Imagine that most of your day is spent working to solve problems and answer questions (maybe it is). If you had a magic machine that would output the correct answer to any question you could think of, you wouldn’t bother asking “How do I answer this?” Instead, you would wonder, “Which question do I ask?”
As artificial intelligence (AI) begins to answer more engineers’ questions, engineers will show their value more in the problems they pose rather than the answers they provide. In the case of generative design, this requires having a clear understanding of each design component’s purpose, because that understanding allows the engineer to request the best solution. In cases where there is no single, best solution, engineers must become adept at trading between multiple viable options. These trades often impact more than just the individual component, so engineers must operate with a systems-level understanding.
In Part 1 of this two-part blog, we explored how generative design allows engineers to produce increasingly excellent work, meeting—or surpassing—expectations in less time. As a result, engineers have more time to improve designs by not focusing only on parts, but by understanding the system as a whole and being able to redefine the requirements of specific parts to optimize the whole.
Take the case of Volvo’s SuperTruck II powertrain design. This next-generation powertrain, sponsored by the U.S. Department of Energy, aims to maximize hauling capacity and minimize fuel emissions. In this domain, any weight over the front axle comes at a premium cost. Components like the front engine mount (pictured below) are primary targets for generative redesign.
By simply swapping the original geometry with a generative definition, the Volvo engineer, Kevin, was able to reduce the mount’s weight without compromising any structural integrity (pictured below). This alone was an impressive feat, but Kevin wanted to push the design further. To make any more progress, he had to reconsider the original design problem.
Employing engineering judgment and intuition, he realized the bolt pattern used on the engine block drove unwanted stress into the engine mount. Kevin determined that a new configuration needed to be designed to properly relieve the stress. So, he reframed the problem, changed the bolt pattern entirely, and produced a generative result with a more structurally sound load path.
This final design can be seen below, and it is a testament to the power generative design brings when wielded by a skilled engineer. Here, AI hasn’t done Kevin’s job for him. Instead, it gave Kevin the time and confidence needed to respond more clearly and creatively to the problem.
At its best, AI (and, more specifically, generative design) will help improve your current processes and design outcomes—in less time overall. As a continuous improvement activity, AI can help you take existing designs and optimize them for your needs. It can serve as a valuable asset during design for both new and existing parts, innovating the work you already do.
Ideally, any form of AI-driven technology you adopt would be directly embedded in the tools you use today (like it is in Creo), rather than a separate application to learn that requires switching and moving files from one tool to another. AI is only one piece of your overall design puzzle. It won’t replace the way you do things now, but it can elevate what you’re already doing and help your processes evolve and stay on the cutting edge.
Katherine Brown-Siebenaler is the Marketing Content Manager for PTC's CAD team. Based in Austin, TX, Katherine is responsible for editing the Creo and Mathcad blogs. She has six years' experience as a content creator for various corporate marketing teams, primarily in SaaS environments. Katherine holds two degrees from the University of Florida, a BS in Journalism and an MA in Mass Communication. She enjoys learning how PTC customers bring software to life in real-world applications every day, leading innovation in their various industries.