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024 7 $a10.1007/978-3-031-32154-2
035   $a(MiAaPQ)EBC30562428
035   $a(Au-PeEL)EBL30562428
035   $a(OCoLC)1381479855
035   $a(DE-He213)978-3-031-32154-2
035   $a(BIP)090182350
035   $a(PPN)272263303
035   $a(CKB)26821627700041
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101 0 $aeng
135   $aurcnu||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
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200 10$aEngineering of Additive Manufacturing Features for Data-Driven Solutions $eSources, Techniques, Pipelines, and Applications /$fby Mutahar Safdar, Guy Lamouche, Padma Polash Paul, Gentry Wood, Yaoyao Fiona Zhao
205   $a1st ed. 2023.
210  1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2023.
215   $a1 online resource (151 pages)
225 1 $aSpringerBriefs in Applied Sciences and Technology,$x2191-5318
311 08$aPrint version: Safdar, Mutahar Engineering of Additive Manufacturing Features for Data-Driven Solutions Cham : Springer,c2023 9783031321535 
327   $aIntroduction -- Feature Engineering in AM -- Applications in Data-driven AM -- Analyzing AM Feature Spaces -- Challenges and Opportunities in AM Data Preparation -- Summary.
330   $aThis book is a comprehensive guide to the latest developments in data-driven additive manufacturing (AM). From data mining and pre-processing to signal processing, computer vision, and more, the book covers all the essential techniques for preparing AM data. Readers willl explore the key physical and synthetic sources of AM data throughout the life cycle of the process and learn about feature engineering techniques, pipelines, and resulting features, as well as their applications at each life cycle phase. With a focus on featurization efforts from reviewed literature, this book offers tabular summaries for major data sources and analyzes feature spaces at the design, process, and structure phases of AM to uncover trends and insights specific to feature engineering techniques. Finally, the book discusses current challenges and future directions, including AI/ML/DL readiness of AM data. Whether you're an expert or newcomer to the field, this book provides a broader summary of the status and future of data-driven AM technology.
410  0$aSpringerBriefs in Applied Sciences and Technology,$x2191-5318
606   $aIndustrial engineering
606   $aProduction engineering
606   $aEngineering?Data processing
606   $aArtificial intelligence
606   $aMachine learning
606   $aEducation
606   $aIndustrial and Production Engineering
606   $aData Engineering
606   $aArtificial Intelligence
606   $aMachine Learning
606   $aEducation
610   $aManufactures
610   $aTechnology & Engineering
615  0$aIndustrial engineering.
615  0$aProduction engineering.
615  0$aEngineering?Data processing.
615  0$aArtificial intelligence.
615  0$aMachine learning.
615  0$aEducation.
615 14$aIndustrial and Production Engineering.
615 24$aData Engineering.
615 24$aArtificial Intelligence.
615 24$aMachine Learning.
615 24$aEducation.
676   $a621.988
700   $aSafdar$b Mutahar$01365626
701   $aLamouche$b Guy$01365627
701   $aPaul$b Padma Polash$01365628
701   $aWood$b Gentry$01365629
701   $aZhao$b Yaoyao (Fiona)$01365630
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910728930303321
996   $aEngineering of Additive Manufacturing Features for Data-Driven Solutions$93387804
997   $aUNINA