The quality of a CAD database is directly correlated to its adaptability.

What do I mean by that? If you’ve designed a device (let’s say an orthopedic brace) and want to change something fundamental to the design (e.g., the shape and size of the arm to fit in the brace), an adaptable database will dutifully read your request and update all subsequent surfaces and parts such that your design intent remains intact.

But while all high-quality traditional databases have some degree of flexibility, precious few of them are adaptable.

Consider, for example, the humble office chair. It has some surfaces that attempt to conform to your body, a few mechanisms to adjust height and tilt, several wheels at the base, spokes reaching out the wheels from the column in the center, and perhaps the armrests are adjustable as well. A traditional CAD database would have, by virtue of the techniques used in its development, a certain degree of flexibility. An adaptable database would take things a step further.

Warning: The sections on traditional databases may seem a slightly esoteric if you don’t spend much time in CAD. For the big picture, skip ahead to The Value of Adaptable Databases.

Traditional databases

Sticking with the chair example, a high-quality traditional database would allow you to adjust the range of motion of the tilt mechanisms, perhaps the number of wheels at the base, the length of the spokes attaching the wheels to the center column, and so on. Generally speaking, any feature of the chair that can be defined by a single number could be changed and updated. Some of the means used to reach such an end:

• Your CAD should be DRY (Don’t Repeat Yourself)
• Surface Refs > Edge Refs > Point Refs
• Good dimensioning is as little dimensioning as possible (but not less)
• 10 simple features > 1 complex feature
• and so on…

While these will take you a long way, adaptable databases, as mentioned before, go further.

Adaptable databases

By implementing additional techniques on top of traditional best practices, design intent is able to be so thoroughly baked into an adaptable database that its flexibility is no longer limited to a few discrete parameters. Instead, it’s able to read user-specific scan data and adjust the height, length, width, and surface curvature such that the resulting database is now custom-fit to the user.

Consider the following techniques when building an adaptable database. The level of adaptability required will drive to what extent (if at all) such additional techniques will be implemented.

Use splines

Splines are complex. While the descriptions of lines and arcs are so clear that it’s difficult to remember not knowing them, a description of splines will quickly get you into calculus territory. It’s no wonder that as you look around you are likely to see yourself surrounded by products constituted of lines and arcs instead of splines. The engineers of those products are telling you something: using splines is hard.

That said, splines are the lifeblood of an adaptable databases. This is especially true in databases that want to adapt to user anatomy: there aren’t (m)any pure arcs anywhere on our bodies, and trying to force something is a path fraught with peril.

What is truly important?

When an FEA study reveals a weak spot, it’s likely the problem can’t be fixed by changing one parameter. In such a case, the traditional approach is to adjust several dimensions in the model to sufficiently mitigate the problem. This works for traditional models, but leaving such a critical area defined implicitly rather than explicitly puts your database at risk when it scales.

The adaptable approach is to rebuild the model such that the weak spot is explicitly defined; ensuring that it does not poke its head out again as the database begins to adapt.

Make it data-driven

In the age of big data we are limited not by the amount of data we have, but by our ability to leverage it; product development is no exception. Anthropometric data, use scenarios, FEA, cost of goods, etc. would all, in an ideal world, drive the CAD database. Instead of such an explicit relationship, information typically makes its way into the CAD database via a more circuitous path:

• Data is obtained
• CAD is adjusted to reflect new data
• Design is prototyped
• Prototype is tested
• Tests produce data
• Repeat

This continues until the test data indicates that the performance of the prototype is acceptable. It’s a sound approach, but difficult to scale. Geometry that is explicitly driven by data rather than becoming an iterative approximation of it is bound to be more adaptable.

Consider optimization

Once you have built a sufficiently adaptable model, you have the opportunity to take advantage of some high-end capabilities.

Top CAD programs provide the capability (either through additional off-the-shelf extensions or access to an application programming interface) to set up studies that can take a “generic” of your design, read in a 3D scan of an object (e.g., a part of a patient’s anatomy), compute various analyses between the “generic” and the scan (e.g., distance between the two at a specific point), then adjust dimensions to optimize those parameters (e.g., minimize the aforementioned distance).

These extensions and APIs are the mechanisms that enable adapt-able databases to adapt.

The value of adaptable databases

You may have already guessed why most databases today aren’t adaptable; it’s hard and the benefits aren’t significant enough to make the effort worthwhile. Most companies with products of various sizes have relatively few variations (think S, M, L, XL). In this case, adaptability is superfluous.

But what if a competitor was able to offer thousands of sizes and able to clinically prove that his/her device, by virtue of its superior fit, was able to improve performance, patient compliance, and recovery time? What if the cost of such a device was nominally more expensive, but (for the reasons just mentioned) provided greater value?

In such a circumstance, the ability of your CAD model to adapt goes from being a nicety that smooths the ECO process and manufacturing tweaks to a full-blown competitive advantage. 

Today, this is the case for high-end implants. Some companies are currently working on such an approach for prosthetics. Forward-looking organizations will keep tabs on this trend, and adapt accordingly.