A pesar de que los seres humanos aún somos mejores para tomar decisiones subjetivas en cuestiones de estética por ejemplo. El diseño computacional explora la alternativa a través de la programación para ofrecer una variedad de opciones que pueden ayudar en las etapa mas tediosas del proceso de diseño a las que denominamos comúnmente “talacha”. Esté artículo muestra el porqué en el diseño de información debemos de prestar mas atención al algoritmo.
Why Algorithms Are The Next Star Designers
RATHER THAN USING SOFTWARE TO DRAW AND ANALYZE A DESIGNER’S VISION, AUTODESK WANTS TO USE COMPUTING POWER TO GENERATE THE IDEA ITSELF. WILL THIS RENDER DESIGNERS OBSOLETE?
The industrial designer of the future might just be a computer. The traditional design process can be a laborious one, full of iterations and tweaks to make a product just so. For years, design software makerAutodesk has been working on an alternative: a program that sorts through all the ways to make a product of specific measurements and then spits out the best option.
“In the past couple of years, we have experienced such an explosion of computing power that we can completely change the design equation,” says Autodesk chief technology officer Jeff Kowalski. Rather than using software to draw and analyze a designer’s vision, Autodesk wants to use that computing power to generate the idea itself–by running through a nearly infinite number of ways to build the same product.
Though still in the experimental phase, Autodesk has been working on computational design for the last seven years through research projects such as Project Dreamcatcher, a system that generates CAD geometry based on a list of functional requirements. This approach could prove especially effective in sectors where being light and strong are of the utmost importance–as with cars and airplanes, where extra pounds have a direct impact on fuel efficiency.
Here’s how it works. You type in a set of constraints for the product. Then an algorithm “will go through a nearly infinite set of results and head towards ones that are going to satisfy the designer,” Kowalksi explains. Say you want a chair that needs to support 200 pounds of weight and be two feet off the ground while using the least amount of material possible. The program might start off with 1,000 ways to make the same chair, and then intermix what it sees as the most effective components of those 1,000 chairs to make 1,000 new options. It can go through a million iterations of the same chair “in the time that it would take the designer to describe just one.” It can also suggest what type of material to use.
A computer isn’t constrained by preconceived notions of what a chair should look like. The example above, chosen from several designs the algorithm generated, has a curvy, slightly portly, webbed aesthetic, quite unlike our traditional conception of how a chair should look. Other options for making the same chair might include straighter legs, or just a single pedestal leg, but the algorithm is capable of generating a variety of different designs that all meet the required criteria.
It’s not a process that’s limited to making chairs. “This applies to buildings, bridges, automotive parts–everything we do where we have to draw things,” Kowalski explains. It could be used to design a lightweight underlying structure of, say, a bicycle, which could then be covered by a skin to give it a different look–so you’re not limited to the lattice aesthetic, even if that’s the most efficient material for that product.
Computational design isn’t aimed at replacing human insight. People are still better at making more subjective selections over issues such as aesthetics. But the program certainly does serve up a panoply of options and can help sort through some of the more tedious aspects of the design process.
[Images: Courtesy of Autodesk]