SVM‐Based Dynamic Reconfiguration CPS for Manufacturing System in Industry 4.0

Shin, Hyun-Jun; Cho, Kyoung-Woo; Oh, Chang-Heon

doi:10.1155/2018/5795037

Cited by 24 publications

(17 citation statements)

References 30 publications

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“…An SVM solution was proposed to address issues in shop-floors where the main production is performed, through automated machines, workers or both, in Industry 4.0 environments, millions of devices and sensors can produce a wide range of data for predictive maintenance. So, in Reference [66], ML based on SVM was presented, modelling the conveyor belt system through the M/D/1 queue. In this setting, SVM predicted instances of abnormal operation where machine overloading or slow downs occurred, by detecting changes in the queue parameters.…”

Section: Supervised Learning-based Solutionsmentioning

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

216

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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: Supervised Learning-based Solutionsmentioning

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

216

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show abstract

“…JSSP, with its theoretical background, is the basis for testing new proposed methods and approaches, but it is used significantly less in more applied cases. Recently, when the concept of Industry 4.0 has been influencing the optimization of dynamic production systems increasingly (Shin et al, 2018), it also benefits from the DJSSP, which is reflected in the high activity of research work done in the recent period. The results presented in Table 7 show the activity and importance of multi-objective optimization investigation using evolutionary methods in Production Scheduling.…”

Section: Discussionmentioning

confidence: 99%

Multi-objective optimization of production scheduling with evolutionary computation: A review

Ojsteršek¹,

Brezočnik²,

Buchmeister³

2020

10.5267/j.ijiec

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Multi-Objective (MO) optimization is a well-known research field with respect to the complexity of production planning and scheduling. In recent years, many different Evolutionary Computation (EC) methods have been applied successfully to MO production planning and scheduling. This paper is focused on making a review of MO production scheduling methods, starting from production scheduling presentation, notation and classification. The research field of EC methods is presented, then EC algorithms` classification is introduced for the purpose of production scheduling optimization. As a main goal, MO optimization is focused on hybrid EC methods, and presenting their advantages and limitations. Finally, a survey of five scientific databases is presented, with the analysis of the scientific publications the terminology development of the scientific field is presented. Using the citation analysis of the scientific publications, the application for the MO optimization in manufacturing scheduling is discussed.

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“…Building a smart factory model with a cyber-physical system (CPS). Shin et al (2018) designed a dynamic reconfigurable factory CPS, which is capable of selfmanagement, on the basis of support vector machines (SVMs). Adamson et al (2017) constructed an eventdriven manufacturing model on the basis of distributed collaborative CPSs; this model achieves distributed control and the adaptive matching of manufacturing resources and tasks.…”

Section: Modeling Theory and Smart Factory Methodsmentioning

confidence: 99%

Engineering management for high-end equipment intelligent manufacturing

Yang¹,

Wang²,

Shi³

et al. 2018

Front. Eng

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The high-end equipment intelligent manufacturing (HEIM) industry is of strategic importance to national and economic security. Engineering management (EM) for HEIM is a complex, innovative process that integrates natural science, technology, management science, social science, and the human spirit. Newgeneration information technology (IT), including the internet, cloud computing, big data, and artificial intelligence, have made a remarkable influence on HEIM and its engineering management activities, such as product system construction, product life cycle management, manufacturing resources organization, manufacturing model innovation, and reconstruction of the enterprise ecosystem. Engineering management for HEIM is a key topic at the frontier of international academic research. This study systematically reviews the current research on issues pertaining to engineering management for HEIM under the new-generation IT environment. These issues include cross-lifecycle management, network collaboration management, task integration management of innovative development, operation optimization of smart factories, quality and reliability management, information management, and intelligent decision making. The challenges presented by these issues and potential research opportunities are also summarized and discussed.

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SVM‐Based Dynamic Reconfiguration CPS for Manufacturing System in Industry 4.0

Cited by 24 publications

References 30 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

Multi-objective optimization of production scheduling with evolutionary computation: A review

Engineering management for high-end equipment intelligent manufacturing

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