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AI-based mobility/product design (Generative design)

What if artificial intelligence can design engineering systems on its own?

Smart Design Lab (SDL) wants to open a new paradigm in the manufacturing industry with AI-based engineering design. It combines physics-based dynamics with data-based deep learning technologies, and builds a virtual product development platform through digital transformation. This innovatively reduces the time and cost of product development and enables design to succeed in the marketplace.

AI-based generative design, which combines artificial intelligence and engineering design technologies (optimal design, CAD/CAM/CAE, HCI, etc.), allows artificial intelligence to achieve designs that satisfy engineering performance, aesthetics, and economy on its own. Develop and integrate the elemental technologies required for the four steps below.

AI-based Generative Design Process
The current design process involves designers, engineers, and marketers designing the final product through a long, repetitive process with a small amount of design before going to the market and being evaluated by customers. In the future, if AI creates a large number of engineered, economical, and customer-friendly designs, designers, engineers, and marketers will be able to come together to make quick and reasonable decisions based on quantitative predictions.
Innovation of design paradigm through AI-based design
SDL Research Introduction Video (Professor by) and Lab Life Introduction Video (Professor by Student).
Our lab deals with various manufacturing design applications such as mobility, machinery/electronics, and plants. Below are examples of studies conducted in four stages of artificial intelligence-based generative design.
① Design Generation AI
Creating a design by combining Topology optimization and Deep learning
Data-based 3D CAD generation automation process
② Design Evaluation AI
Prediction of Engineering Performance Based on 3D Deep Learning
Deep learning-based CAD/CAE integration automation
3D deep learning based manufacturing cost prediction
③ Design Optimization AI
Best Design for Multi-disciplinary Integration Based on Deep Learning

Deep Learning Based Design Parameters Inverse Design

④ Design Recommendation AI
Learning customer preferences based on active learning
Deep learning-based design recommendation system