|(Image source: PTC)|
In combination with the generative design technology, the efficiency gains of the GPU-accelerated simulation are increased even further.
Gone are the days when design simulation was limited to stress analysts. Today, product designers have the computing power to run simulations directly in their 3D CAD modeling environment. These simulations are accelerated by the GPU performance of modern computers and allow designers to make informed decisions at an interactive pace – without waiting for a stress analyst to network the design and perform a finite element analysis (FEA).
At this point, even relatively inexperienced designers can assess the performance of their design without necessarily understanding how an FEA analysis is performed. Given the industry Skills gapGPU-accelerated simulation is a gift for engineering teams who want to do more with less.
The mathematical models behind the GPU-accelerated simulation can show how a number of variables affect design, including nonlinear static stress, vibration tolerance, fluid dynamics, and heat transfer. Of course, these variables do not exist in isolation. The good news is that GPU-accelerated simulation is quickly approaching the point at which to assess how the synergistic interplay of these variables affects a design’s performance.
The impact on product design cycles is significant, allowing teams to iterate faster and avoid costly prototyping steps and later design changes. However, GPU-accelerated simulation alone is just the beginning.
For unknown persons, generative design is a design process in which artificial intelligence optimizes designs for parameters defined by the engineer and functional goals. The engineer uploads his design. defines the loads, material restrictions and boundary conditions; identifies their functional goals; and the algorithm optimizes the layout of the material within the design space to achieve these goals and maximize performance.
When most engineers think of generative design, they think of topology optimization. However, topology optimization is only the starting point for generative design. Generative design can now go far beyond topology optimization, which is largely thanks to GPU-accelerated simulation.
Using a generative design optimization approach, genetic algorithms optimize the design iteratively and simulate performance in light of the new configuration. This creates more efficient and powerful design configurations. The higher the GPU processing power, the faster the simulations can be executed, so that the generative design tool can optimize for more variables and develop more solutions in parallel.
Optimize for manufacturing and beyond
One of the main advantages of generative design over basic topology optimization is the ability to optimize for various manufacturing techniques. In the past, it was often not possible to create the complex organic shapes that were created through topology optimization. The optimized shapes could inspire designers, but these designers still had to use their expertise to transform the optimized design into something to manufacture. In contrast, designs with generative design can be explicitly optimized for extrusion, casting, additive or other methods – which saves time and energy.
The power of generative design doesn’t stop at optimization for manufacturing. This method also enables optimization of a variety of functional goals, including materials, strength, heat transfer, fluid dynamics and weight. From a purely commercial perspective, a generative design approach that is linked to live market information can also take cost constraints into account. In the end, generative design discovers solutions that most efficiently reconcile these competing goals.
Modular generative design
While GPU accelerated simulation allows inexperienced engineers to quickly test their designs, generative design continues by allowing them to quickly optimize their designs. The productivity of inexperienced engineers can be further increased by modular generative design, in which modules are created to solve generic problems. These modules can be combined with cloud-based approval and collaboration – think of a GitHub or an app store for generative design use cases.
Consider a simple bracket for illustration. There are countless applications in which a bracket is used to attach one part to another larger part. The function is generally the same, but in any case the bracket can have different force loads and different connection points, not to mention a variety of other variables depending on the application. A generative design module can be created for brackets, in which the engineer adjusts the variables to his problem and the generative design algorithm optimizes the design for the unique goals of the engineer.
By automatically searching for solutions to complex design challenges, generative design can dramatically increase productivity and compress design cycles, even more than GPU-accelerated simulations alone. Together, these methods enable you to drive operational efficiency, accelerate innovation, and bring your products to market faster.
Jesse Coors-Blankenship joined PTC as Senior Vice President of Technology, Advanced Development after the company acquired its startup for advanced generative design software, Frustum Inc. Before Jesse founded Frustum Inc., he was a professor at Columbia University, where he taught students how to use generative design technologies.
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