Industry 4.0 Development and Application of Intelligent Manufacturing

Cheng, Guojian; Liu, Li-Ting; Qiang, Xinjian; Liu, Ye

doi:10.1109/isai.2016.0092

Cited by 156 publications

(96 citation statements)

References 4 publications

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“…The major technological areas which enable DT are very diverse and traditionally called "general purpose technologies" (Hirsch-Kreinsen and ten Hompel 2017). These include, for example, cyber-physical systems (CPS), (industrial) internet of things (I/IoT), cloud computing (CC), big data (BD), artificial intelligence but also augmented and virtual reality (Cheng et al 2016).…”

Section: Digital Transformationmentioning

confidence: 99%

Research streams on digital transformation from a holistic business perspective: a systematic literature review and citation network analysis

et al. 2019

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Digital transformation (DT) has become a buzzword, triggering different disciplines in research and influencing practice, which leads to independent research streams. Scholars investigate the antecedents, contingencies, and consequences of these disruptive technologies by examining the use of single technologies or of digitization, in general. Approaches are often very specialized and restricted to their domains. Thus, the immense breadth of technologies and their possible applications conditions a fragmentation of research, impeding a holistic view. With this systematic literature review, we aim to fill this gap in providing an overview of the different disciplines of DT research from a holistic business perspective. We identified the major research streams and clustered them with co-citation network analysis in nine main areas. Our research shows the main fields of interest in digital transformation research, overlaps of the research areas and fields that are still underrepresented. Within the business research areas, we identified three dominant areas in literature: finance, marketing, and innovation management. However, research streams also arise in terms of single branches like manufacturing or tourism. This study highlights these diverse research streams with the aim of deepening the understanding of digital transformation in research. Yet, research on DT still lacks in the areas of accounting, human resource management, and sustainability. The findings were distilled into a framework of the nine main areas for assisting the implications on potential research gaps on DT from a business perspective.

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Section: Digital Transformationmentioning

confidence: 99%

Research streams on digital transformation from a holistic business perspective: a systematic literature review and citation network analysis

et al. 2019

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“…The competition to bring up the elements of industry 4.0 is still under way between companies in Asia, Europe, and America [38]. There are two successful robotic implementation in industries that link to industry 4.0, namely, robotic application in automobile factories of BMW and Tesla [14]. In BMW group factory, the autonomous mobile robots are used for smart transport systems in supply logistics, while KUKA collaborative robots are utilized to work side by side with humans, for example, for lifting and positioning heavy components and welding operations [39].…”

Section: New Era Of Industrial Robotsmentioning

confidence: 99%

“…In BMW group factory, the autonomous mobile robots are used for smart transport systems in supply logistics, while KUKA collaborative robots are utilized to work side by side with humans, for example, for lifting and positioning heavy components and welding operations [39]. On the other hand, Tesla's factory also has utilized robots with other technologies in their production lines to support smart and intelligent products [14]. One example is the use of industrial robots for repetitive tasks such as applying an even layer of paint for automobiles.…”

Section: New Era Of Industrial Robotsmentioning

confidence: 99%

Computer Simulation of Human-Robot Collaboration in the Context of Industry Revolution 4.0

Rizal¹

2020

Becoming Human With Humanoid - From Physical Interaction to Social Intelligence

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The essential role of robot simulation for industrial robots, in particular the collaborative robots is presented in this chapter. We begin by discussing the robot utilization in the industry which includes mobile robots, arm robots, and humanoid robots. The author emphasizes the application of collaborative robots in regard to industry revolution 4.0. Then, we present how the collaborative robot utilization in the industry can be achieved through computer simulation by means of virtual robots in simulated environments. The robot simulation presented here is based on open dynamic engine (ODE) using anyKode Marilou. The author surveys on the use of dynamic simulations in application of collaborative robots toward industry 4.0. Due to the challenging problems which related to humanoid robots for collaborative robots and behavior in human-robot collaboration, the use of robot simulation may open the opportunities in collaborative robotic research in the context of industry 4.0. As developing a real collaborative robot is still expensive and time-consuming, while accessing commercial collaborative robots is relatively limited; thus, the development of robot simulation can be an option for collaborative robotic research and education purposes.

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“…Industry4.0 evolved from embedded system to the CPS. 5 CPS are engineered systems that are built from and depend on the seamless integration of computational algorithms and physical components. 6 CPS inherits all the complexities of modern large-scale distributed systems: they have to handle uncertainty and change during operation, control their emergent behavior, and be scalable and tolerant to threats.…”

Section: Introductionmentioning

confidence: 99%

Method of tasks and resources matching and analysis for cyber-physical production system

Jiang

Jian-hui

Mingcheng

et al. 2018

Advances in Mechanical Engineering

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A new generation of industrial revolution represented by intelligent manufacturing has come. Multi-product and small batch have become trend in market demands nowadays. Cyber-physical production system is a useful tool to meet this trend by performing tasks intelligently. Since cyber-physical production system performs tasks intelligently and autonomously, tasks and resources should be defined in standardized form in order to be identified and analyzed. Thus, how to define and analyze tasks and resources become the prerequisite for the operation of cyber-physical production system. In this article, a tasks and resources matching and analysis method is proposed for cyber-physical production system. First, the form of process knowledge base is designed, and the method for tasks and resources definition and matching is discussed. Next, the model of system operation logical based on colored Petri net and processing route generation algorithm is built. The parameters forecast method for non-standard processes is designed based on backpropagation neural network. Finally, implementation mechanism and effectiveness of the method are verified by a prototype system.

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Industry 4.0 Development and Application of Intelligent Manufacturing

Cited by 156 publications

References 4 publications

Research streams on digital transformation from a holistic business perspective: a systematic literature review and citation network analysis

Research streams on digital transformation from a holistic business perspective: a systematic literature review and citation network analysis

Computer Simulation of Human-Robot Collaboration in the Context of Industry Revolution 4.0

Method of tasks and resources matching and analysis for cyber-physical production system

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