A5104318008- Manufacturing Process and Optimization
- Composite Material Mechanics
- Topology Optimization in Engineering
- Additive Manufacturing and 3D Printing Technologies
- Injection Molding Process and Properties
- Computational Physics and Python Applications
- Mechanical Behavior of Composites
- Photopolymerization techniques and applications
- Process Optimization and Integration
- Meteorological Phenomena and Simulations
- Dental materials and restorations
- Complex Systems and Time Series Analysis
- Science and Climate Studies
- Fault Detection and Control Systems
- Advanced Battery Technologies Research
- Advanced Mathematical Modeling in Engineering
- Climate variability and models
- Advanced machining processes and optimization
- Mechanical Engineering and Vibrations Research
- Innovations in Concrete and Construction Materials
Korea Advanced Institute of Science and Technology
2024-2025
Kootenay Association for Science & Technology
2025
We introduce an advanced multi-task deep learning framework, designed to predict the evolution of stress fields and crack propagation across both time space.
Recent advances in deep learning have aimed to address the limitations of traditional numerical simulations, which, although precise, are computationally intensive and often impractical for real-time applications. Current models, however, may challenge obtaining high predictive accuracy long-term stability while obeying physical principles spatiotemporal prediction problems. We introduce DynamicGPT, a Vision Transformer-based generative model specifically designed prediction. This operates...
<title>Abstract</title> Recent advances in deep learning have aimed to address the limitations of traditional numerical simulations, which, although precise, are computationally intensive and often impractical for real-time applications. Current models, however, may challenge obtaining high predictive accuracy long-term stability while obeying physical principles spatiotemporal prediction problems. We introduce DynamicGPT, a Vision Transformer-based generative model specifically designed...
Abstract Injection molding is one of the dominant methods for mass‐producing short fiber reinforced plastics renowned their exceptional specific properties. In utilization such composite components, optimization process parameters significantly influences material characteristics and part performance. However, in industrial practice, this often relies on intuition iterative experimentation. Prior studies have introduced data‐efficient but faced limitations adopting minor variations product...
Abstract Injection molding is a prevalent method for producing plastic components, yet determining the ideal process parameters has predominantly relied on heuristic approaches. In this research, data‐driven injection optimization framework developed to simultaneously minimize warpage, cycle time, and clamping force. Employing multi‐objective Bayesian (MBO), applied fan blade model verification. Incorporating those objectives enables selection of an machine with proper maximum force within...
Liquid metal-elastomer composites (LMECs) have gathered significant attention for their potential applications in various functional stretchable devices, with inclusion sizes ranging from micrometers to nanometers. These exhibit exceptional properties, such as high electric permittivity and thermal conductivity, surpassing those of the elastomer matrix, thus enabling a broader range without compromising material's stretchability. To investigate diverse effective elastic properties LMECs,...