CORTIME DESIGN OPTIMIZATION
Use optimization algorithms to find your best design
Advantage
Thorough exploration of your solution space.
Optimize your designs automatically
Design optimization is the aim of finding your best possible design parameters which also satisfy the design requirements. Practically, this means that parametric optimization algorithms intelligently test variations of your design automatically based on the variables and objectives you have determined. A common optimization would be a mass reduction of a design. Certain parameters of the CAD model are chosen as variables which the algorithms can alter to reduce the mass. At the same time a maximum level of stress is determined as an objective, that the algorithms must fulfill.
Reduces manual development time
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Focus your ingenuity
Constantly tweaking your CAD model, configuring every simulation study and repeating that process over and over, can be an exhausting and time consuming task for even the most skilled R&D Engineer – with no guarantee of an improved design.
By applying design optimization to your design process you will cut away manual trial and error optimizations. Furthermore, you can run computer-based optimizations 24/7 and thereby transforming nights and weekends to productive development time. All in all, using parametric optimization technology on your designs will focus R&D ingenuity on what matters the most: developing new design concepts.
Provides data-driven design insight
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Let optimization algorithms do the heavy lifting
Even with CAD and CAE tools at your it can be difficult to determine which direction to go when you want to improve your design. Sometimes you are confident to have modified your design for the better, only for the simulation study to tell you that you haven’t.
Using design optimization software will provide you with a confident direction that is impossible to achieve manually. Computer-aided optimization algorithms are simply better at providing design insight, because they are able to test a much larger number of design variations. So, let design optimization do the heavy lifting and provide you with the data you need to make qualified design decisions.
Optimizes product performance
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Stop relying on engineering epiphanies
Optimal product performance can often be sacrificed in the pursuit of hectic project deadlines. But this means that valuable improvements, which might be decisive in beating competitors are left unexplored.
If you use parametric optimization in your design process, you can stop wondering if there is an optimization potential. The optimization algorithms will exhaust your solution space making sure that, by choosing different starting points and intelligently determining where the optimal design is most likely found. In other words, you stop relying on moments of engineering brilliance and instead systematically explore your solution space.
Parametric Optimization
CORTIME is currently offering parametric optimization for design optimization. This means that you use parameters of your model to drive the optimization – dimensions and equations are used as variables while sensors from simulation studies and other properties are used as objectives. The algorithms then test different designs within the range you set for your variables and will gradually move in the direction that fulfills your objectives the best.
The advantage of using parametric optimization, is that you still have a level of control over your design, which means that the design variations CORTIME generates will not be problematic to produce once it needs to be manufactured.
If you want a quick overview of how to set up a design optimization, you can watch the video below.
CORTIME applies direct optimization algorithms when running parametric optimizations. This simply means that for every design variation a simulation study is run to test the iteration. This method differs from CORTIMEs Design of Experiments and Meta-Model since these are statistical interpolations of the original CAD model and simulation study. In other words, every design variation is simulated and tested instead being a statistical prediction.
The question is, which algorithm you should use on your design – global or local?
Global Algorithm
A thorough exploration of your solution space
You apply the global algorithm when you want to explore if there might be a better design variation out there that varies significantly from your current design. The global algorithm will initially test designs at random places in your solution space, but will gradually explore design variations in the vicinity of the best designs of the initial randomization. Furthermore, the algorithm will restart at different places in your solution space to triangulate the solution space. However, since a simulation study is run with every design variation, the lengthy run-time is a potential downside of the global algorithm.
Disadvantage
Optimizations can be lengthy.
Local Algorithm
Small modifications to your design
A local algorithm is useful when you have a design that you believe is fairly close to the optimal design. However, finding those final improvements can still be difficult and might make a big difference in terms of performance and cost – especially if you have to produce a lot of units based on that design. The local algorithm will make refined design variations which are close to your original design. While you will not get an extensive exploration of your solution space, the local algorithm is much faster than the global algorithm, which will be sufficient for some of your designs.
Advantage
Provides fast design optimizations.
Disadvantage
Explores a smaller part of your solution space.
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