Big Data Analytics In Smart Manufacturing

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Data Driven Smart Manufacturing Technologies and Applications

Data Driven Smart Manufacturing Technologies and Applications
Author :
Publisher : Springer Nature
Total Pages : 224
Release :
ISBN-10 : 9783030668495
ISBN-13 : 3030668495
Rating : 4/5 (495 Downloads)

Book Synopsis Data Driven Smart Manufacturing Technologies and Applications by : Weidong Li

Download or read book Data Driven Smart Manufacturing Technologies and Applications written by Weidong Li and published by Springer Nature. This book was released on 2021-02-20 with total page 224 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book reports innovative deep learning and big data analytics technologies for smart manufacturing applications. In this book, theoretical foundations, as well as the state-of-the-art and practical implementations for the relevant technologies, are covered. This book details the relevant applied research conducted by the authors in some important manufacturing applications, including intelligent prognosis on manufacturing processes, sustainable manufacturing and human-robot cooperation. Industrial case studies included in this book illustrate the design details of the algorithms and methodologies for the applications, in a bid to provide useful references to readers. Smart manufacturing aims to take advantage of advanced information and artificial intelligent technologies to enable flexibility in physical manufacturing processes to address increasingly dynamic markets. In recent years, the development of innovative deep learning and big data analytics algorithms is dramatic. Meanwhile, the algorithms and technologies have been widely applied to facilitate various manufacturing applications. It is essential to make a timely update on this subject considering its importance and rapid progress. This book offers a valuable resource for researchers in the smart manufacturing communities, as well as practicing engineers and decision makers in industry and all those interested in smart manufacturing and Industry 4.0.


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