I can go both ways on this one. I have had designs that were quickly finished by engineers sharing disciplines, but then there have been the really complicated designs that required specialists in each field. As a manager, it was important to determine which design was in front of me and gather the correct engineers to find the solution. It really comes down to off the shelf parts. If I can find an off the shelf pump to get the job done, then I don't need an ME to design a pump. If I can find an off the shelf controller to get the job done, then I don't need a EE to design a control. If the design needs to be cheap, small and efficient, then I will need the whole team to do what they do best.
The separation of engineering efforts into different areas does allow more work to get done, mostly because of there bheing more workers. But the situation of having engineers skilled in more than one area is very valuable. For starters, it allows collaboration between those who understand, as opposed to a collection of experts working on different parts of a project, clueless as to what the others are doing. This is also important in the very beginning stages of a project, where the overall functionality crosses many different areas. It is quite a challenge for a programmer to understand what a machine must do, while it would not be that hard for the mechanical design engineer to master the PLC programming neded to craqte the correct sequence of actions and the associated interlocking controls.
But finding people who have expertise, or even just a good understanding, of more than one area is a challenge, and convincing HR folks to even consider looking for them is a task. And, quite unfortunately, many organizations are not willing to pay more for somebody who has all of those skills.
Excellent post Al. I think we are to the point that survival as an engineer depends upon that engineer having talents that will allow conformance with and performance in the "digital age". I know old guys like me are working to re-educate ourselves and adopt new methods of working so we are at least comparable to recent graduates. Imagine how valuable we would be having 30 plus years of experience AND the ability to learn and manipulate any software available. That has to be a definite plus.
Rich, I would say that digital-mechanical engineers definitely need much of the skill set of mechatronic engineers. What the ultimate vision seems to be encompassing is more sophisticated algorithms/cloud-based data collection systems that go far beyond the kind of data analysis found in today's industrial systems. Might require an advanced programming capabilities not commonly found today to develop the more intense data analysis and control algorithms required.
Cabe, I agree this sounds like it needs AI functionality but hopefully there is a middle ground with intelligent algorithms that don't depend on learning but can create value by saving energy, for example, with less sophisticated solutions. Instead of big data analysis, maybe system analysts can target more specific data to meet more targeted goals.
Sounds like "Intelligent Decisioning" will only happen when artificial intelligence is created. Something akin to the computer in Star Trek Next Generation. As can be seen with Apple's SIRI and Samsung's S-Voice, the top of the line digital assistant leaves much to be desired.
It was found that most accidents in an industrial environment are due to human error. Removing human thinking from processing is a great idea. Automation has proven that. Too bad so few are going into Industrial jobs these days. The intelligent factory has to begin with intelligent people. Making consumer gadgets sounds like more fun than factory electronics.
Interesting that you point this out Al. My research over the last few months has show that there's more than a little blurring taking place amongst the various "traditional" engineering disciplines. Would you say that a digital-mechanical engineer is different from a mechatronics engineer?
I agree with naperlou, Al. Great article on an important subject. A few days ago, I talked to David Cole of the Center for Automotive Research (CAR), who used to be head of the automotive engineering department at the University of Michigan. Cole said that although the traditional curriculums (i.e., EE, ME, etc) still exist, the walls are gone between the those departments at the big universities. Today's grads can't afford to ignore the other disciplines. Electronics and microprocessors now touch virtually everything an engineer can design.
Al, great article. There seem to be many efforts around to realign education with the needs of industry in engineering. This is fully appropriate. In the communications area, the IEEE Communications Society is proposing a communications engineering bachelor's degree. In computer science, there are CS, computer engineering and information technology degrees, all of which cover different areas of the field.
Frankly, the ability to cover the digital control area and the mechanical area is more of a stretch. The skills and education required are very different. In my experience in the aerospace industry we had many engineering disciplines applied to each project. In electrical we had controls, power and logic engineers. In mechanical we had thermal, structural, mechanisms and materials. We also had software engineers and systems engineers. I held both of these roles. It was really the system engineering discipline that brought the whole thing together.
Now, as you mention, we need data engineers. These will generally be statisticians and computer scientists. What we really need is a teaming approach like we had in the aerospace industry. Many of the techniques are really the same.
Are they robots or androids? We're not exactly sure. Each talking, gesturing Geminoid looks exactly like a real individual, starting with their creator, professor Hiroshi Ishiguro of Osaka University in Japan.
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.