The Future of Auto Manufacturing: AI Driven Design
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The Czinger 21C hypercar concept incorporates a revolutionary brake node, a combination of braking system and suspension upright, using Divergent 3D's DAPS system. DAPS utilizes Metal Additive Manufacturing and generative design powered by AI to create highly optimized structures. Generative design explores numerous solutions based on defined parameters, producing innovative designs. It can optimize parts while considering various constraints and objectives.
Generative design methods include Cellular Automata, Genetic Algorithms, Shape Grammar, L-Systems, and Agent-Based Models. Cellular Automata use mathematical models with discrete cells and predefined rules to create emergent patterns. Genetic Algorithms simulate natural selection to evolve solutions in iterative generations. Shape Grammar employs a vocabulary of basic shapes and rules to create diverse designs. L-Systems model growth and complex structures using symbols and iterative rules. Agent-Based Models simulate interactions of autonomous agents, producing emergent patterns and system-level dynamics.
These generative design methods find application in various industries, including architecture, automotive, and aesthetics. They help optimize components, such as connecting rods, lattice patterns, taillights, and suspension systems, improving performance while reducing weight. However, the use of generative design is still developing, with advancements in AI and computational models continually expanding its capabilities.
In the future, AI-driven generative design could revolutionize engineering and design processes, surpassing human capabilities and rapidly producing highly efficient and complex designs. It has the potential to redefine the roles of engineers and designers, leading to more innovative and optimized products in various fields.
FOOTAGE
AutoDesk - @Autodesk
Softology - @Softology
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