000			03631cam a22005178i 4500
001			9781003167372
003			FlBoTFG
005			20240213122832.0
006			m o d
007			cr |||||||||||
008			210928s2022 flu ob 001 0 eng
040			_aOCoLC-P _beng _erda _cOCoLC-P
020			_a9781003167372 _q(ebk)
020			_a1003167373
020			_z9780367765279 _q(hbk)
020			_z9780367765286 _q(pbk)
020			_a9781000541380 _q(electronic bk. : EPUB)
020			_a100054138X _q(electronic bk. : EPUB)
020			_a9781000541335 _q(electronic bk. : PDF)
020			_a1000541339 _q(electronic bk. : PDF)
035			_a(OCoLC)1273727631
035			_a(OCoLC-P)1273727631
050	0	0	_aTA483
072		7	_aTEC _x021030 _2bisacsh
072		7	_aTEC _x023000 _2bisacsh
072		7	_aTGM _2bicssc
082	0	0	_a620.1/6 _223/eng/20211117
100	1		_aJha, Rajesh, _eauthor.
245	1	0	_aArtificial intelligence-aided materials design : _bAI-algorithms and case studies on alloys and metallurgical processes / _cRajesh Jha and Bimal Kumar Jha.
250			_aFirst edition.
264		1	_aBoca Raton, FL : _bCRC Press, _c2022.
300			_a1 online resource
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
520			_a"This book describes the application of artificial intelligence (AI)/machine learning (ML) concepts to develop predictive models that can be used to design alloy materials. Readers new to AI/ML algorithms can use the book as a starting point and use the included MATLAB and Python implementation of AI/ML algorithms through included case studies. Experienced AI/ML researchers who want to try new algorithms can use this book and study the case studies for reference. This book is written for materials scientists and metallurgists interested in the application of AI, ML, and data science in the development of new materials"-- _cProvided by publisher.
505	0		_a1. Introduction. 2. Metallurgical/Materials Concepts. 3. Artificial Intelligence Algorithms. 4. Case Study 1: Nanomechanics and Nanotribology: Combined Machine Learning-Experimental Approach. 5. Case Study 2: Design of Hard Magnetic Alnico Alloys: Combined Machine Learning-Experimental Approach. 6. Case Study 3: Design of Soft Magnetic Finemet Type Alloys: Combined Machine Learning-CALPHAD Approach. 7. Case Study 4: Design of Nickel-Base Superalloys: Combined Machine Learning-CALPHAD Approach. 8. Case Study 5: Design of Aluminum Alloys: Combined Machine Learning-CALPHAD Approach. 9. Case Study 6: Design of Titanium Alloys for High-Temperature Application: Combined Machine Learning-CALPHAD Approach. 10. Case Study 7: Design of Titanium Based Biomaterials: Combined Machine Learning-CALPHAD Approach. 11. Case Study 8: Industrial Furnaces I: Application of Machine Learning on an Industrial Iron Making Blast Furnace Data. 12. Case Study 9: Industrial Furnaces II: Application of Machine Learning Algorithms on an Industrial LD Steel Making Furnace Data. 13. Software/Codes Included with this Book. 14. Conclusion.
588			_aOCLC-licensed vendor bibliographic record.
650		0	_aMetallurgy _xData processing.
650		0	_aAlloys _xData processing.
650		0	_aArtificial intelligence _xIndustrial applications.
650		7	_aTECHNOLOGY / Metallurgy _2bisacsh
700	1		_aJha, B. K. _q(Bimal K.), _eauthor.
856	4	0	_3Taylor & Francis _uhttps://www.taylorfrancis.com/books/9781003167372
856	4	2	_3OCLC metadata license agreement _uhttp://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
999			_c5945 _d5945