New Industry 4.0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes

Edited by
March 2020
428 pages
  • ISBN978-3-03928-290-6 (Paperback)
  • ISBN978-3-03928-291-3 (PDF)

This book is a reprint of the Special Issue New Industry 4.0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes that was published in

Biology & Life Sciences
Chemistry & Materials Science
Computer Science & Mathematics
Environmental & Earth Sciences
Physical Sciences
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.
  • Paperback
© 2020 by the authors; CC BY-NC-ND license
cutting insert selection; cutting parameter optimization; artificial neural networks; genetic algorithm; connected enterprise; smart manufacturing; big data; machine learning; data reduction; predictive analytics; in-line dimensional inspection; warm forming; 3D mesh reconstruction; optical system; revolution workpiece; defect detection; polymer lithium-ion battery; convolutional neural network; deep learning; blister defect; flower pollination algorithm; Industry 4.0; anomaly detection; scheduling; neural network; skyline queries; Cyber-Physical Systems (CPS); scalability test; Internet of Things (IoT); INDUSTRY 4.0; economic recession; research and development indicators; maintenance expert; competence; decision support; micro-armature; defect detection; convolutional neural networks; computer vision; capacity control; job shop systems; RMTs; operator theory; 4th industrial revolution; industry 4.0; AHP; QFD; matching; fibre of preserved Szechuan pickle; contour detection; dilated convolutions; HED; social network; industry 4.0; industrial knowledge graph; deep learning; industrial big data; intellectualization of industrial information; digital manufacturing; smart factory; Industry 4.0; digital platforms; automated surface inspection; D-VGG16; bilinear model; Grad-CAM; classification; localization; elliptical paraboloid array; self-calibration method; vertex distance; optical slope sensor; geometric relationship; relative angle; fabric defect detection; LGM; FCM; image smoothing; Industry 4.0; marketing innovations; innovative marketing tools; impacts marketing innovations; Industry 4.0; configure-to-order; BIM; construction equipment; digital information flow; lean assembly; digital twins; cyber-physical production systems; depthwise separable convolution; YOLOv3; feature pyramid; aircraft structure crack detection; industrial load management; demand-side management; demand-side response; energy flexibility; IT concept; platform-based ecosystem; control service; smart service; control as a service; cloud-based control system; automation system; chatter; train wheel; smart system; turning; edge computing; n/a