{"id":1777,"date":"2017-01-13T10:00:00","date_gmt":"2017-01-13T16:00:00","guid":{"rendered":"https:\/\/www.cadcrowd.com\/blog\/?p=1777"},"modified":"2021-07-23T00:18:43","modified_gmt":"2021-07-23T06:18:43","slug":"generative-design-what-it-is-and-why-its-cool","status":"publish","type":"post","link":"https:\/\/www.cadcrowd.com\/blog\/generative-design-what-it-is-and-why-its-cool\/","title":{"rendered":"Generative Design: What It Is and Why It’s Cool"},"content":{"rendered":"

Generative design is an intriguing new creative paradigm emerging in the field of design which mirrors similar developments in art and music. The modernists of the early 20th century<\/a> emphasized the importance of the processes used to create a painting, a poem, or a composition. Generative design pushes this procedural method to the conclusion that could only have been imagined by many of the modernists. In generative design, designers set up a process \u2014 they write the rules for a system \u2014 but the end result is produced by the process itself. Like a chain of<\/p>\n

Like a chain of dominoes where each domino determines the position of the next, the designer has only indirect control over the end result of the creative process. One might even go as far as saying that, in generative design, the creative work is split between the designer and tools they are working with. In most cases, those tools are a combination of hardware and software.<\/p>\n

What Is Generative Design?<\/h3>\n

Let us start with the basics, something most of us already have a bit of understanding: a computer. Computers are, among other things, machines with the ability to precisely calculate complex mathematical problems within split seconds. Computers are actually a lot better at performing these kinds of equations that people are. Which is why, even with the technology available in 1997, IBM could design a computer that could defeat chess grandmaster Garry Kasparov.<\/p>\n

For the computer to do that, a programmer (or a large team of programmers) write the right program to tell the computer exactly what it needs to do in order to accomplish the end goal. Let us put it this way: in chess, the end goal to checkmate the opponent, but there are millions or more possibilities with each consecutive move. The program tells the computer the most effective way to accomplish the goal relative to every move that the opponent makes during a game; this is where computer algorithm comes in. In other words, the computer can only accomplish these incredible feats when it’s been given a set of rules to follow by a designer.<\/p>\n

An algorithm allows a\u00a0programmer to tell a computer not only what to do, but also how to do it. When this is applied to the world of engineering and design, what you get is generative design. When algorithms can be applied in manufacturing industries, infrastructure, and basically everything that requires impeccable combination between form and function; we call it generative design. The following diagram helps to explain the principle:<\/p>\n

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Hartmut Bohnacker – Own work, CC BY 3.0 Source<\/a><\/figcaption><\/figure>\n

Based on the diagram, we can easily tell that idea is the only part of the process. From this idea, a set of rules are created, and it is these rules that the computer uses to generate a solution to the design problem being posed. The important and pivotal aspect of this process is that, once let loose, the computer can and probably will come up with solutions that are entirely unanticipated by the designers. And this is what makes it a generative process.<\/p>\n

When it comes to design, an idea is always a solution to some kind of problem. When someone has an idea, it does not necessarily mean that the person has found a way to solve the problem at hand. Sometimes, an idea can simply be an insight into the nature of the problem itself \u2014 the solution often remains opaque or prohibitively complex. But some things which are incredibly complex to us are simply cheesecake to a computer.<\/p>\n

The designer starts with an objective (or a set of objectives) and runs them through an algorithm. While they are the architects of the system, it is the system which produces the final result.<\/p>\n

For example, a shoe designer wants to create a running shoe capable of providing comfortable cushioning and maximum weight support of 300 pounds; or an architect who wants to build a structure with massive solar energy conversion without sacrificing aesthetics. In conventional design, the designer or architect would sketch their ideas, create blueprints, make a 3D model<\/a> or a prototype<\/a>, and work out how the final product should look and be made. Changes would be made by iterating these steps, until a design is arrived at which the designer believes satisfies all of their project goals.<\/p>\n

All these processes are called \u201cexplicit design\u201d. If they put their ideas or problems into computer algorithms and let the computer create design iterations for them, what they do is called generative design. A computer can perform infinitely many iterations much, much faster than any human being could.<\/p>\n

A good example of how the technology works was set by the works of Lightning Motorcycle below.<\/p>\n\n\n\n<\/colgroup>\n\n\n
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1. Swing Arm Prototype – Source<\/a><\/p>\n<\/td>\n

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2. Developed Prototype – Source<\/a><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Autodesk, with its Dreamcatcher software, made the prototype of a swing arm for Lightning, a motorcycle company based in San Carlos, California. The objective was to create the lightest swing-arm possible for a motorcycle without sacrificing structural integrity. The swing-arm must support the weight of the engine and rider. Of course, with any vehicle design, reducing weight leads to higher efficiency.<\/p>\n

Thanks to generative design, engineers were able to set starting parameters such as strength and weight, and allowed algorithms to potential solutions through mathematical analysis. Dreamcatcher worked on the given parameters and came up with numerous potential design solutions, which were then evaluated by the design team so that the optimal solution could be identified.<\/p>\n

Notice how the swing arm in that first design is… not what you’d expect? That’s because the algorithms were provided with weight and strength requirements as their primary constraints \u2014 the generative system was not concerned with how the thing should look, and so it produced an unexpected result. In fact, these kinds of generative algorithms often produce these kinds of organic-looking results.<\/p>\n

Image number 2 shows a refined version of the prototype. Engineers probably tinkered with the algorithm during the process to make the end product more realistic for the envisioned application. While the end result is a lot more conventional than the algorithms earlier iteration, it still demonstrates a kind of mathematical structure that is a hallmark of generative design processes.<\/p>\n

Generative Design does not refer to one designing platform only, such as Dreamcatcher. According to Autodesk, the four most common methods are:<\/p>\n