AI Case Study
MIT Researchers design tool to optimise product designs according to optimal efficiencies and performance objectives
Researchers at Computer Science and Artificial Intelligence Laboratory (CSAIL) and Columbia University have developed a tool that suggests ways to optimise their design to achieve different optimal efficiencies. By analysing designs using computer-aided design (CAD) programs the software navigates tradeoffs between design parameters, such as height, length, and radius of a product. The enhanced designs can be modified to satisfy performance objectives such as weight, balance, and durability.
Construction And Engineering
"MIT researchers, in conjunction with Columbia University, have unveiled a new tool for designers who work with computer-aided drafting software. Building on previous work over the past year, their technique can optimize a design for any object, like a lamp or boat or wrench, for all sorts of metrics like mass, drag, and stress tolerance. And then it can create dozens of designs of that object, each tuned to different optimal efficiencies.
In other words, it removes iteration from the design process–and it could be applied to the design and engineering of consumer goods and industrial parts, replacing some of the human guesswork of product design and augmenting the intuition of designers themselves.
In other words, this machine logic can do in seconds what people perfected over centuries, arriving at the exact same conclusion–which is validating and humbling at the same time.
Right now, all these design alternatives are spit out through complex graphs which are hardly navigable to the average person, and the software also isn’t available in any sort of downloadable tool for you to run. The researchers recognize this–that while they’ve created an AI that might replace a team of designers, getting it to work well for those designers is another challenge altogether."
The tool "could help designers optimize their existing processes–and, crucially, deconstruct what works and what doesn’t, sooner."
R And D
"'A fundamental limitation of typical design optimization techniques is that they require a single objective function for evaluating performance. In most applications, however, multiple criteria are used to evaluate the quality of a design,' the paper explains. 'Structures must be stable and lightweight. Vehicles must be aerodynamic, durable, and inexpensive to produce. In most cases, the performance objectives are not only multiple but also conflicting: improving a design on one axis often decreases its quality on another axis. In reality, designers and engineers navigate a complex landscape of compromises, generating objects that perhaps do not optimize any single quality measure but rather are considered optimal under a given performance trade-off.'"