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020			_a9783030668495
024	7		_a10.1007/978-3-030-66849-5 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _erda _dES-MaUEC
050		4	_aTS183 _b2021 EB
245	0	0	_aData Driven Smart Manufacturing Technologies and Applications _cedited by Weidong Li, Yuchen Liang, Sheng Wang
250			_aFirst edition 2021
264		1	_aCham _bSpringer International Publishing _c2021
300			_a1 recurso en línea (IX, 218 páginas) _b143 ilustraciones, 130 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_aarchivo de texto _bPDF
490	0		_aSpringer Series in Advanced Manufacturing _x1860-5168
490	0		_aEngineering (SpringerNature-11647)
490	0		_aEngineering (R0) (SpringerNature-43712)
505	0		_aPart I: Introduction and Fundamental -- Introduction -- Big Data Analytics and Deep Learning Algorithms -- Part II: Survey -- Intelligent Manufacturing Prognosis: A Survey -- Sustainable Manufacturing Enabled by Artificial Intelligence: A Survey -- Human-Robot Collaboration and Artificial Intelligence: A Survey -- Part III: Applications and Case Studies -- Fog Computing and Convolutional Neural Network Enabled Machining Prognosis and Optimisation -- Big Data Enabled Intelligent Immune System for Energy Efficient Manufacturing Management -- Tool Wear Prognosis Using Deep Learning Algorithms -- Big Data Analytics Supported Close-loop Machining Control and Optimisation -- Intelligent Learning from Demonstrators for Human-Robot Collaboration -- Human-Robot Collaboration and Intelligent Welding Applications -- Deep Learning Driven Intelligent Welding Robotics.
520	3		_aThis 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.
988			_aSpringer_Engineering_2021
650		7	_2embne _aProcesos de fabricación _9163432
650		7	_2embne _9173416 _aProducción _xPlanificación
700	1		_aLi, Weidong _eeditor literario _0(orcid)0000-0001-5559-7834 _1https://orcid.org/0000-0001-5559-7834 _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aLiang, Yuchen _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aWang, Sheng _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-66849-5 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE _n0
998			_b04/2021 _dz _eb _zSI