The article is certainly correct. Of course, in order to be able to make all of those choices it is mandatory to understand the application. MY best example comes from years ago, which was selecting rear wheel bearings for a custome made motorcycle rear wheel. IT turns out that machining the wheel is not really that hard, but in order to pick the right bearing I had to understand the loading, in addition to the speed and chain tension. At the time I had not been to engineering school yet, besides that, it was not in the realm of what they taught EE students. Ultimately I picked bearings intended for the front wheels of a small car. This was a good choice because they lasted and never gave any problems. In addition, if they had failed I could have purchased replacements in almost any town in the US.
Were they "overkill"? Possibly they were more than I really needed, but isn't reliability worth a lot?
Great summary of the constraints of design envelope, load, alignment, stiffness, and precision. Keeping an eye on these issues will reduce early failures, which is the plague of all bearing applications. And, of course, don't forget lubrication.
Truchard will be presented the award at the 2014 Golden Mousetrap Awards ceremony during the co-located events Pacific Design & Manufacturing, MD&M West, WestPack, PLASTEC West, Electronics West, ATX West, and AeroCon.
In a bid to boost the viability of lithium-based electric car batteries, a team at Lawrence Berkeley National Laboratory has developed a chemistry that could possibly double an EV’s driving range while cutting its battery cost in half.
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.