I once wrote an optimization program which found the best fillet size, thickness and angle of a two dimensional cantilever beam with a given load. It took three days to write. What I’m about to show you took me 30 minutes. It makes use of real geometry and doesn’t require programing! Enter simulation automation…
Let’s consider a scenario. You work for an amusement park ride manufacturer. Your company has begun to streamline their design process. (Good idea if you ask me.) The company would like your team to design components which will be interchangeable (like Erector Set parts). The designer you work with calls himself an artist. He wears leather pants. His ideas are nutty. It’s up to you to make real decisions. He comes to you with the part seen below and says, with a blank stare, “To date, this is my best work. Try not to taint it.”
The company will be making thousands of these parts which will be made from cast carbon steel. It is designed to hold pipes and should carry at least 3000 lbs.
Decision: you decide that the part should have a safety factor of two. (The stress should not be higher than 120 MPa.) This means that the part should be able to hold at least 6000 lbs. without failing.
You setup an initial study to benchmark the current design.
The study shows a FOS of 1.2. Not strong enough. However, you see a few areas where you can improve the design; notably, the diameter of the upper support rod, the thickness of the ring, and the size of the fillet. Instead of making incremental changes to the design and rerunning the study, you decide, you can run an optimization study to do this for you.
This brings up the design study interface at the bottom of the screen.
Variables – Parameters within the study which will be changed. This can include model dimensions (as in our case), simulation input (loads, mesh controls, material properties, etc.), and anything else you can link to a global variable.
Constraints – Rules that define the bounds of the simulation. In our case, the factor of safety cannot be below 2. The optimization study will ignore any studies that do not meet our constraints. Constraints are created from sensor data. Anything that can be linked to a sensor can be constrained in an optimization study.
Goals – Parameters which we are trying to achieve. In our case, we are optimizing our part to have the lowest possible mass. Goals are created from sensor data. Anything that can be linked to a sensor can be a goal in an optimization study.
Once you add a variable, a window pops up allowing you to select a dimension from the model. By clicking on a face which carries the dimension of interest, the dimension becomes visible on the screen. You then select the dimension from the graphics window. To keep things clean, you give your variables names.
You can add a range (minimum and maximum values) to your variables with a step size. Other options include having just a range (SolidWorks will decide the step size for you) or entering discrete values.
You now add constraints and goals to keep the factor of safety under two (constraint) while minimizing mass (goal).
*If you would like to understand how to add sensors, please see this week’s the tech tip.
After clicking “Run” the simulation goes through all the scenarios you asked it to. Out of the 50 design scenarios, there are only 5 that have a maximum stress below 120 MPa. The design study highlighted in green is the optimal study.
You now feel proud. Not only did you make a stronger part, you also saved your company money by optimizing the part for weight. The designer in the leather pants is calling YOU an artist. He keeps a prototype of your part in a glass container. Best of all, it took less than an hour of work. Life is good.
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