Unlocking DfAM’s Potential
A new field-based design software is supporting more widespread use of Additive Manufacturing, for faster product development times with less rework and risk.
Most models and methods for design and innovation suggest that true innovation comes from not just a single event of inspiration, but rather the constant iteration and improvement upon an initial idea or design. James Dyson tested over 5,000 iterations of his product before releasing the company’s first bagless vacuum cleaner. The F-15 Eagle fighter jet models B, C, D, and E were all iterations of the original design. It’s clear that the ability to iterate leads to innovation—and an inability to iterate leaves the exchange of risk for knowledge until the very end, when there’s little time left to make changes and innovate.
Truth is, iteration in design engineering can be difficult and expensive. Design revisions result in hours of rework and a duplication of effort when models fail due to geometric errors. No wonder there is a natural aversion to iterate even though its impact on product success is well understood.
Design for Additive Manufacturing is no exception. DfAM introduces even more manual rework in file translations, build configuration, and slicing steps when a design is altered. Often DfAM is left as an after-thought of engineering design. At this point, low-level changes are made to the as-designed part to enable self-supporting features or to add sacrificial volumes that can be machined away for dimensionally critical features. There are other changes that can be made to fully leverage the capabilities of additive manufacturing (AM), but such changes to a model might require other dependent features to be reworked. Again, this is time-consuming and expensive.
A typical workflow when designing for AM starts with a conceptual design that gets represented in CAD software and is translated step-by-step to ultimately be configured for manufacturing. Changes to the part’s design or geometry in CAD require significant rework in order to get back to a print-ready configuration. Iterating back through one of these stages has an associated cost due to the rework required to adapt the models at each subsequent stage.
Recent software advances introduce a more agile development framework based on implicit modeling where changes to a design compile and rebuild automatically. A single data platform replaces multiple streams of engineering knowledge, consolidating a wide variety file types. This removes the traditional barriers to iteration and innovation and connects design and manufacturing in a way that enables DfAM in a seamless fashion.
Beyond simple distance fields
Let’s see what a DfAM workflow on this new type of platform might look like for an engineered part. The design opportunity here is to lightweight and additively manufacture a brake pedal.
The workflow starts with the initial bulk model geometry. In order to lightweight this part, various lattice configurations are explored with the platform’s lattice tools. Each lattice type is thickened and joined to the original model, according to part’s strength requirements.
A common problem with additively manufactured lattice structures is delamination between the lattice and skin. To mitigate this, a rule is created to blend the lattice into the skin smoothly (Step 3). Thanks to distance fields in implicit modeling, this can be done globally and robustly without manually selecting every edge in the model as would need to be done with conventional tools. Traditional methods would require hours of tedious edge selection that would need to be repeated if changes are made to the geometry.
At this point, our team decides that we need the lattice’s thickness to vary as a function of distance from the main mounting point. A variable thickness lattice structure is added to the internal area. The thickness is driven from the implicit field emanating from a planar surface (the red line in the image). This strategy is meant to provide extra stiffness near the mounting point as we would expect the highest stresses at the interface. Again, distance fields allow us to specify this rule, and when we do, the rounds we specified in the previous step still regenerate.
With implicit modeling, we’re not just limited to simple distance fields to drive geometry. Any field can be used to intelligently drive the lattice’s mesostructure. Examples of usable fields include stress, thermal, and fluid simulation data. Practically, you can drive geometry with any data you have. In our brake pedal example, we’re going to use Von Mises stress data from the engineers to influence the thickness of the lattice. Where higher stress values exist, the lattice elements are made thicker to provide strength where it is needed. This design workflow is enabled by systems built on implicit modeling, as fields are the foundational language in which component data is represented here.
Now that we have our brake pedal, the final step in creating additive-manufacturing-ready models is to create contour slices. A distance-field-based platform provides the freedom to generate and specify additive tool paths and scan patterns to deliver directly to machines without the use of intermediate STL files.
At this point in the design process, it’s traditionally very difficult to make design changes to the original model because such changes would set off a cascade of rework in modelling, build configuration, and slicing. With implicit modelling we don’t have the same constraints. We can make changes and trust the rules we made will still work. To demonstrate this, let’s consider a scenario where our engineering team brings simulation data to the table and is looking for a way to improve the design based on this new knowledge.
Normally, going all the way back to Step 2 of the design process to change the lattice type and/or geometry would be a major effort and possibly deemed infeasible due to time or resource constraints. Not only can this be done more efficiently with implicit technology but the other rules specified in the workflow will also automatically rebuild according to the new lattice geometry. All thickness rules and blends will still apply to the new model without additional work. This is the benefit of designing with an implicit data structure—rebuilds are fast, robust, and do not fail. And as before, the part will regenerate all the way to the contour slices that were specified in the initial workflow.
In this workflow, we showed constant progress towards an additively manufacturable part while still being able to iterate and make design changes that rebuild back to where we left off.
Streamlining the engineering design process in this new way enables robust rebuilding after changes to a design at any point. This ability is largely enabled by implicit modeling technology. Users have the ability to make upstream design changes that rebuild all the way down the design chain. They can also configure orientation and supports to export slices to send directly to a machine. Any upstream design changes will recompile all the way back to that point automatically. Not only does this save time for design engineers, but this also removes the barriers to design revisions and iterations.
James Dyson spent fifteen years completing the 5,127 iterations required to produce a great and innovative product. This kind of effort is now achievable in days with new field-based design software for exponentially reduced product development times. It’s possible to move quickly, gain deep product knowledge, and derisk the whole product development effort. Connecting multi-dimensional, early-stage design exploration with an implicit engineering workflow that robustly rebuilds ready-to-manufacture parts is supporting more widespread use of AM and changing the way products are developed.
Blake Perez is Senior Application Engineer at New York-based nTopology .