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210512 ||| eng |
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|a 9783039282906
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|a books978-3-03928-291-3
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|a 9783039282913
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|a Posada, Jorge
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| 245 |
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|a New Industry 4.0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes
|h Elektronische Ressource
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| 260 |
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|b MDPI - Multidisciplinary Digital Publishing Institute
|c 2020
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| 300 |
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|a 1 electronic resource (428 p.)
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| 653 |
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|a IT concept
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|a machine learning
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|a fabric defect detection
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|a digital twins
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|a Industry 4.0
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|a 3D mesh reconstruction
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|a impacts marketing innovations
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|a predictive analytics
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|a FCM
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| 653 |
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|a genetic algorithm
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| 653 |
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|a scheduling
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| 653 |
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|a industrial knowledge graph
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|a big data
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| 653 |
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|a AHP
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|a deep learning
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|a localization
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|a cutting insert selection
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| 653 |
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|a cyber-physical production systems
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| 653 |
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|a construction equipment
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| 653 |
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|a platform-based ecosystem
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|a control service
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| 653 |
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|a aircraft structure crack detection
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|a operator theory
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|a dilated convolutions
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|a cutting parameter optimization
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|a image smoothing
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| 653 |
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|a 4th industrial revolution
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| 653 |
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|a polymer lithium-ion battery
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| 653 |
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|a YOLOv3
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| 653 |
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|a automation system
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| 653 |
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|a decision support
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| 653 |
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|a Cyber-Physical Systems (CPS)
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| 653 |
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|a industrial big data
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| 653 |
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|a bilinear model
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|a digital information flow
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| 653 |
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|a elliptical paraboloid array
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|a optical system
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| 653 |
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|a innovative marketing tools
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| 653 |
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|a chatter
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| 653 |
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|a convolutional neural networks
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| 653 |
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|a warm forming
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| 653 |
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|a relative angle
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| 653 |
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|a D-VGG16
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| 653 |
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|a automated surface inspection
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| 653 |
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|a computer vision
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| 653 |
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|a feature pyramid
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| 653 |
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|a scalability test
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| 653 |
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|a LGM
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| 653 |
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|a revolution workpiece
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| 653 |
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|a edge computing
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| 653 |
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|a Internet of Things (IoT)
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| 653 |
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|a vertex distance
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| 653 |
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|a anomaly detection
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| 653 |
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|a depthwise separable convolution
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| 653 |
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|a industry 4.0
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|a demand-side management
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| 653 |
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|a lean assembly
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|a smart service
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| 653 |
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|a industrial load management
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| 653 |
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|a RMTs
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| 653 |
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|a micro-armature
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| 653 |
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|a energy flexibility
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| 653 |
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|a digital platforms
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| 653 |
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|a fibre of preserved Szechuan pickle
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| 653 |
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|a maintenance expert
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| 653 |
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|a n/a
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|a optical slope sensor
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| 653 |
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|a geometric relationship
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| 653 |
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|a intellectualization of industrial information
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| 653 |
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|a social network
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| 653 |
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|a demand-side response
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| 653 |
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|a History of engineering and technology / bicssc
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| 653 |
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|a flower pollination algorithm
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| 653 |
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|a in-line dimensional inspection
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| 653 |
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|a research and development indicators
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| 653 |
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|a turning
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| 653 |
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|a control as a service
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| 653 |
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|a BIM
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|a cloud-based control system
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| 653 |
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|a capacity control
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| 653 |
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|a HED
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| 653 |
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|a connected enterprise
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| 653 |
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|a marketing innovations
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| 653 |
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|a train wheel
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| 653 |
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|a digital manufacturing
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| 653 |
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|a smart system
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| 653 |
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|a defect detection
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| 653 |
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|a QFD
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| 653 |
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|a Grad-CAM
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| 653 |
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|a matching
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| 653 |
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|a configure-to-order
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| 653 |
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|a self-calibration method
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| 653 |
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|a data reduction
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| 653 |
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|a smart manufacturing
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|a classification
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|a smart factory
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|a economic recession
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| 653 |
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|a artificial neural networks
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| 653 |
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|a skyline queries
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| 653 |
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|a convolutional neural network
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| 653 |
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|a blister defect
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| 653 |
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|a job shop systems
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| 653 |
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|a competence
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|a neural network
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|a INDUSTRY 4.0
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| 653 |
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|a contour detection
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| 700 |
1 |
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|a López de Lacalle, Luis Norberto
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b DOAB
|a Directory of Open Access Books
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| 500 |
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|a Creative Commons (cc), https://creativecommons.org/licenses/by-nc-nd/4.0/
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| 028 |
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|a 10.3390/books978-3-03928-291-3
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| 856 |
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|u https://www.mdpi.com/books/pdfview/book/2109
|7 0
|x Verlag
|3 Volltext
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| 856 |
4 |
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|u https://directory.doabooks.org/handle/20.500.12854/54583
|z DOAB: description of the publication
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|a Modern factories are experiencing rapid digital transformation supported by emerging technologies, such as the Industrial Internet of things (IIOT), industrial big data and cloud technologies, deep learning and deep analytics, AI, intelligent robotics, cyber-physical systems and digital twins, complemented by visual computing (including new forms of artificial vision with machine learning, novel HMI, simulation, and visualization). This is evident in the global trend of Industry 4.0. The impact of these technologies is clear in the context of high-performance manufacturing. Important improvements can be achieved in productivity, systems reliability, quality verification, etc. Manufacturing processes, based on advanced mechanical principles, are enhanced by big data analytics on industrial sensor data. In current machine tools and systems, complex sensors gather useful data, which is captured, stored, and processed with edge, fog, or cloud computing. These processes improve with digital monitoring, visual data analytics, AI, and computer vision to achieve a more productive and reliable smart factory. New value chains are also emerging from these technological changes. This book addresses these topics, including contributions deployed in production, as well as general aspects of Industry 4.0.
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