ScholarWorks@SUNGKYUNKWAN UNIVERSITY: LEE, SANG WON

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LEE, SANG WON (이상원)

Engineering (Mechanical Engineering)

Research Area: Vision: ''Flash is disk, disk is tape, and tape is dead.''(by Jim Gray)/ Current Research: ''Database systems on flash memory''/ All aspects of database techniques for flash memory and NVRAM storage:

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Nanofluid minimum quantity lubrication:8||Titanium alloy:7||Additive manufacturing:5||Microdrilling process:4||Milling process:4||Service element:2||Machine learning:2||Size effect:2||Metallic material:2||Product element:2||Numerical analysis:2||Microcoining:2||Multiwall carbon nanotubes:2||Productservice systems (PSS):2||Nanodiamond:2||Microforming system:2||Protocol analysis:2||Thermal characteristics:2||Artificial intelligence:2||Nanosolid dry lubrication:2||Thrust force:2||Torque:2||Design process:2||Lens injection moulding process:2||PSS representation:2||Datadriven approach:2||Parametric Analysis:2||Parametric Study:2||Multidirectional Carbon fiber reinforced plastics (MDCFRP):2||Service design:2||Condition monitoring:1||Contamination:1||Contextbased Activity Modeling:1||Cryogenic cooling:1||Crystallization:1||Data augmentation:1||Data imagification:1||Data mining:1||Data models:1||Data visualization:1||Deep learning:1||Deformation load (변형 힘):1||Deposition defect:1||Design activity:1||Design education:1||Design for Affordance:1||Design information:1||Design of experiment (DOE):1||Design thinking process:1||Desktop dry EDM machine:1||Diesinking EDM:1||Digital Twin:1||Directed Energy Deposition (DED):1||Directed energy deposition:1||Directed energy deposition (DED):1||Discrete phase model (DPM):1||Drilling Process:1||Dry electrical discharge machining:1||Dynamic resistance (DR):1||EDM parameter:1||Early design phases:1||Ecofriendly microgrinding:1||Energy user needs:1||Environmental characteristics:1||Environmentallyfriendly micro endmilling:1||Experience Values:1||Experience design:1||Experimental analysis:1||Explainable artificial intelligence:1||Fault detection and diagnosis:1||Fault diagnosis:1||Feature Extraction (특징요소 추출):1||Feature extraction:1||Force:1||Formability (성형성):1||Freque:1||Functionally graded material:1||Fused deposition modeling:1||GA optimisation:1||Generative adversarial network:1||Grapbene nanoplates:1||Graphene:1||Graphene nanoplatelets:1||High speed train:1||Hole quality:1||Human behaviour in design:1||Hybrid manufacturing:1||Image preprocessing:1||In situ meltpool monitoring:1||Industrial data generation:1||Industrial site application (산업현장적용):1||Industry 4:1||Injection molding condition diagnosis (사출성형공정상태진단):1||Interaction process analysis:1||Interdisciplinary design:1||Interdisciplinary education:1||Interdisciplinary learning:1||Laser:1||Lens Injection Molding Process (렌즈 사출성형 공정):1||Lens injection molding (렌즈 사출성형):1||Machinability:1||Machine learning algorithm:1||Machine tools:1||Machining:1||Machining characteristics:1||Manufacturing process:1||Material removal rate:1||Mechanical property:1||MicroDrilling Process:1||Microdrilling:1||Microend milling:1||Microgrinding:1||Microgrinding process:1||Micromilling:1||Microscale grinding process:1||Microscale deep drawing (마이크로 딥드로잉):1||Microstructure:1||Milling:1||Mist behaviors:1||Multidirectional Carbon fiber reinforced plastic (MDCFRP):1||Multiobjective Optimization:1||Multiobjective optimazation:1||Multiobjective optimization:1||Multiple defect diagnosis:1||Nanosold dry lubrication:1||Nanosolid Dry Lubrication:1||Nanosolid lubrication:1||Nanodiamond:1||Nanodiamond particle:1||Nanofluid:1||Nanofluid Minimum Quantity Lubrication:1||Nanofluid Minimum Quantity Lubrication (MQL):1||Nanofluid minimum quantity lubrication milling:1||Nd:YAG laser:1||Node Energy (노드 에너지):1||Object detection:1||Online monitoring and diagnosis:1||Online tool condition monitoring and diagnosis system:1||Optimal spray condition:1||Optimization:1||Organonachip:1||PSS design process:1||PSS design support tools:1||PSS designscape:1||PSS functionbased design tool:1||PSS modified service blueprint tool:1||Personal creativity modes:1||Polyethylene:1||Probabilistic Neural Network (확률신경망):1||Process Monitoring:1||Process monitoring and diagnosis:1||ProductService Systems (PSS) design:1||Productservice system:1||Productservice systems:1||Product/service systems:1||Prognostics and health management (PHM):1||RealTime system (실시간시스템):1||Realtime validation:1||Recommender system:1||Recycled materials:1||Response Surface Methodology:1||Response surface method (RSM):1||Response surface methodology (RSM):1||Robot sensing systems:1||Robot spotwelding:1||Scaffold:1||Selective laser sintering (:1||Service Design:1||Silicon:1||Similarity measurement:1||Size effect (크기 효과):1||Sliding mesh:1||Software module:1||Spray cooling:1||Stainless steel powder:1||Support vector machine algorithm:1||Suspension:1||Sustainability:1||Team interaction:1||Thermoplastic composite:1||Threedimensional:1||Turning process:1||User centred design:1||Userfriendly Interface:1||Villi:1||WSi:1||Wavelet transform (WT):1||Wet electrical discharge machining:1||Whisker:1||Wire EDM:1||YoLov3:1||additive manufacturing:1||automated processes:1||automation:1||backpropagation neural network:1||biodegradable polymer:1||builtinsensor mould:1||cavity pressure and temperature:1||condition diagnosis:1||controlled material characteristics:1||deep learning:1||directed energy deposition:1||discrete phase model:1||explainable fault diagnosis:1||fault diagnosis:1||form error estimation:1||functionally graded material:1||functionally graded materials (FGMs):1||fuzzy inference system (FIS):1||garment manufacturing processes:1||gradientweighted class activation mapping (GradCAM):1||graphene nanoplatelets:1||grinding force feature extraction:1||hollow glass beads:1||inprocess cavity pressure signal:1||industrial robot:1||kfold cross validation:1||laser material processing:1||lens form error prediction:1||lightweight:1||machining performance:1||material extrusion:1||micro factory:1||microdrilling:1||microhardness testing:1||microstructure analysis:1||multidirectional carbon fiberreinforced plastic (MDCFRP):1||nanosolid dry lubrication:1||phase change:1||polycaprolactone:1||postmachining:1||prognostics and health management (PHM):1||response surface methodology:1||robotic spotwelding (RSW):1||sliding mesh:1||smart clothing:1||smart factory:1||spot welding:1||surrogate model:1||thermocompression bonding design:1||tool condition diagnosis:1||transfer learning:1||tribological behavior:1||uncertainty quantification:1||volume of fluid method:1||0:1||wavelet packet decomposition:1||3D printing:1||ANN:1||Activity modelling tool:1||Adaptive neuro fuzzy inference system (ANFIS):1||Additive Manufacturing:1||Air flow assisted electrospray lubrication (AFESL):1||Amorphous Si thin f:1||Angular misalignment:1||Anomaly detection:1||Autoencoder:1||Automated visual inspection:1||Autonomous data labeling:1||Backpropagation neural network (BPNN):1||Biodegradable polymer:1||Blank holder gap (블랭크 홀더 간극):1||Building energy saving:1||BuiltinSensor mold (센서장착금형):1||CVD:1||Carbon Fiber Reinforced Plastics:1||Carbon fiber reinforced plastic (CFRP):1||Case indexing:1||Case representation:1||Chilly CO2 gas cooling:1||Chilly gas:1||Class imbalance:1||Coating boundary pinpointing:1||Composite:1||Computational fluid dynamics analysis:1||Condition Diagnosis Algorithm (상태 진단 알고리즘):1||

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(03063) 25-2, SUNGKYUNKWAN-RO, JONGNO-GU, SEOUL, KOREAsamsunglib@skku.edu

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