Beyond CAD: How Simulation, AI, and Digital Twins Are Reshaping Engineering Design
Conventional engineering design relies on human expertise, computer-aided design (CAD), and repeated trial-and-error through physical prototyping until an optimized engineering design is achieved. As technology has advanced, simulation-based design has gradually replaced the need for repeated testing using physical prototypes. Virtual testing based on finite element analysis (FEA) for multiphysics simulations has reduced the cost required for physical testing. Nevertheless, simulation still requires substantial computational resources to improve the realism and efficiency of the simulations.
Until recent years, programmers have used code to drive various algorithms, enabling machines to learn from massive amounts of data and recognize complex patterns, thereby promoting the development of modern AI. The most common applications of AI techniques such as machine learning, neural networks and evolutionary algorithms are prediction, optimization and decision making. Generative design utilizes these intelligent algorithms for automatic search of design solutions that are difficult to find with traditional design methods. These computational algorithms reduce the need for such large amounts of repetitive simulation calculations. Furthermore, through automation, they reduce repetitive engineering design tasks, improving work efficiency while lowering the time cost required for the design process.
However, there is still a gap between AI-generated data and real-time data. These gaps cause AI-generated data or objects to be potentially usable or unusable. It must undergo real-world validation, only after virtual data are rationalized can they be implemented in the real world. Digital twin technology is a technology that serves as a bridge between the real world and the virtual world by rationalizing their connection. Through converting real-time data into virtual data, it then uses the virtual world to design or maintain engineering projects required by the real world. Compared to pure artificial intelligence (AI), digital twins can not only monitor real systems but also predict future mechanical behaviors and optimize the performance of materials during their lifecycle. These capabilities make digital twins of great value for maintenance and manufacturing of engineering projects.
AI has not yet completely replaced CAD and FEA, they are still powerful tools in the engineering fields. They have different purposes , but are complementary . Simulation is driven by the human knowledge of the physics . AI may improve the ability to predict and optimize . Digital twins link the virtual model to the physical performance. The technologies do not overlap in terms of their function but integrating them can improve the overall engineering workflow.
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