New Industry 4.0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes
Posada, Jorge
New Industry 4.0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes - MDPI - Multidisciplinary Digital Publishing Institute 2020 - 1 electronic resource (428 p.)
Open Access
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.
Creative Commons
English
books978-3-03928-291-3 9783039282906 9783039282913
10.3390/books978-3-03928-291-3 doi
localization smart system n/a connected enterprise digital manufacturing AHP YOLOv3 decision support neural network vertex distance depthwise separable convolution cutting insert selection smart service contour detection convolutional neural networks platform-based ecosystem in-line dimensional inspection dilated convolutions fabric defect detection classification FCM LGM digital information flow turning computer vision control service blister defect RMTs feature pyramid research and development indicators maintenance expert polymer lithium-ion battery IT concept Industry 4.0 matching data reduction competence fibre of preserved Szechuan pickle elliptical paraboloid array relative angle geometric relationship optical system configure-to-order aircraft structure crack detection digital twins smart factory D-VGG16 optical slope sensor smart manufacturing self-calibration method convolutional neural network industry 4.0 skyline queries machine learning scalability test cyber-physical production systems Cyber-Physical Systems (CPS) demand-side response cutting parameter optimization image smoothing marketing innovations genetic algorithm automation system defect detection scheduling job shop systems big data operator theory micro-armature train wheel industrial knowledge graph industrial load management bilinear model artificial neural networks 4th industrial revolution INDUSTRY 4.0 construction equipment lean assembly capacity control Grad-CAM revolution workpiece chatter anomaly detection QFD social network deep learning control as a service warm forming automated surface inspection cloud-based control system innovative marketing tools Internet of Things (IoT) flower pollination algorithm HED edge computing predictive analytics BIM digital platforms industrial big data energy flexibility impacts marketing innovations intellectualization of industrial information economic recession 3D mesh reconstruction demand-side management
New Industry 4.0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes - MDPI - Multidisciplinary Digital Publishing Institute 2020 - 1 electronic resource (428 p.)
Open Access
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.
Creative Commons
English
books978-3-03928-291-3 9783039282906 9783039282913
10.3390/books978-3-03928-291-3 doi
localization smart system n/a connected enterprise digital manufacturing AHP YOLOv3 decision support neural network vertex distance depthwise separable convolution cutting insert selection smart service contour detection convolutional neural networks platform-based ecosystem in-line dimensional inspection dilated convolutions fabric defect detection classification FCM LGM digital information flow turning computer vision control service blister defect RMTs feature pyramid research and development indicators maintenance expert polymer lithium-ion battery IT concept Industry 4.0 matching data reduction competence fibre of preserved Szechuan pickle elliptical paraboloid array relative angle geometric relationship optical system configure-to-order aircraft structure crack detection digital twins smart factory D-VGG16 optical slope sensor smart manufacturing self-calibration method convolutional neural network industry 4.0 skyline queries machine learning scalability test cyber-physical production systems Cyber-Physical Systems (CPS) demand-side response cutting parameter optimization image smoothing marketing innovations genetic algorithm automation system defect detection scheduling job shop systems big data operator theory micro-armature train wheel industrial knowledge graph industrial load management bilinear model artificial neural networks 4th industrial revolution INDUSTRY 4.0 construction equipment lean assembly capacity control Grad-CAM revolution workpiece chatter anomaly detection QFD social network deep learning control as a service warm forming automated surface inspection cloud-based control system innovative marketing tools Internet of Things (IoT) flower pollination algorithm HED edge computing predictive analytics BIM digital platforms industrial big data energy flexibility impacts marketing innovations intellectualization of industrial information economic recession 3D mesh reconstruction demand-side management