I have always taken the expression "common sense" to mean things that are obvious to anyone except a fool. That is quite different from the expression referencing "conventional wisdom", or it's equvalent, "common thinking", which refelects the unverified assumptions that are usually based on sources less reliable than gossip.
Common sense dictates that one would not stick a hand into the cutting area of a lawnmower, or stand on the very top of a folding ladder. Common sense is that wisdom that lawyers seem to dictate that it is not reasonable to expect their client to posess.
To Gramsci, "common sense" meant the ruling ideology of a society: the ideas that are generally accepted without much critical thought. As such, "common sense" is not universal, but may be radically different in different times and places (according to Gramsci, mainly depending on who's in charge).
"Good sense," on the other hand, requires critical thought.
"Common sense" might contain some elements of "good sense," but they are very different things.
Dave's comment about "intuitively obvious" makes me think of how many times I've been told that a certain practice is "common sense." My response is always "common to which group?" The term implies shared values and meanings, and shared assumptions about how the world does and should work. But these are not so common In a modern complex society.
Another great article from Professor Craig. The only part I take issue with is the claim that "the grey-box modelling approach is intuitive and obvious." First of all, our intuitions can often mislead us. Read The Invisible Gorilla, by psychologists Chris Chabris and Dan Simons, for many instructive examples of everyday illusions. Second, if these things were obvious, there would be no need for engineers... let along engineering professors!
On the subject of modelling the real world, take a look at Dan Meyer's blog. Dan is a high school math teacher who gave a though-provoking TED Talk called Math Class Needs a Makeover. Dan puts modelling at the center of his teaching. It's an approach that can help prepare students to engage in engineering problem-solving.
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.
Using Siemens NX software, a team of engineering students from the University of Michigan built an electric vehicle and raced in the 2013 Bridgestone World Solar Challenge. One of those students blogged for Design News throughout the race.
Robots that walk have come a long way from simple barebones walking machines or pairs of legs without an upper body and head. Much of the research these days focuses on making more humanoid robots. But they are not all created equal.
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.