Computational modeling provides automated workflows, designs for promoting osseointegration
Additive manufacturing has become increasingly sophisticated in recent years, capable of producing orthopedic implants with complex lattice structures that further enables osseointegration. But the software needed to design such innovative medical products has, until recently, lagged behind the hardware used to make them.
Old Software, New Design Needs
Traditional CAD software was essentially designed to digitize by-hand drafting—but drafting and classic CAD were most relevant when subtractive manufacturing and CNC were the primary methods of machining. Now that additive manufacturing (AM) has moved into end-part production, directly manufacturing a much wider variety of highly complex geometries has become possible. Yet such geometry can be difficult to define when using those older drafting-based methodologies.
It’s easy to say, “The distance from point A to point B is 10 mm,” but how do you define the complex, organic-looking surfaces you’ve created in between those two points when you’re designing a trabecular lattice for a bone implant? It’s not feasible to just note that there are 40,000 different radii callouts there. You need a new way to represent that intricate geometry and all the driving parameters behind it.
Some software makers have labored to try to represent such complex structures by designing what are essentially workarounds inside traditional CAD. The result can be dense, extremely populated, and very, very heavy files. These make it highly difficult to manage all those edges and curves within a design. Run times can be massive; don’t try to modify or change any geometry and expect quick transitions that allow you to go deep into design exploration.
There can be another challenge when using CAD-based software that may have been used to validate a device with the Food and Drug Administration from years prior—perhaps that version of the software may no longer be supported by the company that created it. To make new design changes in a product based on these, additional testing and expensive gap analysis are typically required to prove that your “new” software still correlates with the previous, validated version and produces the same end results.
Computational Modeling for AM
Recent advances are now providing solutions for many of these challenges. Implicit algorithms and field-driven design capabilities can manage huge amounts of data and complex geometries much faster than more traditional processes, and in near-real-time.
Automated workflows rapidly update designs to speed innovation and meet competitive imperatives. Material parameters, stress analysis, and performance are easily evaluated with adjunct simulation capabilities. Outputs and workflows are automatically captured for sharing and reuse by internal teams. The mathematically-based, precise repeatability of this new technology is not only valuable for product development, it is easily documented for regulatory submission; data that is generated will remain consistent over time, even as software capabilities continue to evolve.
There’s a bonus to this approach that goes far beyond the design stage. The resulting geometry can be delivered, “pre-sliced,” directly to a 3D printer. It eliminates the need for an intermediate, often-labor-intensive STL-file-management stage. This sliced data is the digital foundation that drives the layer-by-layer AM process that fuses the metal powder and builds the finished product.
Familiar Materials, New Designs
No matter what design software is being used, bone-friendly implants are still limited to a few proven materials in the highly regulated medical industry. Historically, these have been non-reactive materials like titanium, PEEK and stainless steel that have all been successfully machined in the past. The industry understands their characteristics and the FDA has already approved them for human use so there are fewer restrictions on products made from them.
As AM began emerging as a new solution in the medical industry, AM equipment makers wisely stayed with these core materials to avoid risk. Advanced computational modeling can help realize the full potential of these familiar materials to be “architected” for 3D printing.
Control At Any Scale
Such architected-material design capabilities are available in easy-to-use “tool kits” that provide the medical design engineer with total control of geometries for AM at both the micro and macro scales. Functional models with hundreds of “unit cells”—or hundreds of millions of them—are now easily generated. Unit cells can be controlled for parameters like strut thickness or pore size, and further customized or graded for optimal osseointegration.
These cells can then be locked down, and subsequently applied to any surface or volume in a repeatable workflow across an entire line of devices. Surface properties can be achieved by quickly applying a specific or dynamic texture to an entire surface, regardless of its complexity. Volume-filling lattices can seamlessly shift from one geometry to another, resulting in structures that reinforce weak regions or minimize failure points. Smoothly transitioning complex lattice shapes to external solid features enables engineers to simulate biological mimicry of bone structures like trabecular elements and the seamless organic transitions between them.
This combination of design freedom and control can be extremely powerful, allowing for complex manipulation of true orthotropic properties. Engineers can use simulation to study how bone ingrowth factors might affect the macro stiffness of an implant over time, and learn how to create unique lattice elements that prolong biologically optimized, long-term benefits. With AM machines now capable of resolving strut sizes well under 0.200 mm and features as small as 0.080 mm, manufacturing of such complex structures is increasingly possible.
Freedom for Design Exploration
By engaging the powerful computational modeling tools described in this article, the design engineer can explore new geometries and surfaces that are now possible to manufacture with today’s advanced 3D printers. The end goal of this approach is more rapid customization of designs that can increase the ability of physicians to improve patient outcomes, comfort, and safety.
Christopher Cho is senior application engineer of nTopology