Jet engines, lifelike movie monsters, cancer-fighting nanorobots and bespoke products. We live in a world where the objects all around us are designed by someone, and at the same time we are experiencing the biggest change in how we make things since the industrial revolution.
But this change isn’t just about new technology; designers and engineers are experiencing profound process changes and totally new and different ways of working. How do designers and engineers rise to meet the challenges that accompany these changes?
At Autodesk, we asked ourselves this question: What if there was a better way to organize all design information and allow people’s ideas and creations to emerge more organically?
Imagine having Google search-like functionality for the world of 3D models. Think of the simplicity and the speed at which you could work. This is what we have been working on at Autodesk and it is called Design Graph.
Design Graph is a powerful new machine learning system that uses algorithms to extract large amounts of rich 3D design data. It then categorizes every single component and design your design team has ever created, by classification and relationship, to create a living catalog that is able to react to a constantly-evolving world and guide your designs of the future.
Designers and engineers simply search across all of their files for a part type, such as a bolt or a bike seat, and Design Graph returns dozens or hundreds of options.
“We created Design Graph to enable designers to focus more on solving design problems rather than the mechanics of representing their design,” said Mike Haley, Sr. Director of Machine Intelligence at Autodesk. “Design Graph can save valuable time, eliminate redundant work and reduce costly errors.”
In recent years, programming breakthroughs and the limitless compute power of the cloud have given a computer the ability not just to think, but to learn independently, identify patterns and make predictions based on what it learns – rather than relying solely on the limited set of logic that can be programmed into it. Machine learning acts as an accelerant that advances what our machines and tools are capable of doing.
In the case of Design Graph, the goal was to teach computers to identify and understand designs based on their inherent characteristics—their shape and structure—rather than by any labeling (tags) or metadata. After all, whoever designed the part originally could label it any of dozens of ways, using full words or abbreviations. Metadata created by people, unless carefully managed, tends to be unreliable. With Design Graph, the computer uses its own observations about the 3D geometry contained in every 3D model.
How Does It Work?
Say that you’re designing a motorcycle, and you need a certain gear box. While working inside A360 Drive (soon to be available across A360), you can use Design Graph to search based on name, shape, category, properties or a combination of these to find the right pre-existing gear box design from your own firm’s design files.
Identical designs will appear as a single object, while slight variations will appear as separate designs, and you’ll be able to see how frequently each was used. Once you find what you’re looking for, you just drop it into your design—no manual searching through your catalog or worse yet starting from scratch.
“Machine learning and artificial intelligence are starting to make the first inroads into daily life, but to our knowledge this is its very first application for industrial design and mechanical engineering,” said Haley. “This is an initial step for Autodesk in harnessing this powerful technology to dramatically improve and accelerate the design process. But the promise is immense, and we expect there will be many more benefits of machine learning to exploit in the future.”
To learn more about Design Graph or try a beta version, visit https://a360.autodesk.com/.
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