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020 _a9783031305108
_9978-3-031-30510-8
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072 7 _aTGP
_2bicssc
072 7 _aTEC009060
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072 7 _aTGP
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082 0 4 _a670
_223
245 1 0 _aArtificial Intelligence for Smart Manufacturing
_h[electronic resource] :
_bMethods, Applications, and Challenges /
_cedited by Kim Phuc Tran.
250 _a1st ed. 2023.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2023.
300 _aVI, 269 p. 66 illus., 55 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringer Series in Reliability Engineering,
_x2196-999X
500 _aAcceso multiusuario
505 0 _aChapter 1: Introduction to Artificial Intelligence for Smart Manufacturing -- Chapter 2: Artificial Intelligence for Smart Manufacturing in Industry 5.0: Methods, Applications, and Challenges -- Chapter 3: Quality control for Smart Manufacturing in Industry 5.0 -- Chapter 4: Dynamic Process Monitoring Using Machine Learning Control Charts -- Chapter 5: Fault Prediction of Papermaking Process Based on Gaussian Mixture Model and Mahalanobis Distance -- Chapter 6: Multi-objective optimization of flexible flow-shop intelligent scheduling based on a hybrid intelligent algorithm -- Chapter 7: Personalized pattern recommendation system of men's shirts -- Chapter 8: Efficient and Trustworthy Federated Learning-based Explainable Anomaly Detection: Challenges, Methods, and Future Directions -- Chapter 9: Multimodal machine learning in prognostics and health management of manufacturing systems -- Chapter 10: Explainable Artificial Intelligence for Cybersecurity in Smart Manufacturing -- Chapter 11: Wearable technology for Smart Manufacturing in Industry 5.0 -- Chapter 12: Benefits of using Digital Twin for online fault diagnosis of a manufacturing system.
520 _aThis book provides readers with a comprehensive overview of the latest developments in the field of smart manufacturing, exploring theoretical research, technological advancements, and practical applications of AI approaches. With Industry 4.0 paving the way for intelligent systems and innovative technologies to enhance productivity and quality, the transition to Industry 5.0 has introduced a new concept known as augmented intelligence (AuI), combining artificial intelligence (AI) with human intelligence (HI). As the demand for smart manufacturing continues to grow, this book serves as a valuable resource for professionals and practitioners looking to stay up-to-date with the latest advancements in Industry 5.0. Covering a range of important topics such as product design, predictive maintenance, quality control, digital twin, wearable technology, quantum, and machine learning, the book also features insightful case studies that demonstrate the practical application of these tools in real-world scenarios. Overall, this book provides a comprehensive and up-to-date account of the latest advancements in smart manufacturing, offering readers a valuable resource for navigating the challenges and opportunities presented by Industry 5.0.
541 _fUABC ;
_cPerpetuidad
650 0 _aIndustrial engineering.
650 0 _aProduction engineering.
650 0 _aStatistics .
650 0 _aMachine learning.
650 1 4 _aIndustrial and Production Engineering.
650 2 4 _aApplied Statistics.
650 2 4 _aMachine Learning.
700 1 _aTran, Kim Phuc.
_eeditor.
_0(orcid)0000-0002-6005-1497
_1https://orcid.org/0000-0002-6005-1497
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031305092
776 0 8 _iPrinted edition:
_z9783031305115
776 0 8 _iPrinted edition:
_z9783031305122
830 0 _aSpringer Series in Reliability Engineering,
_x2196-999X
856 4 0 _zLibro electrónico
_uhttp://libcon.rec.uabc.mx:2048/login?url=https://doi.org/10.1007/978-3-031-30510-8
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cLIBRO_ELEC
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