Very good description of the process Jon. You mentioned iterating through the modeling process and tests once a candidate motor is selected. I would suspect the requirements for prototyping could be reduced but not completely eliminated. And good model based testing would go a long way toward making sure the prototypes were very close to a final design and implementation.
I am curious as to how the models might be updated with feedback based on prototype testing. Validation of the model inputs based on testing the prototype might be useful as well as testing failure modes.
One thing we strive for in testing is to characterize the failure modes and try to insure the system degrades gracefully under loads. Catastrophic and unexpected failures are to be avoided at least in early testing but it is useful to know just how the system can fail.
I usually learn a lot about a system when I have to examine in detail any unexpected failures. Frequently this is related to an assumption on my part that was incorrect and inadequately tested before hand. Does this happen in the models before prototyping?
How do you characterize model fidelity with respect to the real world? It seems a great deal of experience is required to model complex systems with a high degree of accuracy.
For industrial control applications, or even a simple assembly line, that machine can go almost 24/7 without a break. But what happens when the task is a little more complex? That’s where the “smart” machine would come in. The smart machine is one that has some simple (or complex in some cases) processing capability to be able to adapt to changing conditions. Such machines are suited for a host of applications, including automotive, aerospace, defense, medical, computers and electronics, telecommunications, consumer goods, and so on. This discussion will examine what’s possible with smart machines, and what tradeoffs need to be made to implement such a solution.