Toward New-Generation Intelligent Manufacturing

Zhou, Jing; Li, Peigen; Zhou, Yanhong; Wang, Baicun; Zang, Jiyuan; Li, Meng

doi:10.1016/j.eng.2018.01.002

Cited by 451 publications

(227 citation statements)

References 41 publications

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“…However, in technology-rich working environments, work division between human workers and machines is usually observed and the assigned tasks are categorised into human-specific and machine-specific. Contrary to the principle of the computer-integrated manufacturing (CIM), Industry 4.0 does not envision workerless production facilities, but rather pursues the synergetic integration of humans and machines in a shared working space, in the sense of a human-cyber-physical system (HCPS) [91], where the individual skills and talents of humans can be successfully exploited. Industry 4.0 imposes transitions on work division between human and machines, since humans are assisted by intelligent devices and machines, that is, human-machine cooperation, while interacting and exchanging information with the machines, that is, human-machine crossover collaboration.…”

Section: Human-machine Interactionmentioning

confidence: 99%

Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects

Angelopoulos

Michailidis

Νομικός

et al. 2019

Sensors

222

111

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The recent advancements in the fields of artificial intelligence (AI) and machine learning (ML) have affected several research fields, leading to improvements that could not have been possible with conventional optimization techniques. Among the sectors where AI/ML enables a plethora of opportunities, industrial manufacturing can expect significant gains from the increased process automation. At the same time, the introduction of the Industrial Internet of Things (IIoT), providing improved wireless connectivity for real-time manufacturing data collection and processing, has resulted in the culmination of the fourth industrial revolution, also known as Industry 4.0. In this survey, we focus on the vital processes of fault detection, prediction and prevention in Industry 4.0 and present recent developments in ML-based solutions. We start by examining various proposed cloud/fog/edge architectures, highlighting their importance for acquiring manufacturing data in order to train the ML algorithms. In addition, as faults might also occur from sources beyond machine degradation, the potential of ML in safeguarding cyber-security is thoroughly discussed. Moreover, a major concern in the Industry 4.0 ecosystem is the role of human operators and workers. Towards this end, a detailed overview of ML-based human–machine interaction techniques is provided, allowing humans to be in-the-loop of the manufacturing processes in a symbiotic manner with minimal errors. Finally, open issues in these relevant fields are given, stimulating further research.

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Section: Human-machine Interactionmentioning

confidence: 99%

Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects

Angelopoulos

Michailidis

Νομικός

et al. 2019

Sensors

222

111

View full text Add to dashboard Cite

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“…Production data is provided of a completely new quality and with real-time information on production processes. This is possible through the comprehensive placement of production with sensors and the continuous integration of intelligent objects (Spath et al 2013;Zhou et al 2018). Future data models will work in real time and the production will be more transparent ).…”

Section: Origin and Characterization Of Industry 40mentioning

confidence: 99%

SME 4.0: The Role of Small- and Medium-Sized Enterprises in the Digital Transformation

Matt

Rauch

2020

Industry 4.0 for SMEs

104

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“…However, due to continuous technological leaps, cyber systems have become a crucial integrated part of this composite system, forming what is known as human cyber-physical systems (HCPSs). In such a system, the cyber system allows the machine to contribute to controlling and decision making (factory 3.0) or even more advanced intelligent control and decision making (factory 4.0) [39]. One example is the cyber-physical discrete manufacturing network developed by Gacia and his coworkers [40].…”

Section: Smart Factory Traditional Factorymentioning

confidence: 99%

Sustainable and Smart Manufacturing: An Integrated Approach

et al. 2020

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The necessity for decreasing the negative impact of the manufacturing industry has recently increased. This is getting recognized as a global challenge due to the rapid increase in life quality standards, demand, and the decrease in available resources. Thus, manufacturing, as a core of the product provision system and a fundamental pillar of civilized existence, is significantly influenced by sustainability issues. Furthermore, current manufacturing modeling and assessment criteria require intensive revisions and upgrades to keep up with these new challenges. Nearly all current manufacturing models are based on the old paradigm, which was proven to be inadequate. Therefore, manufacturing technology, along with culture and economy, are held responsible for providing new tools and opportunities for building novel resolutions towards a sustainable manufacturing concept. One of such tools is sustainability assessment measures. Revising and updating such tools is a core responsibility of the manufacturing sector to efficiently evaluate and enhance sustainable manufacturing performance. These measures should be adequate to respond to the growing sustainability concerns in pursuit of an integrated sustainability concept. The triple bottom line (TBL) that includes environment, economic, and social dimensions has usually been used to evaluate sustainability. However, there is a lack of standard sets of sustainable manufacturing performance measures. In addition to the sustainability concept, a new concept of smart manufacturing is emerging. The smart manufacturing concept takes advantage of the recent technological leap in Artificial Intelligent (AI), Cloud Computing (CC), and the Internet of Things (IoT). Although this concept offers an important step to boost the current production capabilities to meet the growing need, it is still not clear whether the two concepts of smart manufacturing and sustainability will constructively or destructively interact. Therefore, the current study aims to integrate the sustainable smart manufacturing performance by incorporating sustainable manufacturing measures and discussing current and future challenges that are faced by the manufacturing sector. In addition, the opportunities for future research incorporating sustainable smart manufacturing are also presented.

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Toward New-Generation Intelligent Manufacturing

Cited by 451 publications

References 41 publications

Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects

Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects

SME 4.0: The Role of Small- and Medium-Sized Enterprises in the Digital Transformation

Sustainable and Smart Manufacturing: An Integrated Approach

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