The analysis of composites and other heterogeneous materials 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 virtual automation tools to understand our materials and improve our end products?
Piece of Cake
To 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, such as the amount of water, quality of flour, and convection of your oven. You could bake 10 cakes and laboriously come up an empirical formula to predict how various inputs affect the resulting cake. Or you can understand a cake’s ingredients well enough to predict how a change 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, this opens 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.
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:
- Inputs – Provide a set of variable parameters and their upper and lower bounds
- Solving – Perform some operation using those inputs that generates a single result
- Match – Try to match that result to a set of pre-defined target values, or try to minimize/maximize any number of resulting values
- Iteration – Iterate (using a number of smart parameter selection techniques) until that solution converges
Just as it’s important to deconstruct cake ingredients, it’s also 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, then your outputs will be equally un-revealing.
The other important and rate-limiting component is the “iteration” step. This is the key ingredient in all optimization tools. The takeaway there is that the speed at which a tool takes to arrive at a solution must be 1,000x faster than the time to actually find an optimal solution manually. This is because it might take an optimization tool 1,000-5,000 iterations before it finds a suitable solution. Thus, another weakness of composite analysis tools in this space is their inability to quickly generate solutions to complex problems.
There are a number of factors that can be modified to potentially improve the properties of the material. At the same time, there are variables that are strictly controlled, such as the presence of voids in a matrix, as these variables result in the degradation of a part’s performance.
Since isolating variables is often a wiser approach, typical optimization studies found in the composites industry include:
- Fiber manufacturers
- This group may try to find the ideal length of fibers to meet target strength and weight while minimizing cost
- Alternatively, they may be interested in evaluating the ratio of glass-to-carbon fiber in a hybrid reinforcement bundle vs. other key mechanical properties
- Proprietors of woven composites
- May be curious about the ideal weave geometry to hit a certain strength target
- Designers of mining technologies
- 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
- May 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
For context, the ideal composite optimization jobs would be the following:
- Given all possible variables
- Part geometry
- And costs to modify each of the below:
- Cost to control defect
- Costs of different materials
- Costs of different manufacturing processes
- Costs to “model” various geometric features
- Find the lowest cost option to hit a given set of targets
- High-Powered Optimization Tool (HyperStudy, Optimus, Design Explorer)
- Manufacturing simulation tool (Moldex3d, FiberGraphix, FiberSim, etc.)
- Moldex3d in particular is adept in various forms of optimization
- Structural/thermal analysis tool capable of:
- Ingesting manufacturing inputs
- Using inputs from various sources to drive automated pre-processing at multiple scales
- Efficiently using manufacturing inputs to minimize computational costs
- Intelligently notifying optimization engine when a manufacturing input yields sub-par results
- Outputting simulation results in a useful and consolidated manner
The workflow for the optimization of a discontinuous fiber reinforced part, using available software tools, would be as follows:
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, it is up to the baker to understand all ingredients and know how to come together to make something the end user wants to eat.