Computer-automated Design, or CAD, is the use of AI and machine learning algorithms to design new objects or enhance the designs of existing ones. It enables faster iterations and the ability to optimize designs based on set objectives and constraints.
Imagine when you play with Lego blocks, trying to build the best castle you can. Now Computer-Automated Design is like having an invisible robot friend who helps you build it. This friend could make the castle stronger or taller, or suggest you where to put the blocks, based on rules you tell it.
Computer-Automated Design (CAD) is the implementation of machine learning and intelligent algorithms to aid in design processes. In the realm of artificial intelligence (AI), CAD extends beyond the basic idea of CAD tools used for drafting objects in 3D. It leverages computational intelligence to optimize designs, evaluate design variants, and predict the performance of design configurations.
CAD, as interfaced with AI, involves deploying techniques such as Reinforcement Learning (RL), Generative Adversarial Networks (GANs), and Evolutionary Computation (such as Genetic Algorithms and Genetic Programming). These methods serve as “intelligent agents” that explore an extensive design space, assess various design parameters, undertake iterative design modifications, and drive an automated design evolution process.
To illustrate, consider designing a vehicle. Traditional CAD tools permit a designer to model the vehicle in 3D, but AI-integrated CAD would do more. Given goals related to fuel efficiency, load-bearing capacity, and aesthetics, the AI could generate design variants, assess their performance, and subsequently modify and optimize them to reach the best design fulfilling those set goals.
This AI-aided process can uncover innovative designs that may remain elusive to human designers, owing to the AI’s ability to efficiently navigate a vast multi-dimensional design space and identify optimal or near-optimal solutions. Moreover, CAD facilitates rapid iterations and can swiftly adapt to changing requirements or constraints, thus offering enormous productivity gains and potential cost savings.
Importantly, the performance of an AI-based CAD highly depends on the quality of the underlying computational models used to simulate and predict design performance. Additionally, these algorithms often behave as black-box models — while they can yield impressive results, understanding the precise logic leading to a particular design solution can be challenging.
Another critical dimension is defining suitable objective functions that accurately represent the design goals. Defining these objectives too narrowly might limit the diversity and innovation of the produced designs, while too broad definitions might leave the AI rudderless, leading to unpractical solutions.