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Using Knowledge Smarter

February 1, 2011 by Ron Averill

“The world is changing very fast. Big will not beat small anymore. It will be the fast beating the slow.” – Rupert Murdoch


RunnerWhen computer aided engineering (CAE) analysis techniques, like the finite element method, were first introduced, their primary role was to investigate why a design failed. Surely, this understanding would help designers avoid such failures in the future.

But soon, manufacturing companies realized that it was smarter to use CAE tools to predict whether a design would fail, before manufacturing. This gave designers the chance to make changes to designs and avoid most failures in the first place. This pass/fail test is still in place at many companies, in the form of scheduled iterations of computer aided design (CAD) drawings followed by CAE simulations.

Often, companies decide on a fixed number of manual CAD/CAE design iterations ahead of time. I’ve often wondered how project managers know exactly how many iterations it will take to arrive at the best design. Naturally, they haven’t figured the last-minute redesign “fire drills” and disorganized patchwork of final design changes into that preset number of design iterations.

Despite overwhelming evidence that a CAE-led design process usually yields better solutions in less time, CAD designers are still driving the iteration process at many organizations. There, CAE results are used just to verify the intuitions of the CAD designers or to suggest regions where the designers might consider making changes.

In many cases, the minor role of CAE is made clear by the level of detail included in each set of CAD models. Often, even the very first CAD model contains a complete spec of every fillet, bolt and washer, including the heat treatment requirements for each material used. Sometimes, even the tolerance stacks are already computed!

Then, the first thing a CAE engineer must do is defeature the CAD model, or remove all of the geometric details that should not be included in an effective CAE math model. And, when the CAE simulations suggest the need for design changes, a large portion of the detailed CAD modeling effort is suddenly made obsolete.

Additionally, while the CAE models are being built, it is likely that the CAD team has already embarked on the next version of the design, making even the CAE simulation results obsolete before they are completed. In organizations with a CAD-driven design process, it is not surprising that the ratio of CAD designers to CAE analysts is much higher than it needs to be.

If our goal is to accelerate the invention of higher performing and more robust designs, then we must draw upon the full power of CAE technology to lead the design process.


CAD models should be built to support the CAE process, not the other way around. Design directions should be determined by knowledge-creating CAE simulations, not an intuition-limited CAD process. The number of design iterations should be governed by the problem complexity, not an ad hoc decision now residing in a best practices document.

Moreover, today’s CAE-based automated optimization technology allows hundreds of design iterations to be studied in less time than most companies spend on two or three manual CAD/CAE iterations. These automated iterations are guided by intelligent, mathematical optimization algorithms that are not limited by human intuition or brain capacity.

Better solutions faster, using knowledge smarter. This is what media magnate Rupert Murdoch had in mind when he said, “The world is changing very fast. Big will not beat small anymore. It will be the fast beating the slow.”