Just Do it Lab
of Additive Manufacturing & Semiconductor Convergence Process
Embedded Files
Welcome to JDL
Welcome to JDL
The JDL@KAIST conducts interdisciplinary research integrating artificial intelligence, novel materials, process control, optimal design, and dynamics. By combining advanced manufacturing technologies, computational modeling, and experimental validation, we aim to develop innovative engineering solutions that enhance the performance, intelligence, and reliability of next-generation mechanical systems.
Research Areas
Research Areas
Novel Materials
Novel Materials
Advanced Functional Materials for Next-Generation Manufacturing
We develop advanced functional materials to enhance mechanical performance and enable new functionalities. Our research focuses on nanocomposites, smart polymers, and ceramics for advanced manufacturing applications.
1) Nanocomposite Materials
- Development of CNC- and CNT-reinforced polymers for enhanced mechanical properties.
- Optimization of nanofiller dispersion and material composition for additive manufacturing.
2) Smart and Functional Polymers
- Development of shape memory polymers for 4D printing applications.
- Investigation of thermomechanical behavior and shape recovery characteristics.
3) Advanced Ceramic Materials
- Development of ceramic materials for vat photopolymerization 3D printing.
- Optimization of material formulations to improve printability and mechanical performance.
Optimal Design
Optimal Design
Performance-Driven Design of Engineering Systems
We develop advanced design methodologies to improve the mechanical performance and functionality of engineering systems through numerical analysis and optimization. 3D printing enables complex, customized designs that are difficult to manufacture using conventional methods.
1) Patient-Specific Implant Optimization
- Multi-objective optimization of screw configurations using NSGA-II to minimize stress concentration while reducing stress shielding.
- Simultaneous optimization of five screw design parameters: position, orientation, diameter, type, and number.
- Flexible optimization of screw positions and orientations within patient-specific anatomical constraints, without relying on predefined screw holes.
2) Design and Mechanical Characterization of Lattice structure
- Metal powder bed fusion fabrication of Ti6Al4V lattice (metamaterial) structures with controlled geometric parameters and manufacturing process parameters.
- Integration of experimental characterization and finite element analysis to investigate mechanical properties and failure mechanisms.
- Establishment of practical design considerations for lightweight, load-bearing structures based on their mechanical requirements.
Process Control
Process Control
Intelligent Monitoring and Control for Advanced Manufacturing
We develop intelligent monitoring and control strategies to improve manufacturing precision, quality, and reliability. Our research integrates real-time process monitoring, feedback control, and process optimization for additive manufacturing.
1) Real-Time Extrusion Control
- Camera-based monitoring and measurement of filament diameter during 3D printing.
- Real-time G-code correction for adaptive extrusion control and defect reduction.
2) Process Parameter Optimization
- Investigation of process–property relationships to identify optimal printing conditions.
- Optimization of manufacturing parameters to enhance dimensional accuracy and mechanical performance.
3) Intelligent Process Monitoring
- Development of vision-based monitoring systems for additive manufacturing.
- Data-driven defect detection and quality prediction for improved process reliability.
Artificial Intelligence
Artificial Intelligence
Data-Driven Intelligence for Advanced Engineering
We develop AI-driven approaches to improve prediction accuracy, quality assessment, and decision-making in engineering systems. Our research integrates machine learning, deep learning, and computer vision for intelligent manufacturing and performance prediction.
1) AI-Based Quality Monitoring
- Development of deep learning models for automated defect detection and quality assessment.
- Application of computer vision and Vision Transformers to additive manufacturing processes.
2) Data-Driven Performance Prediction
- Development of machine learning models to predict material properties and system performance.
- Integration of experimental data and predictive modeling to identify complex relationships between input parameters and performance.
3) Intelligent Modeling and Optimization
- Development of data-driven models for complex engineering systems.
- Application of AI-based prediction and optimization to improve reliability and engineering performance.
Dynamics
Dynamics
Nonlinear Dynamics for Vibration and Motion Control
We investigate the dynamic behavior of mechanical systems to improve stability, vibration isolation, and motion control. Our research integrates nonlinear dynamics, mechanical modeling, and experimental validation to develop innovative mechanical systems.
1) Nonlinear Vibration Isolation
- Development of bistable and nonlinear stiffness mechanisms for vibration and shock isolation.
- Investigation of dynamic responses to enhance stability and impact protection.
2) Swashplateless Rotor Systems
- Development of swashplateless rotor mechanisms for simplified thrust vector control.
- Investigation of rotor dynamics and control strategies to improve maneuverability and system performance.
3) Dynamic Modeling and Analysis
- Development of analytical and numerical models to predict complex mechanical behavior.
- Experimental characterization and validation of dynamic responses under various operating conditions.
Our Global Partners
Our Global Partners
Our Research Centers Partners
Our Research Centers Partners
Page updated
Google Sites
Report abuse