Re:Algorithms that design structures better than engineers
by robomartin on Friday January 25, 2013 @03:19PM
(#5117397)
This is neat stuff. It's the future and, yes, in some fields it is now.
I've applied GA/GP techniques in a number of areas. There are still a number of issues when applying this method to mechanical design.
A naive implementation can produce structures that are either impossible to fabricate or insanely complex and expensive.
It is easy to also optimize away safety factors. The pictured truss might be optimal under ideal conditions yet fail in spectacular ways if even a small crack or a bad weld develops somewhere. Remember, rust never sleeps. There's also the case of material quality and consistency (is all aluminum equal?).
One application that was a lot of fun to work on was the evolution of an optimal cooling solution for a very high power (1500W) LED array. I evolved the solution using what I am going to dare and call a manual-GA approach.
All of the mechanical modeling was done using SolidWorks and FlowWorks/SolidFlow FEA package. The mechanical model was designed to be parametrically controlled from extensive geometry tables that could be edited with MS Excel (which made it so much simpler). The approach was to run reasonably realistic (meaning, close to what you could actually fabricate) structures. Each structure took 16 to 18 hours to run. Using multiple cores and machines we could get a set of them done overnight for evaluation the following morning.
The evaluation function consisted of agreed-upon parameters that came out of the FEA simulation: delta T, max and min temperatures, thermal uniformity, etc. These were used to calculate a score for each structure.
Once enough data (enough variants or "chromosomes" if you will) accumulated, the manual "natural selection", "reproduction" and "random mutation" steps would take place.
Over a period of about eight months this work produces some really interesting, unexpected and, I think, innovative approaches to the problem.
I learned a lot by taking this approach. I also learned just how hard it would be to fully automate such a project. I am not necessarily speaking about the tools involved as much as the need to encode manufacturing knowledge into a system such that it autom-magically evolve objects one can actually construct.
I've applied GA/GP techniques in a number of areas. There are still a number of issues when applying this method to mechanical design.
A naive implementation can produce structures that are either impossible to fabricate or insanely complex and expensive.
It is easy to also optimize away safety factors. The pictured truss might be optimal under ideal conditions yet fail in spectacular ways if even a small crack or a bad weld develops somewhere. Remember, rust never sleeps. There's also the case of material quality and consistency (is all aluminum equal?).
One application that was a lot of fun to work on was the evolution of an optimal cooling solution for a very high power (1500W) LED array. I evolved the solution using what I am going to dare and call a manual-GA approach.
All of the mechanical modeling was done using SolidWorks and FlowWorks/SolidFlow FEA package. The mechanical model was designed to be parametrically controlled from extensive geometry tables that could be edited with MS Excel (which made it so much simpler). The approach was to run reasonably realistic (meaning, close to what you could actually fabricate) structures. Each structure took 16 to 18 hours to run. Using multiple cores and machines we could get a set of them done overnight for evaluation the following morning.
The evaluation function consisted of agreed-upon parameters that came out of the FEA simulation: delta T, max and min temperatures, thermal uniformity, etc. These were used to calculate a score for each structure.
Once enough data (enough variants or "chromosomes" if you will) accumulated, the manual "natural selection", "reproduction" and "random mutation" steps would take place.
Over a period of about eight months this work produces some really interesting, unexpected and, I think, innovative approaches to the problem.
I learned a lot by taking this approach. I also learned just how hard it would be to fully automate such a project. I am not necessarily speaking about the tools involved as much as the need to encode manufacturing knowledge into a system such that it autom-magically evolve objects one can actually construct.