LEADER 05850nam 22007935 450 001 9910407736303321 005 20220919215534.0 010 $a981-15-4565-0 024 7 $a10.1007/978-981-15-4565-8 035 $a(OCoLC)1159168557 035 $a(CKB)5280000000218455 035 $a(MiAaPQ)EBC6225718 035 $a(DE-He213)978-981-15-4565-8 035 $a(PPN)248593374 035 $a(EXLCZ)995280000000218455 100 $a20200609d2020 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAdvances in Intelligent Manufacturing$b[electronic resource] $eSelect Proceedings of ICFMMP 2019 /$fedited by Grzegorz Krolczyk, Chander Prakash, Sunpreet Singh, Joao Paulo Davim 205 $a1st ed. 2020. 210 1$aSingapore :$cSpringer Singapore :$cImprint: Springer,$d2020. 215 $a1 online resource (220 pages) 225 1 $aLecture Notes in Mechanical Engineering,$x2195-4356 311 $a981-15-4564-2 320 $aIncludes bibliographical references. 327 $aSix Sigma Methodology and Implementation in Indian Context: A Review Based Study -- Evaluation of work posture using ergonomics in Indian small scale industry -- Manufacturing Intelligence in the Context of Indian SME?s -- Multi-objective optimization of process parameter during dry Turning of Grade5 Titanium alloy with carbide inserts: Hybrid Fuzzy-TOPSIS approach -- Analysis of Factors for Green Supply Chain in Indian Timber Market: An ISM Approach -- The Value Engineering Way: A Case of Industrial Fans -- Obstacles of Flexible Manufacturing System: An AHP Approach for Quantifying the Rank Given by the ISM Model -- To Find the Effectiveness of Barriers in Reverse Logistics by using ISM -- Optimization of MQL Machining Parameters Using Combined Taguchi and TOPSIS Method -- Life Cycle Assessment Framework for Sustainable Development in Manufacturing Environment -- Artificial Neural Network Models for the Prediction of Metal Removal Rate in Rotary Ultrasonic Machining -- Finding the Percentage Effectiveness of Agile Manufacturing Barriers: An AHP Approach -- Modeling the Knowledge Sharing Barriers in Indian Small and Medium Industries using ISM -- Simulation and Optimization of Flexible Flow Shop Scheduling Problem in an Indian Manufacturing Industry -- Computational Investigation on the Thermal Characteristics of Heat Pipe Using Nanofluids -- Analysis of Barriers to Lean-Green Manufacturing System (Lgms): A Multi-Criteria Decision Making Approach -- Machine Learning Application for Pulsating Flow Through -- Ranking of Factors for Integrated Lean, Green and Agile Manufacturing for Indian Manufacturing Sme?s. 330 $aThis book consists of select proceedings of the International Conference on Functional Material, Manufacturing and Performances (ICFMMP) 2019, and presents latest research on using the combined intelligence of people, processes, and machines to impact the overall economics of manufacturing. The book focuses on optimizing manufacturing resources, improving business value and safety, and reducing waste ? both on the floor and in back-office operations. It highlights the applications of the latest manufacturing execution system (MES), intelligent devices, machine-to-machine communication, and data analysis for the production lines and facilities. This book will be useful to manufacturers of finished goods and of sub-assemblies in the automotive, agriculture, and construction equipment sector. It will also provide solutions to make production strategies exceptional and can be a useful reference for beginners, researchers, and professionals interested in intelligent manufacturing technologies. 410 0$aLecture Notes in Mechanical Engineering,$x2195-4356 606 $aManufactures 606 $aComputational intelligence 606 $aControl engineering 606 $aRobotics 606 $aMechatronics 606 $aEngineering economics 606 $aEngineering economy 606 $aArtificial intelligence 606 $aManufacturing, Machines, Tools, Processes$3https://scigraph.springernature.com/ontologies/product-market-codes/T22050 606 $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014 606 $aControl, Robotics, Mechatronics$3https://scigraph.springernature.com/ontologies/product-market-codes/T19000 606 $aEngineering Economics, Organization, Logistics, Marketing$3https://scigraph.springernature.com/ontologies/product-market-codes/T22016 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 615 0$aManufactures. 615 0$aComputational intelligence. 615 0$aControl engineering. 615 0$aRobotics. 615 0$aMechatronics. 615 0$aEngineering economics. 615 0$aEngineering economy. 615 0$aArtificial intelligence. 615 14$aManufacturing, Machines, Tools, Processes. 615 24$aComputational Intelligence. 615 24$aControl, Robotics, Mechatronics. 615 24$aEngineering Economics, Organization, Logistics, Marketing. 615 24$aArtificial Intelligence. 676 $a670.42 702 $aKrolczyk$b Grzegorz$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aPrakash$b Chander$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aSingh$b Sunpreet$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aDavim$b Joao Paulo$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910407736303321 996 $aAdvances in Intelligent Manufacturing$92201462 997 $aUNINA