449 citations · 945 across the 13 of their papers we have counts for
13 papers
Performance Comparison of Design Optimization and Deep Learning-based Inverse Design
Minyoung Jwa, Jihoon Kim, Seungyeon Shin +3
Surrogate model-based optimization has been increasingly used in the field of engineering design. It involves creating a surrogate model with objective functions or constraints bas…
Adaptive Neural Network Ensemble Using Frequency Distribution
Ungki Lee, Namwoo Kang
Neural network (NN) ensembles can reduce large prediction variance of NN and improve prediction accuracy. For highly nonlinear problems with insufficient data set, the prediction a…
Topology Optimization via Machine Learning and Deep Learning: A Review
Seungyeon Shin, Dongju Shin, Namwoo Kang
Topology optimization (TO) is a method of deriving an optimal design that satisfies a given load and boundary conditions within a design domain. This method enables effective desig…
Wheel Impact Test by Deep Learning: Prediction of Location and Magnitude of Maximum Stress
Seungyeon Shin, Ah-hyeon Jin, Soyoung Yoo +4
For ensuring vehicle safety, the impact performance of wheels during wheel development must be ensured through a wheel impact test. However, manufacturing and testing a real wheel…
Deep Learning-Based Inverse Design for Engineering Systems: Multidisciplinary Design Optimization of Automotive Brakes
Seongsin Kim, Minyoung Jwa, Soonwook Lee +2
The braking performance of the brake system is a target performance that must be considered for vehicle development. Apparent piston travel (APT) and drag torque are the most repre…
Idle Vehicle Relocation Strategy through Deep Learning for Shared Autonomous Electric Vehicle System Optimization
Seongsin Kim, Ungki Lee, Ikjin Lee +1
In optimization of a shared autonomous electric vehicle (SAEV) system, idle vehicle relocation strategies are important to reduce operation costs and customers' wait time. However,…
Explainable Artificial Intelligence for Manufacturing Cost Estimation and Machining Feature Visualization
Soyoung Yoo, Namwoo Kang
Studies on manufacturing cost prediction based on deep learning have begun in recent years, but the cost prediction rationale cannot be explained because the models are still used…
Generative Design by Reinforcement Learning: Enhancing the Diversity of Topology Optimization Designs
Seowoo Jang, Soyoung Yoo, Namwoo Kang
Generative design refers to computational design methods that can automatically conduct design exploration under constraints defined by designers. Among many approaches, topology o…
The Effect of Robo-taxi User Experience on User Acceptance: Field Test Data Analysis
Sunghee Lee, Soyoung Yoo, Seongsin Kim +2
With the advancement of self-driving technology, the commercialization of Robo-taxi services is just a matter of time. However, there is some skepticism regarding whether such taxi…
Integrating Deep Learning into CAD/CAE System: Generative Design and Evaluation of 3D Conceptual Wheel
Soyoung Yoo, Sunghee Lee, Seongsin Kim +3
Engineering design research integrating artificial intelligence (AI) into computer-aided design (CAD) and computer-aided engineering (CAE) is actively being conducted. This study p…
A Study on Anxiety about Using Robo-taxis: HMI Design for Anxiety Factor Analysis and Anxiety Relief Based on Field Tests
Soyoung Yoo, Sunghee Lee, Seongsin Kim +3
Despite the approaching commercialization of robo-taxis, various anxiety factors concerning the safety of autonomous vehicles are expected to form a large barrier against consumers…
Form + Function: Optimizing Aesthetic Product Design via Adaptive, Geometrized Preference Elicitation
Namwoo Kang, Yi Ren, Fred Feinberg +1
Visual design is critical to product success, and the subject of intensive marketing research effort. Yet visual elements, due to their holistic and interactive nature, do not lend…
Deep Generative Design: Integration of Topology Optimization and Generative Models
Sangeun Oh, Yongsu Jung, Seongsin Kim +2
Deep learning has recently been applied to various research areas of design optimization. This study presents the need and effectiveness of adopting deep learning for generative de…