Design optimization is the use of simulation and analysis techniques to optimize product designs. Nearly all CAD and simulation vendors are hard at work developing automated solutions—or improving upon current solutions—to facilitate this endeavor and distinguish their solutions from those of their competitors.
The goal of design optimization efforts is often to reduce the weight of a product. In the case of vehicles or airplanes, reducing the weight means it requires less fuel to operate and less material to manufacture it. For consumer goods, the opposite may be true. Adding weight may increase durability and strength. In some cases, optimization is done with the idea that it will eventually end up in a landfill, making environmental health a primary concern.
Optimizing through digital means
Inferior designs inflate product development costs, due to wasted material, excessive shipping expense, and poor product performance. Ideally, design teams could imbed real-world design requirements with the digital model so that these criteria are always met within the product development process.
Design optimization functionality enables users to find the best possible design alternatives by running a series of what-if iterations. Through digital simulation, users can identify the “best” or optimal shape for a particular design. Users specify a variable range for their designs, run multiple simulation sessions, and then find out the best value for their target criteria.
Design optimization can also help users build manufacturing-suitable parts and assemblies based on the resulting optimal topology. Direct modeling tools with their push-pull approach to geometry creation make the entire process more intuitive and easier for non-CAD users.
Traditional build-test-review-improve design cycles require that engineers to perform manual iterations so they can determine what cause is behind the effect they are seeing. The next step to improving upon the process of design optimizations is to automate the optimization.
The idea behind automated optimization solutions is to remove the need for manual iterations, so users don’t have to change one parameter at a time. The software would automate the process to search the entire design space to find the right combination of parameters that will yield optimal design or performance.
As design teams increasingly lean on simulation techniques to improve upon developing designs, so does the number of required iterations they must perform to find the optimal solution. Some design projects require users set up multiple individual simulation sessions for each possible scenario, making it a very time-consuming process. By automating optimization, the user could run multiple simulations in the background and allow the software to suggest the best possible answer.
Extending the reach of behavior modeling
The Creo Behavioral Modeling Extension enables users to improve detailed design, reverse engineering, and verification and validation processes. It does so by embedding design requirements within models to solve optimization problems involving multiple design goals. The software also assesses model sensitivity to understand the effects of change on design objectives.
The results of the design optimization can be integrated with external applications. Regardless of the manufacturing method used, the software enables engineers to consider all design requirements. It also enables them to run experimental design studies crossing multiple functional areas.