LEADER 04135nam 22006855 450 001 9910869168203321 005 20240626125347.0 010 $a9783031574962 010 $a3031574966 024 7 $a10.1007/978-3-031-57496-2 035 $a(MiAaPQ)EBC31505439 035 $a(Au-PeEL)EBL31505439 035 $a(CKB)32580019400041 035 $a(DE-He213)978-3-031-57496-2 035 $a(Exl-AI)31505439 035 $a(OCoLC)1443932519 035 $a(EXLCZ)9932580019400041 100 $a20240626d2024 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAdvances in Artificial Intelligence in Manufacturing $eProceedings of the 1st European Symposium on Artificial Intelligence in Manufacturing, September 19, 2023, Kaiserslautern, Germany /$fedited by Achim Wagner, Kosmas Alexopoulos, Sotiris Makris 205 $a1st ed. 2024. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2024. 215 $a1 online resource (207 pages) 225 1 $aLecture Notes in Mechanical Engineering,$x2195-4364 311 08$a9783031574955 311 08$a3031574958 327 $a Preface -- Organization -- Contents -- Artificial Intelligence at Manufacturing System Level -- An Integrated Active Learning Framework for the Deployment of Machine Learning Models for Defect Detection in Manufacturing Environments -- 1 Introduction -- 2 The Problem -- 2.1 Active Learning -- 2.2 Deployment -- 2.3 Monitoring -- 2.4 Explainability -- 3 Use Cases -- 3.1 Binary Classification -- 3.2 Multiclass Classification -- 3.3 Object Detection -- 4 Results -- 4.1 Binary Classification -- 4.2 Multiclass Classification -- 4.3 Object Detection -- 4.4 MLOps -- 5 Conclusions -- 6 Aknowlegments -- References -- Complex and Big Data Handling and Monitoring Through Machine Learning Towards Digital-Twin in High Precision Manufacturing -- 1 Introduction -- 2 Brief Overview of the State-of-the-Art -- 3 Real Case Studies -- 4 Proposed Methods -- 4.1 Low-Dimensional Learning for Machine Health Condition Monitoring -- 4.2 Recurrent NNs for Multi-stream Process Pattern Prediction$7Generated by AI. 330 $aThis book reports on recent developments of artificial intelligence applications in the manufacturing industry. Gathering contributions to the first European Symposium on Artificial Intelligence in Manufacturing, held on September 19, 2023, in Kaiserslautern, Germany, it reports on machine learning models and algorithms for systems monitoring and industrial data management, on advances in human-robot collaboration, and on artificial intelligence applications in industrial control and process monitoring. Giving a special emphasis to the integration of artificial intelligence in manufacturing systems and processes, this book offers a timely and practice-oriented guide to a multidisciplinary audience of engineering researchers, software developers and industrial managers. 410 0$aLecture Notes in Mechanical Engineering,$x2195-4364 606 $aAutomation 606 $aIndustrial engineering 606 $aProduction engineering 606 $aHuman-machine systems 606 $aAutomation 606 $aIndustrial and Production Engineering 606 $aHuman-Machine Interfaces 606 $aProcess Engineering 615 0$aAutomation. 615 0$aIndustrial engineering. 615 0$aProduction engineering. 615 0$aHuman-machine systems. 615 14$aAutomation. 615 24$aIndustrial and Production Engineering. 615 24$aHuman-Machine Interfaces. 615 24$aProcess Engineering. 676 $a629.8 700 $aWagner$b Achim$01743503 701 $aAlexopoulos$b Kosmas$01743504 701 $aMakris$b Sotiris$01229427 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910869168203321 996 $aAdvances in Artificial Intelligence in Manufacturing$94171682 997 $aUNINA