MultiMechanics Blog

The Benefits of Optimization in Composites Engineering

Posted by MultiMechanics on Jul 21, 2015 2:11:00 PM

Analysis of composites is complex for a number of known and very well documented reasons. 

Many virtual testing techniques have been developed to help predict the behavior of composite parts; however most tools end up relying on a great deal of physical testing of a composite specimen before virtual testing of a part becomes a viable solution. 

Is there a way we can avoid excess physical testing and instead use optimization tools to understand our materials and improve our end-products?. 

Piece of Cake

Smith_island_cake2009To use an analogy, let's say you want to understand and predict the science behind baking a good cake. 
There are a number of variables that define a good cake, like amount of water, quality of flour, convection of your oven. You could bake 10 cakes and laboriously come up with an empirical formula to predict how various inputs affect the resultant cake. Or you can understand a cakes ingredients well enough to predict how a change in those ingredients will influence the outcome.  
If you can define these inputs, you can start to understand which ingredients or processes contribute to favorable or unfavorable cake characteristics. If you can do the latter, then this open the doors up to the true power of computers, the ability to iterate and optimize, such that for any given variation of your ingredients, you can reasonably predict how well that cake is going to turn out.  

Optimization

There are a number of optimization tools available on the market. They vary in their ease of use, pre and post processing capabilities, and methodologies, but boiled down, optimization tools operate under the following conditions:

  1.       INPUTS - Provide a set of variable parameters and their upper and lower bounds
  2.       SOLVING - Perform some operation using those inputs that generates a single result
  3.       MATCH - Try and match that result to a set of pre-defined target values, or try and minimize/maximize any number of result values
  4.       ITERATION - Iterate (using a number of smart parameter selection techniques) until that solution converges
thickness-optimization

Just as it's important to deconstruct cake ingredients, it's wise to look at the key pieces of the optimization process. For the parameter selection, this step dictates that you need an input paradigm flexible enough to take in and work with numerous variables. If your tool requires that your inputs are generic, vague, or boiled-down, than your outputs will be equally un-revealing. 

The other important, and rate-limiting component is the "iteration" step. This is the key ingredient to all optimization tools. The takeaway there is that the speed at which a tool takes to arrive at a solution must be 1000x+ faster than the time to actually find an optimal solution manually.  This is because it might take an optimization tool 1000-5000 iterations before it finds a suitable solution. Thus, another weakness of composites analysis tools in this space is their ability to quickly generate solutions to complex problems.  

Composites Optimization

There are a number of factors that can be modified to potentially improve the properties of a material.  At the same time, there are variables that are strictly controlled as their presense results in the degradation of a parts performance.  These are things like the presense of voids in a matrix. That said, if control is expensive, and it's costs can be modeled, it too can become a parameter 

A general list of these factors, while not comprehensive, includes:

optimization_variables_2

 

Since isolating variables is often a wiser approach typical optimization studies found in the composites industry are as follows.

  • Fiber manufacturers
    • They might try and find the ideal length of fibers to meet target strength and weight, while minimizing for cost.
    • Or they might be interested in evaluating the ratio of glass-to-carbon fiber in a hybrid reinforcement bundle versus other key mechanical properties
  • Proprietors of woven composites 
    • They might be curious about the ideal weave geometry to hit a certain strength target
  • Designers of mining technologies
    • They might be interested in the optimal placement of explosives to promote ideal crack propagation within a heterogeneous medium (like coal or shale rock)
  • Part manufacturers
    • They might be interested in optimal adhesion characteristics of fiber/resin, which can be modified by the introduction of surface treatments and coatings
  • 3D printers
    • Looking to print optimized material microstructures in the same part, all with specific properties targeted for that part region. 

Holy Grail 

For context, The grand-daddy of all composite optimization jobs would be the following:

Input:

  1. Given all possible variables
    1. Manufacturing
    2. Material
    3. Part geometry
  2. And costs to modify each of these
    1. Cost to control defect
    2. Costs of different materials
    3. Cost of different manufacturing processes
    4. Costs to "model" various geometric features

Output:

  1. Find the lowest cost option to hit a given set of targets.

Tools Required:

  1. High Powered Optimization Tool (Hyperstudy, Optimus, Design Explorer)
  2. Manufacturing simulation Tool (Moldex3d,FiberGraphix,FiberSim, etc.).
    1. Moldex3d in particular are adept in various forms of optimization. 
  3. Structural / Thermal Analysis tool capable of:
    1. ingesting manufacturing Inputs
    2. Using inputs from various sources to drive automated pre-processing at multiple scales
    3. Efficiently using manufacturing inputs to minimize computational costs
    4. Intelligently notifying optimization engine when a manufacturing input yields sub-par results
    5. Outputting simulation results in useful and consolidated manner

 

The workflow for the optimization of a discontinuous fiber reinforced part, using available software tools, would be as follows:

 optimization_flow2

In Conclusion:

In composites engineering, the list of variables is long and interrelated.  Whenever there exists a problem where there are more input variables than there are favorable outputs (and the stakes for solving are relatively high), you find that each group that controls one variable will claim that their variable is the most important and they have perfected the control of it. Often, you are encountering guessing and speculation (best case) and snake oil (worst case). It's like the sugar producer, or oven manufacturer claiming they have engineered their product to solve the 'most pressing' challenge in cake engineering without understanding how the other one works.  

In reality, its up to the baker to understand ALL ingredients and know how to come together to make something the end-user wants to eat. 

Bon Appetit!

Topics: Composites Engineering, optimization