Thomas Edison demonstrated the first long-lasting, high-quality light bulb in 1879. His successful design resulted from a long and laborious trial-and-error search for the best filament material, a process we now call the Edisonian approach.
Edison’s determined and tireless pursuit of innovation is also evident in some of his famous quotes:
“When I have fully decided that a result is worth getting I go ahead of it and make trial after trial until it comes.”
“I have not failed. I’ve just found 10,000 ways that won’t work.”
While Edison had to create physical prototypes to test each new design, modern advances in computing power and computer-aided engineering (CAE) software now make it possible to create virtual prototypes based on mathematical models. As a result, design trials are easier, faster and cheaper than ever before.
Yet despite our technological advances and our fervent claim that “innovation is the key,” we tend to do fewer – instead of more – design iterations before releasing a new product. Do we lack Edison’s desire or energy to innovate? Not likely! Instead, our ability to innovate is most often limited by a combination of time-to-market pressure and ineffective use of CAE technology.
If only we could iterate designs more systematically, more efficiently and with more confidence that these efforts would lead to a more optimized or more innovative solution. Then, we could realize rapid innovation even in the face of increasing time-to-market pressure.
Fortunately, a new generation of design optimization technology and software provides the ability to efficiently explore a much larger and more complex design space. This new optimization technology is truly in agreement with Edison’s mantras:
“To have a great idea, have a lot of them.”
“Genius is one percent inspiration and ninety-nine percent perspiration.”
Yet most of the “perspiration” is now carried out by advanced computing infrastructure. Modern search algorithms take full advantage of powerful and inexpensive computers and networks to modify virtual system models while intelligently searching for optimal values of design parameters that affect product performance and cost.
This means that designers can take into account a larger number of design variables, each with a wider range than previously considered practical. This dramatically improves the odds of discovering a much better design…perhaps even a new concept that is outside the initial intuition of the engineering team.
By leveraging an engineer’s potential to discover new design concepts, this new generation of automated design technology expands the limits of human intuition and extends a designer’s professional capability to achieve break-through designs and Edisonian-like innovation.
“There’s a way to do it better – find it.” – Edison