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

• Main Author: Posada, Jorge (auth)
• Other Authors: López de Lacalle, Luis Norberto (auth)
• Format: Electronic Book Chapter
• Language: English
• Published: MDPI - Multidisciplinary Digital Publishing Institute 2020
• Subjects: History of engineering & technology; 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
• Online Access: DOAB: download the publication; DOAB: description of the publication

Summary

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.

Physical Description

1 electronic resource (428 p.)

ISBN

books978-3-03928-291-3
9783039282906
9783039282913

Access

Open Access