99%

Fundamental Issues of Concept Mapping Relevant to Discipline-Based Education: A Perspective of Manufacturing Engineering

Ullah

2019

Education Sciences

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Section: Introductionmentioning

confidence: 99%

“…The development of manufacturing not only considers technological innovations but also economic, social and environmental effects [4,7]. In the literature, many novel paradigms known as intelligent manufacturing systems (IMS) [8][9][10][11][12][13][14][15] have been proposed such as holonic [16], biological [17], reconfigurable [18], and cloud manufacturing systems [19].In the manufacturing field, Industry 4.0 is characterized by the autonomous systems with cyber and physical representation [20][21][22] and advance in ICT [23] such as cloud computing [24], internet of things (IoT), internet of service (IoS) [25][26][27][28][29], agent technology [30][31][32][33][34][35], cognitive technology [36,37], big data [38,39], data mining [40,41], and artificial intelligence [42][43][44][45][46][47]. Smart manufacturing systems (SMS) built with intelligent cyber-physical systems (CPSs) are the heart of the fourth industrial revolution [21,48].…”

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confidence: 99%

Development of a Smart Cyber-Physical Manufacturing System in the Industry 4.0 Context

Tran

Park²,

Nguyen

et al. 2019

Applied Sciences

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Featured Application: A proposed smart manufacturing system has the ability to adapt to manufacturing changes. Abstract:The complexity and dynamic of the manufacturing environment are growing due to the changes of manufacturing demand from mass production to mass customization that require variable product types, small lot sizes, and a short lead-time to market. Currently, the automatic manufacturing systems are suitable for mass production. To cope with the changes of the manufacturing environment, the paper proposes the model and technologies for developing a smart cyber-physical manufacturing system (Smart-CPMS). The transformation of the actual manufacturing systems to the Smart-CPMS is considered as the next generation of manufacturing development in Industry 4.0. The Smart-CPMS has advanced characteristics inspired from biology such as self-organization, self-diagnosis, and self-healing. These characteristics ensure that the Smart-CPMS is able to adapt with continuously changing manufacturing requirements. The model of Smart-CPMS is inherited from the organization of living systems in biology and nature. Consequently, in the Smart-CPMS, each resource on the shop floor such as machines, robots, transporters, and so on, is an autonomous entity, namely a cyber-physical system (CPS) which is equipped with cognitive capabilities such as perception, reasoning, learning, and cooperation. The Smart-CPMS adapts to the changes of manufacturing environment by the interaction among CPSs without external intervention. The CPS implementation uses the cognitive agent technology. Internet of things (IoT) with wireless networks, radio frequency identification (RFID), and sensor networks are used as information and communication technology (ICT) infrastructure for carrying out the Smart-CPMS.Currently, manufacturing demands are changing from mass production to mass customization, which is focused on exclusive and individualized demand [2,3]. This trend requires the manufacturing systems to have the ability to adapt to the fast change of the manufacturing environment. To face this challenge, the new trend of manufacturing system development is to apply autonomous behaviors inspired from living systems and nature to have the sustainable manufacturing systems. Industry 4.0 enables the realization of sustainable manufacturing using the ubiquitous information and communication technology (ICT) infrastructure [4]. National strategies for developing smart manufacturing systems are focused on by both developing and developed countries [5,6]. The development of manufacturing not only considers technological innovations but also economic, social and environmental effects [4,7]. In the literature, many novel paradigms known as intelligent manufacturing systems (IMS) [8][9][10][11][12][13][14][15] have been proposed such as holonic [16], biological [17], reconfigurable [18], and cloud manufacturing systems [19].In the manufacturing field, Industry 4.0 is characterized by the autonomous systems with cyber and physical representation [20]...

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“…These three elements of Industry 4.0 (CPS, the interoperability and a smart city) have a significant role in production and services. It should be mentioned that Industry 4.0 has a major role in boosting the manufacturing process of various industries (Gentner, 2016;Theorin et al, 2017;Zheng et al, 2018;Alaeddin et al, 2018;Ślusarczyk, 2018;Muzekenyi et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Fourth industrial revolution: a way forward to attain better performance in the textile industry

Ślusarczyk

Haseeb

Hussain

2019

Engineering Management in Production and Services

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A B S T R A C TThe textile industry is one of the fastest growing industries which expressively contributes to the economic growth of Malaysia. However, in recent years, the situation has changed and demonstrates a downward trend. The imports are growing faster compared to the exports, consequently resulting in a low contribution to the gross domestic product (GDP). To address the issue, this study aims to investigate the role of Industry 4.0 on the performance of firms engaged in the production and services of the Malaysian textile industry. To achieve the objective, this study adopted a cross-sectional research design. A survey was carried out to collect data from employees of textile firms. Results of the study found that Industry 4.0 positively contributed to the effectiveness of the production and services of the textile industry. Production and services have a positive role in the performance of textile firms. The current study provides an interesting insight into the future direction of research for studies on organisational performance, which can be extended to different manufacturing-based industries. In addition, it provides the rationale for the adoption and implementation of smart technologies in these industries. It has been found that cyber-physical systems (CPS), interoperability, a smart city and a smart product have a positive effect on production and services. Additionally, it is not possible without the effective implementation of technology. Thus, the current study provides valuable insights into the improvement of the textile industry's performance. K E Y W O R D S Industry 4.0, firm performance, cyber-physical systems, interoperability, smart product; smart city

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Smart manufacturing systems for Industry 4.0: Conceptual framework, scenarios, and future perspectives

Cited by 719 publications

References 54 publications

Fundamental Issues of Concept Mapping Relevant to Discipline-Based Education: A Perspective of Manufacturing Engineering

Fundamental Issues of Concept Mapping Relevant to Discipline-Based Education: A Perspective of Manufacturing Engineering

Development of a Smart Cyber-Physical Manufacturing System in the Industry 4.0 Context

Fourth industrial revolution: a way forward to attain better performance in the textile industry

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