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710	2		_aSpringerLink (Online service) _9106996
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005			20230102113519.0
008			181215s2019 gw a o |||| 0|eng d
020			_a9783030049034
024	7		_a10.1007/978-3-030-04903-4 _2doi
040			_bspa _dES-MaUEC _cES-MaUEC
050		4	_aTS228.2 _b2019 EB
100	1		_aChaki, Sudipto _eautor _9671214
245	1	0	_aModelling and optimisation of Laser Assisted Oxygen (LASOX) cutting : _ba soft computing based approach _cby Sudipto Chaki, Sujit Ghosal
264		1	_aCham _bSpringer International Publishing : _bImprint: Springer _c2019
300			_a1 recurso en línea (IX, 56 páginas)
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF _2rda
490	0		_aSpringerBriefs in Computational Intelligence _x2625-3704
490	0		_aIntelligent Technologies and Robotics (Springer-42732)
505	0		_aPreface -- Chapter 1. LASOX Cutting: Principles and Evolution -- Chapter 2. Integrated Soft Computing Based Methodologies for Modelling -- Chapter 3. Modelling and Optimisation of LASOX Cutting of Mild Steel: A Case Study.
520	3		_aThis book presents the basics of the Laser Assisted Oxygen (LASOX) cutting process, its development, advantages and shortcomings, together with detailed information on the research work carried out to date regarding the modelling and optimization of the process. It introduces two integrated soft computing-based models consisting of Artificial Neural Networks (ANN-GA and ANN SA) for the modelling and optimization of LASOX cutting. It also includes an in-depth discussion on the basic working algorithms of soft computing tools such as Artificial Neural Networks, Genetic Algorithms, Simulated Annealing etc. The book not only provides an approach to optimizing LASOX by means of soft computing-based integrated models, but also illustrates the practical implementation of the proposed models.
988			_aPrimersemestre_2019_Robotics
650		7	_2embne _9671215 _aOxicorte
650		7	_2embne _9671216 _aSoldadura oxiacetilénica
650		7	_2embne _9345745 _aMetales _xCorte
700	1		_aGhosal, Sujit. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
776	0	8	_iPrinted edition: _z9783030049027
776	0	8	_iPrinted edition: _z9783030049041
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-04903-4 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_aSI _cm _dz _feng _ggw _h0 _b10/2019 _eel _zSI