During the design phase, an engineer may evaluate different design options using FEA and iterate using trial and error to refine the design for one or two key parameters. The problem with this manual approach is that other design parameters, outside of those the user is setting, can inadvertently be impacted. For example, a part modified to minimize weight by removing portions of the material may result in unacceptable changes in the part’s natural frequency. Optimization tools enable modifying multiple variables for iterative design changes while maintaining certain limits on other design parameters.
Many current FEA software tools include the ability to perform optimization. Essentially, these routines automate the common trial-and-error approach of changing design variables and determining the impact of these changes. The advantage of incorporating these tools into an automated algorithm is that the software can “keep an eye on” other variables that may be impacted by changing the variable of interest.
Parametric optimization involves the following concepts:
- Variables: These are the values that the user wants to modify from the part design.
- Constraints: These are the limits that are placed on various parameters.
- Goals: This is generally the parameter optimized. For example, minimizing weight on a part would be a common goal.
In this video we are going to see how the thickness of a surface can be included in the design optimization process.