How to Win at Engineering with Generative Design
Engineering is a journey that ends with “good enough.”
Good enough is subjective, amorphic—and perfectly appropriate place to call it quits. A good enough design, product or outcome satisfies the stated objectives and is a feasible solution.
Good enough is not, however, the best solution. Good enough often leaves the best solution undiscovered.
We have reasons to avoid pursuing the best solution. Cost is the main reason, the ever-present constraint on the number of solutions attempted. Other constraints include lead time, performance targets, manufacturability or regulatory rules.
The best solution is a costly goal because the goal line, like perfection, can never be reached. Every solution comes at a cost and attempting another solution will draw from available resources. In this way, each solution competes against the previous one. When do you stop iterating and move to the next step in the process, knowing you have the winning solution?
Winning implies competition, as does best.
One might not think of engineering as competitive. But businesses that employ engineers are always in competition. Grants and contracts are a competition and so is market share. The globalization of industry and the steady democratization of skills has created fierce competition, with more participants, in every industry. While not every internal project is directly competitive, most projects will support a mission that is ultimately competitive.
Engineers have grown accustomed to iterating, but normally do 1 to 10 iterations per problem. They understand the value of iterating, learning with each iteration, or “refining the design.” It normally leads to good enough.
Of course, every engineer will have to balance the number of iterations against the cost to produce those iterations on a per-project basis. There is significant pressure to find ways to increase the iteration/dollar ratio. Tools that increase this ratio are providing competitive advantages that are hard to ignore.
Over the last few years, three tools have caused a shakeup in how engineers think about the pursuit of the best solution.
- Topology optimization
- Generative design
- Additive manufacturing
Each of these tools presents considerable advantages by themselves, but when strung together they have the potential for creating (and confirming) a winning design while reducing overall cost.
Topology optimization is an objective-based design method that utilizes finite element analysis (FEA) to determine feasible shapes for a given set of loading conditions.
Topology optimization software frees the engineer from having to guess the overall shape or form of the part. A poor guess can lead to a dead end. This is why so many designs resemble one another. Blazing a new trail can be risky. Engineers are playing it safe.
In a conventional design workflow, the engineer will draw their part in CAD and validate their design using FEA or other numerical methods. If the FEA results show that there is room for improvement—perhaps the factor of safety is higher than required or the lifespan is longer than necessary—the engineer may iterate the design. They may choose to sacrifice performance for gain in another category such as manufacturability or cost of materials. Repeated multiple times, this process eventually results in a good enough part.
Typical FEA visualization. Colors are used to communicate stresses, displacement or other metrics. The engineer can use these visualizations as clues to an optimized shape.
But if the goal is the best shape, the engineer might as well be searching for a black cat in an unlit gymnasium. Other than visual clues, there is little help provided to find the best solution.
Topology optimization flips the process. It begins with the loading conditions. An engineer will set the objective and constraints of the problem and then turn on the optimization. The model is broken down into finite elements and solved. Finite elements with no stress means the finite element is not necessary. If a stress is too high, more finite elements are needed. This way, an optimal shape is systematically derived.
Topology optimized control arm. Three loading conditions are evaluated and confirmed to have room for optimization. Algorithms reveal resultant load paths, which are interpreted as the minimum volume of material necessary to meet the engineering criteria.
The resulting shapes can take life-like shapes and be reminiscent of natural designs all around us. From tree limbs to dragonfly wings, nature has proven to be an exceptional objective-driven designer. But however optimized these designs are, they need to be manufacturable. That has been a barrier to wide adoption of topology optimization.
Topology optimized wheel. The load paths that emerged from the loading conditions resemble patterns found in nature.
Among the many benefits of additive manufacturing (also known as 3D printing) is that it enables topology optimized shapes to be manufactured cost-effectively. “Complexity is free,” frequently quoted by the additive manufacturing (AM) industry, may not be true, but complexity is definitely encouraged.
In traditional manufacturing, increased complexity definitely correlates to increased cost. The more complex part will cost more to produce. Project managers are especially averse to individually complex parts because they understand their downstream risk. When a shape is too complex, we’d rather break it down into multiple, simpler parts to be assembled.
However, within the last decade additive manufacturing has emerged as an acceptable method of manufacturing complex parts and has fueled a paradigm shift toward complexity. In a time where complexity is encouraged, topology optimization can deliver.
So, is it starting to sound as if topology optimization and additive manufacturing combine for an easy win? We offer a few caveats:
- Topology optimization works in the concept phase of the design process. The output of this stage is a low-precision model that satisfies the basic requirements of the part.
- This shape must be processed in a CAD tool such as SOLIDWORKS for detailed design.
- The detailed design is validated with more robust FEA and CFD tools than typically found in topology optimization software.
- The validated design is cleared for manufacturing and the drawings or CAD files are transferred to those responsible for making the part. This is where additive manufacturing becomes an option.
- After manufacturing, the part is inspected for quality assurance.
- If the part is destined for assembly, it makes its way there. Otherwise, it is sent for packaging and shipping.
A winning engineering design requires optimization within every stage of this workflow. This quickly becomes difficult because of the sheer volume of possible combinations. At each stage, decisions are made that affect later stages but also previous stages. This is not linear like an assembly line. It’s more like a spider web.
But what may be challenging for an engineer is perfect for iteration through automation.
There are challenges to automation within every stage, but until topology optimization, there was no automation possible at the beginning—the concept stage of a design. Topology optimization tools, by minimizing the effort required of the human operator, enable an unprecedented level of automation at this stage.
Most topology optimization software isn’t currently capable of complete automation, but it is a good start. Rather than a discrete objective, a topology study could be given an array of directives. So too, the constraints (such as manufacturing method and material) could be varied and a user-selected number of iterations could be evaluated. The extreme computational intensity can be mitigated by pushing the number crunching to the cloud, another recent and necessary advancement in technology.
A single component, multi-variable generative design study will result in several viable options to select from. (Image courtesy: Buonamici, Francesco & Carfagni, Monica & Furferi, Rocco & Volpe, Yary & Governi, Lapo. (2020). Generative Design: An Explorative Study. Computer-Aided Design and Applications. 18. 144-155. 10.14733/cadaps.2021.144-155.)
A generative design tool will utilize topology optimization in addition to other layers of optimization, resulting in an array of possible solutions. The array is presented to the engineer for evaluation. The engineer might make a selection based on information not included in the generative design study, such as current supply chain disruptions. Manufacturing methods, material selection, cost of inspection, etc. can and should be investigated at the conceptual stage with this method.
This is the promise of generative design: a holistic automation that ends with the presentation of potential solutions, and from them, we can select the best of the best. It’s our choice.
Learn more with the whitepaper Designers Greatly Benefit from Simulation-Driven Product Development.