New Podcast with Chris Gammell

[Update: The podcast has a new home at The Engineering Commons!]

Chris Gammell, co-host of The Amp Hour podcast, kindly allowed me to join him in creating a podcast dedicated to engineering’s more philosophical issues. You can listen to our first session below:

[Update: You can download the mp3 file directly, if you prefer not using the SoundCloud widget above.]

In this episode we discuss jumping off into a new design effort. What do you do when you don’t know where to start?

  • We discuss the need for engineers to take a greater leadership role in society. (See the Forbes’ opinion piece: Engineers: Our Government Needs You. While we did not discuss this article, as it had not yet been published when we recorded the episode, it seems somewhat apropos.)
  • The “messy” nature of design is covered, and we laugh about the neat, linear nature of the engineering process, as portrayed in textbooks.
  • Jeff shows his advanced age by referencing an Opel GT, which was produced between 1968 and 1973, and featured a bump where the carburetor stuck up into the hood.
  • A TED talk by Tim Harford is cited as Jeff and Chris talk about having to work through design problems via trial-and-error.
  • A happiness curve for the design client is painted in words, with the associated moral that frequent communications are vital to a successful design effort.
  • Jeff addresses why pi is the “magic” multiplier for time and effort estimates.
  • A hot-selling book in the start-up field is Eric Ries’ tome, The Lean Start-Up. Projects are encouraged to try out a “minimal viable product,” or MVP, as quickly as possible.

Chris and I would be quite interested to learn of your reaction to the podcast, and to learn where you think we should take future episodes. Thanks for your input!

Posted in Engineering Roles | 14 Responses

Benefits of an abstract engineering education

My prior post was critical of the abstraction-based, mathematics-heavy, and computation-focused eduction served up to today’s engineering students. It has been my experience has been that the most successful engineers in industrial practice are very hands-on, with years of practical experience, and insights that have little to do with academic equations. That’s not to say that the academic abstractions are unimportant, for they provide the language of discussion. An intelligent conversation about, say, a motor drive system, requires participants to understand that additional motor horsepower is not necessarily needed to produce greater output torque. (An engineer friend recently shared with me his attempt to explain this to a non-engineer boss. In the end, the boss said, “This doesn’t make any sense to me, but if you say it’s so, it must be.”)

Despite possible shortcomings in the current methods of engineering education, today’s graduates continue to be hired, and paid well-above average salaries—so employers obviously benefit from bringing on students who survive the current curriculum. Whatever modifications I might advocate for improving engineering pedagogy, I would certainly hope not to abandon these beneficial characteristics of today’s engineering programs. So what does an engineer learn from dealing with high levels of abstraction?

  • Perseverance — It often takes hours, maybe even days, to work through a single homework assignment. One has to look at each problem statement and begin trying to match the available information with potential methods of solution. Often times, roadblocks in understanding are encountered. The instructional material needs to be re-read, and re-evaluated to build a more comprehensive understanding of the solution technique. If the solution is not already known (from prior semester homework files, or found online), then a student has to at least convince themself that their path of deduction and computation is logical and rational. Shortcuts in this process are rarely rewarded in the long run, and engineering students rarely make it past their sophomore year if they haven’t acquired a healthy dose of perseverance.
  • Emotional suppression — Solving math problems is not a matter of nuance, or persuasion. It doesn’t matter how mad you get, or how optimistic you may be. Either your solution works, or it doesn’t. A successful student learns to keep cranking away at a problem until the correct solution is found, regardless of their emotional state.
  • Delayed gratification — Not withstanding the time spent working individual problems, engineers have to spend several years working on fundamentals before they can begin to solve higher-order engineering problems. Want to study photography? Grab a camera and go take some pictures. Want to be a writer? Start a blog. Want to be a civil engineer? Then expect to take four semesters of prerequisite courses to get to the point where you can begin seriously talking about bridge construction. In fact, your four years in engineering school are just a warm-up for going into industry, where you have to learn how engineering is carried out in the real world.
  • Humility — Solving complex math problems leads one to be wary of false optimism. A dropped sign, a transposed notation, a forgotten property, a misunderstood application—all can lead to an incorrect solution, even though the methodology seems correct. This aspect of the engineering education has been well-stated by Vivek Haldar:

    To be a successful software engineer (or indeed, any engineer), one first needs to be utterly and completely broken by failure. One must be so humiliated by a complex system that they give up and realize that the only chance of moving forward comes from being a supplicant to the complexity, by approaching it with humility and caution, not with hubris. You have to listen to the system, coax it into behaving. Commanding it does not work.

  • Sub-problem identification — Getting the hang of breaking complex problems into solvable sub-problems is an important engineering skill. My understanding of how this could be accomplished came in the second half of my sophomore year. No longer did I have to look for an equation or method that solved an entire problem at once; I could break the problem into smaller parts and solve the sub-problems individually. This was my first inkling that I might actually be able to “think” like an engineer.

Any other benefits to a traditional engineering education that I’ve missed?

Posted in Engineering Curriculum | Tagged , , , , | 7 Responses

Bait and switch in engineering education

It seems that many children have never considered the possibility of an engineering career. Well-meaning programs therefore attempt to nudge students toward engineering through exposure to engineering-like projects. These activities most often involve the manipulation of physical objects, such as constructing toothpick bridges, building LEGO models, preparing for an egg drop, or working with robots. Youngsters may alternatively be presented with a simulation of working with physical objects. As I’ve stated before, the role of an engineer is to implement new methods, devices, or systems. For an engineer working in the physical realm (a physineer), this means constructing something novel in the material world. So I have no problem with these introductory programs attempting to capture the imagination of students with physical-realm activities. My concern is the abrupt switch to purely abstract thinking that we impose on those who have expressed a desire to pursue an engineering career.

When students are sent off to college, they are immediately thrown into a series of math and physics courses that can seem wholly irrelevant to the activities that enticed them to enter the engineering curriculum. Although my undergrad experience took place more than thirty years ago, I remember struggling to rationalize how my knowledge of integration techniques was going to help me design production machinery. I had seen industrial equipment being built in a local machine shop, and I was fascinated with the banks of electrical relays that automated mechanical movement. (This was prior to the time that PLCs came into wide usage.) I wanted to learn how to design such machinery, and so I enrolled in my state’s largest engineering school, taking both mechanical and electrical engineering courses. Although I’m not sorry that I learned calculus along the way, I can safely state that knowing how to integrate by parts was never of benefit in my industrial career. In fact, over the two decades I spent in industry as a design engineer, I never found myself needing to solve an integral equation.

Given that several decades passed before I returned to my alma mater for a PhD, I presumed that the situation had improved. But my conversations with students currently in the middle of their undergraduate studies suggest that things are marginally better, at best. They are learning techniques of solution, but have little engineering insight. Children of my college buddies are now enrolled in various engineering schools around the country. When I talk with them I hear similar stories of being led blindly through math-heavy courses that appear to have little relevance to what they’ve heard at home about real-world engineering duties.

Is it any surprise that students are dropping out of this type of curriculum? Some claim the subject material is simply too hard. However, I think that Robert Talbert correctly identifies the problem:

Students aren’t put off by hard work. They are merely put off by any kind of work that doesn’t appear to be worth the effort.

I’d go further and say that engineering students can’t see the connection between the abstract and physical realms. This confusion is reflected in the following illustration, which has been floating around the internet. (Click on the image to see full size graphic.)

[The upper graphic seems to be from Valve's Team Fortress 2. If someone knows which textbook the lower derivation is from, I'd be happy to give appropriate credit.]

The sad part is that this graphic is wrong. While research engineers may work in high-level abstractions, a great many engineering activities do not need such advanced mathematical acumen. In fact, many engineering problems are solved with spatial or experiential skills that require little mathematical prowess. So while I see nothing wrong in teaching abstract thinking, I think that engineering studies should advance from the physical realm toward the abstract realm, rather than the other way around. Otherwise, we’re promising to teach students one set of skills (to entice their enrollment), and delivering something else entirely. It strikes me that we’re teaching them how to be graduate students, rather than employable engineers. I suspect that this misalignment has real costs for students, employers, and the nation.

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Engineering spectrum differences

In my prior post, I proposed that each engineering position requires a different level of abstraction. To a research engineer, almost everything is model-based, while the production engineer may be primarily focused on issues that are object-based. Although freshman and sophomore engineering students receive guidance as to the sub-discipline they should enter (electrical, mechanical, chemical, etc.), I’ve never seen any discussion about specializing in a particular level of abstraction. So I want to illustrate how skill sets vary according to one’s position along the engineering spectrum. The following abbreviations are used below: high abstraction (HA), moderate abstraction (MA), and low abstraction (LA).

Solution focus

HA: Primary focus is finding an optimal solution within a tightly controlled problem domain. Journal referees don’t want to read about yet another mediocre solution; they want to see mathematical, statistical, or experimental evidence that the proposed solution is in some manner better than previously discovered approaches. Only a single solution can be considered best.

MA: Central effort is placed in discovering a bounded solution. For instance, a bridge doesn’t have to be optimal in every respect, but it had better withstand the specified traffic loads. A bridge with too much strength is of far less concern than one with too little carrying capability. Any solution that meets the project constraints is potentially useable.

LA: Making sure that each component/batch/output is operating correctly often requires a rapid solution. If a manufacturing process is going out of tolerance, the first concern is getting product back within tolerance. Causes of the deviation can be examined later, or passed on for further study, but the key focus is on quickly finding a solution that works. For outputs of sufficient financial worth, almost any workable solution will be considered acceptable, at least on a temporary basis.

Solution domain

HA: Solutions are developed in the symbolic domain, where analytic tools of mathematics are most effective.

MA: Problems are solved in the spatial or schematic domains, where computer-aided-design (CAD) tools allow the consideration of multiple solution possibilities.

LA: Troubleshooting success is highly dependent upon prior exposure to similar problems, and thus the requisite skills are experiential in nature.

Social influence

HA: Symbolic solutions stand on their own, and require minimal social interaction to be presented and accepted.

MA: Gathering problem specifications, managing organizational expectations, and presenting solution proposals requires a moderate level of social interaction.

LA: Talents in motivating and managing others are quite valuable in bringing the right technical skills to bear on a problem, and in coordinating troubleshooting activities, especially in a high-pressure manufacturing environment.

Temporal effects

HA: Symbolic solutions do not care when a system is set into motion, as nature’s laws are assumed not to vary with the passage of time. Thus, problems of high abstraction accommodate everlasting solutions.

MA: Schematic solutions can remain valid over long periods of time. However, as types of components or methodologies change with time, moderate abstraction problems may need to be updated and improved.

LA: Corrective solutions may be specific to particular outputs, or a specific set of events acting on an output. Thus, actions associated with low abstraction problems are highly time dependent.

Summary

Individual engineers may have to move up and down the engineering spectrum over the course of a career, or a year, or even a single day. This post has attempted to point out that the skills needed to be a successful engineer necessarily vary with the abstraction level being utilized. In my next post, I’ll discuss why most engineering students are only exposed to high abstraction skills during their time in college.

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Along the engineering spectrum

My prior series of posts (part 1, part 2, and part 3) proposed that a common characteristic of all engineering activity was the use of abstract models to search a solution space before implementing a new method, device, or system. Engineers fill the gap between science, which seeks to generate accurate models, and manufacturing, which aims to accurately replicate a given embodiment. Some engineers are more “hands-on” than others, so we have a spectrum of engineering activities that range from mostly abstract to mostly physical. It is this spectrum of engineering skills that I want to discuss. (Since the traditional engineering fields focus on the physical realm, I’m addressing that particular domain. However, the same spectrum issues will exist for engineers operating in alternate realms. See my prior posts for a discussion of engineering realms.)

There are many different kinds of engineers, but I’m going to constrain the conversation by dealing with just three types of engineering professionals: research, design, and production engineers.

High Abstraction

Research engineers operate at a high level of abstraction. This necessitates that they be part scientist, and part applied mathematician, as well as an engineer with knowledge of the physical world. When striving to create models that more accurately represent physical world behavior, they take on the role of scientist. In creating such models, they tend to look for general behaviors that apply to all systems, or sets of systems, rather than the idiosyncratic behavior of individual implementations.

Researcher engineers do not normally need to extend the bounds of mathematics (although some do), but must have very strong mathematical skills to create and manipulate abstract models of physical behavior. A researcher in chemical engineering might, for instance, develop a new means for causing to plastics to decay in a ecological manner. The primary focus would likely be on a model that explains the decay mechanism, rather than how the method might someday be introduced into production.

Moderate Abstraction

Design engineers bridge the gap between high and low levels of abstraction. While they are free to consider a broad range of implementation methods, they must eventually deliver a design that will produce a desired result. They use existing models to examine possible solutions, rather than launching new experimental studies.

Rather than seeking a theoretically optimal result, as the researcher might, the design engineer must settle for a practical compromise between competing interests. Experience plays heavily into knowing how much weight to give to each design constraint. A thorough knowledge of manufacturing methods is also crucial to the design engineer, as the end design must allow for robust performance, whether for a single prototype, or for a product that will be reproduced millions of times.

Low Abstraction

A production engineer worries about keeping the manufacturing line operating in a smooth fashion. There is no need to worry about the theory of how the end product operates, or what it is going to look like–those decisions have been made upstream by the research and design engineers. Instead, the production engineer is mostly a troubleshooter, waiting for a production snag to arise, then resolving the issue in a manner that keep the problem from reoccurring.

While the research and design engineers can worry about more global issues, the production engineer has to be concerned about individual elements. Specific batches, machines, and output units each give indications as to the state of the manufacturing process. Coordination of corrective efforts, mixed in with the natural stress of high-volume manufacturing, requires that the production engineer be a strong communicator.

Summary

Just as there are differences between the various realms of engineering, each engineering job requires its own level of abstraction. In my next post, I’ll write more about the skill sets needed along the engineering spectrum, and their impact on engineering education.

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What engineers have in common (part 3)

(Part 1 and part 2 of this series)

In my most recent post, I proposed the following definition (which I’ve slightly revised):

engineer: an individual who designs novel methods, devices, or systems that can be practically implemented to meet specified constraints, or analyzes existing methods, devices, or systems for their capacity to meet such constraints, while making judicious use of scientific models, mathematical analysis, and prior experience.

Engineers working in the physical realm (which I deem to be physineers) use theoretical models that capture how our universe behaves. Strong problem-solving and math skills are used to manipulate those models in a manner that leads to the creation of new physical embodiments.

I note here that working with “real world” objects, in and of itself, does not make one a physineer. A carpenter constructing wooden chairs operates in the physical realm, but we do not typically refer to such an individual as an engineer. If a chair leg were to break, a reasonable carpenter might attempt to replace the failed leg with a larger one, as simple observation of nature lends credence to the notion that bigger is often stronger. However, constructing chairs on a “trial-and-error” basis to find one that does not collapse, but does not waste material, is neither judicious, nor innovative, and is therefore not engineering. I interpret a “judicious” analysis to be a considered compromise among the various parameters influencing the work—including factors of size, weight, environment, material, maintenance, production, and cost. This often requires the development and manipulation of abstract models that permit a multitude of parameters to be evaluated. If such models have already been developed, and acquisition of a solution requires only that a predetermined sequence of computations be faithfully carried out, then the process is again not engineering, as it is not “novel.”

In contrast, a physineer might take the concept of a chair and create an abstract model that can be analyzed and manipulated. This abstraction might take the form of a free-body diagram, or a set of mathematical equations, or computer code. Important parameters could include the type of wood being used and the maximum weight that the chair is to support—as well as the equipment and methods available for fabricating a new leg. Most importantly, a physical engineer keeps in mind the eventual implementation of the modified design. There is little benefit to designing a replacement chair leg that uses an unavailable material (unobtanium), or has to be fabricated using an unreliable method, or incurs an unrealistic cost. By analyzing and modifying the model, a new design (or specific parameters to be incorporated into a design) can be established for a replacement chair leg. In this manner, physineers abstract reality, modify the abstraction, then implement the abstraction.

Key to my thinking is that engineers implement an abstraction. If there is no implementation, or at least a concern about implementation, then the effort is not engineering. However, implementations do not necessarily have to occur in the physical realm. Consider engineers who design database systems, or write system code. Instead of working in the physical realm, they may operate in an informational realm, as illustrated below. The STEM field assignments made in my previous post are still valid; however, the engineering concerns become distinctly different.

I propose that software engineers working in the informational realm be titled infoneers. To the extent they use models of informational behavior to design and implement new “methods, devices, or systems,” then they are as much engineers as physineers. They simply implement their work in a different realm, one that has its own set of entropic concerns. Rather than worrying about rust, or electrical noise, or structural fatigue, infoneers must deal with data integrity, network availability, and transactional states. So infoneers need less knowledge about physics than do physineers, but require a greater awareness of software and data theory. The function of infoneers, however, remains the same as other engineers–to implement an abstraction. If the work doesn’t lead to new code, or a new database, or a new application, then it’s not engineering.

I’ll even make the case, though it pains me to do so, that certain forms of “social engineering” can legitimately be considered engineering. If one uses models of human behavior to design a new means for altering social behavior, then that person seems to fall within my definition of an engineer. (Are marketers such individuals?) I’ll call engineers who work in the social realm to be socianeers. We can even start to match up fields of science with our new engineering descriptors. Physical scientists develop models that can be used by physineers, informational scientists create models used by infoneers, and social scientists advance models used by socianeers. However, each realm has distinct differences, and behavioral properties that are not easily mastered. Thus, crossing realms is difficult. Each has a different set of analytic tools, as well as different idioms to describe pertinent characteristics. However, though difficult, we’ll eventually see engineers starting to share approaches between realms.

Traditional engineering has already blurred the lines between physical realm sub-fields. We now have cross-discipline specialists in areas such as mechatronics, nanorobotics and bioprocessing. As technology advances, we will also begin to see cross-realm specializations. Perhaps an infostructural engineer will be concerned with how traffic and weather data is used to actively modify the structural properties of a bridge. Or a biovirtualization engineer will find ways to use time spent in a virtual environment to improve personal health. So I propose that we accept a wider definition of engineering, one that incorporates the possibility of realms beyond the traditional physical domain.

And what do all engineers have in common? They implement new things in a manner that finds an intelligent compromise between competing constraints in the realm of their expertise.

Of course, that’s just my opinion. What do you think?

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What engineers have in common (part 2)

(Part 1 of this series can be found here)

In my last post, I suggested the term physineer be used to describe engineers who deal with the physical realm. These are individuals employed in the traditional engineering fields, such as chemical, electrical, civil, and mechanical engineering. To see how the responsibilities of such engineers are similar to those employed in non-traditional engineering fields, say software or financial engineering, I want to take a closer look at what we consider to be “engineering” activities. In doing so, I’m going to wax philosophical for a bit—but only for a short while, I promise.

We live in a physical world. To survive in this realm we must eat sufficiently well, avoid facing extreme weather conditions without shelter, and protect ourselves from oncoming traffic. Thinking about how a mythical super-hero might catch a bullet in his or her teeth is a fairly harmless mental exercise, but attempting to replicate the feat in real life will lead (most likely) to a life-ending result. We cannot escape many of the consequences that result from our actions (and inactions) in this physical existence.

Despite our inability to escape the material realm, we humans have a natural proclivity for creating mental notions of how and why things work. These abstractions have no material embodiment; they exist only as a result of a particular pattern of brain waves. We can perceive a model to exist in our minds; but we are unable to separate it from our consciousness. I can explain to you the properties of my model, and suggest relevant analogies, but I cannot directly hand you my concept, nor can you hand me yours.

In contrast to our human traits, the physical world has no need for abstractions. (Okay, some of the quantum mechanics stuff is kind of freaky, so feel free to tell me that I’m wrong.) The apple that fell in front of Isaac Newton did not need to compute the gravitational force acting on it; it simply fell. Electrons passing through a wire do not know the wire’s resistance or the voltage drop across the wire; they simply jump from atom to atom. Force and voltage and resistance are human abstractions; models that allow us to comprehend how the world around us behaves. As we learn more about the universe, we update our models. I’m not aware of any evidence that the universe changes to accommodate our conception of how it should properly function.

So I propose a logical separation between the abstract and physical realms (with apologies to the true philosophers among you, who will accurately identify my lack of knowledge about metaphysical concepts such as Idealism and Platonic Realism.) This separation is illustrated in the figure above. The arrows represent our ability to move (mentally, not physically) between the two worlds. As mentioned above, we have a tendency to generate theories about how our universe operates. If our models (abstractions) are sufficiently accurate, then they can be used to “explain” physical phenomena. In some cases, they can potentially be used to predict events and interactions that have not yet been witnessed. Even more powerfully, our abstractions can be used to create new devices or methods that cause the physical world to behave in a manner that is more to our liking. (Think central air conditioning on a hot August afternoon in central Kansas, when the ever-present west wind has inexplicably died down for an entire week, and shimmering black tar bubbles have popped up along every inch of sealed crack in the dull, sun-baked asphalt.)

To further our discussion, I’ve added the names of certain disciplines to the diagram. These are the STEM subjects; science, technology, engineering, and mathematics. It seems to me that we can assign one of the STEM fields, in a somewhat meaningful manner, to each side of this figure. Associated with the upward arrow are scientists who observe the real world and try to explain its behavior through the creation of theoretical models. Their model development is guided by the rules of logic and analysis that mathematicians advance while working in the abstract realm, represented here by the upper box. In association with the downward arrow, traditional engineers (physineers) use models to assist in safely modifying physical behaviors, and in creating new physical embodiments. Finally, technologists utilize and operate mankind’s tools and creations while working in the physical realm, which is identified by the lower box.

As with all models, my diagram is an incomplete representation of reality (a notion better stated by Howard Skipper). One obvious flaw is that very few individuals employed in the STEM fields have the luxury (or curse, depending on your perspective) of limiting themselves to a single discipline. In addition to creating new methods and mechanisms, engineers must be able to develop new models, and should be adept in applied math. Scientists must, at times, design their own equipment and develop mathematical tools. Today’s mathematicians may interact with computer hardware or gather physical data, while technologists occasionally have to model physical phenomena or solve obtuse logic problems. But this diagram gives context to my proposed definition of an engineer:

engineer: an individual who designs novel methods, devices, or systems that can be practically implemented to meet specified constraints, or analyzes existing methods, devices, or systems for their capacity to meet such constraints, while making use of models and mathematics.

Physineers clearly fit into my definition of an engineer. But since those engaged in software, financial or social engineering do not design objects that can be physically embodied, can they really be engineers? Up until a few weeks ago, I would have said, “no.” However, note that my definition says nothing about the resulting designs being limited to the material world. In my next post, I will discuss the activities of engineers working in alternate realms.

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What do engineers have in common?

While I see many articles concerning new initiatives in STEM education, relatively little is said about the types of duties that engineers perform in the workplace. Any design process has to begin with certain constraints on the finished product, and it seems to me that an informed choice of curriculum and educational methods should begin with an understanding of the skills needed by newly graduated engineers. We live in a rapidly changing world, but are relying on an educational system that is rather dated. Simply doing more of what we’ve always done seems rather inefficient. It is also strikes me as unfair to students to make them endure an education process that is misaligned with their career interests. So I am curious if we can determine the overarching commonalities that exist in the career category we call “engineering.”

Operators of railroad trains, broadcasting equipment, boilers, and aircraft systems have long been given the title of  “engineer.” However, these duties are different from the math-intensive skills that are taught in most engineering classes. Further, just about any activity that involves planning or scheming is now described as “engineering.” I cringe each time I come across the term “social engineer” being used to describe someone who manipulates the emotions and trust of others. Alas, my discomfort with the nomenclature does little to remove it from the common vernacular. So let me be more specific in describing a particular set of engineering functions.  I’m going to identify those who graduate from traditional engineering programs (mechanical, electrical, chemical, nuclear, civil, etc.) as “physical” engineers, or physineers. Central to their skill set is a knowledge of how the physical world behaves.

A story that received a lot of attention at the beginning of last month was Facebook’s plan to open an engineering office in New York City. I have previously expressed some concern over software professionals being called “engineers.” As recently as last week, I was prepared to write a post to argue that the “engineer” moniker has been co-opted to the point of becoming meaningless. It’s not that I don’t appreciate the skills of these software experts, but rather that I believe they solve a different type of problem than do physineers. However, after giving it some thought, I’m of a less dogmatic mindset. Efforts of software engineers, financial engineers, and social engineers do, in fact, share some commonalities with those who work in traditional engineering fields. We just need better naming conventions to describe the duties that each group of engineers perform for society.

The whole naming issue is important because of the disconnect that exists between the skill sets that employers are asking for, that universities are providing, and that students are expecting to learn. A recent article on the Forbe’s website heralded the strong demand for engineering talent, but neglected to point out that most of the job openings are for software engineers. Think that becoming a computer hardware engineer is a closely related safe bet? Sorry, while the Bureau of Labor Statistics predicts that software engineering employment will grow at a mean rate of 2.1% per year, the forecast is that jobs for computer hardware engineers will grow at only one-fifth that rate. Additionally, the forecast is for 1.2 million software engineers in 2018, but only seventy-seven thousand computer hardware engineers. Employment opportunities will not be evenly distributed across the engineering terrain. We need to be far more specific in the skill sets we ask young engineers to attain. So I want to look more closely at what engineers (of all stripes) actually do, and how we might better distinguish between their various responsibilities and activities. I’ll proceed with this discussion in my next post.

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Refocusing Engineering Revision

When I started this blog two years ago, I was convinced there had to be a better way for students to learn engineering concepts. Having professors talk monotonously at an overhead screen while showing page after page of convoluted equations offers, at best, a modicum of useful insight into how one would go about solving real-world engineering problems. Having spent twenty years in industry before returning to school for my doctorate, I’ve got a fair idea as to what skills engineers use outside of the university. And, Lord knows, as I conclude my seventh year in a PhD program, I’ve sat through my share of incredibly dry lectures.

Back in 2010, I thought that better presentations, maybe Khan Academy style, would provide a simple solution. I kept pointing out to my (very patient) friends the ridiculousness of having half the calculus students in the country learning from below-average instructors. Well-produced videos seemed to me a means for addressing this problem. But it appears that mere window dressing is not what learners need. There is evidence that active learning, social engagement and spaced repetition are critical components in effective learning. The relative important of these factors, however, and the precise role they play in enhancing the education of engineering students is as yet unknown (at least to me). For this reason, I’ve not written a lot of posts in the past year and a half; I’ve simply not been able to intelligently address the issue of how engineers can best be taught.

However, a recent stream of news articles related to STEM education seems to indicate that a lot of others think they know how the job should be done. To me, these approaches seem intent on doing more of what we’ve always done; and I must admit to being apprehensive of cranking up the rate at which engineers are being produced, without a considered modification of instructional methods or curriculum focus. Thus, my intent is to refocus this blog on asking questions about what engineers do, how they think, and what duties they perform. In doing so, it is my hope that answers about engineering education will arise. If I can’t offer solutions, then perhaps I can perform a useful service by asking the right questions.

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Building Sandcastles

As a life-long Midwesterner, I haven’t spent a lot of time on the beach. However, I managed to build enough sandcastles during my youth to know that hours of effort can quickly disappear underneath the waves of a rising tide. No matter how beautiful the structure, how perfect its lines and curves, it stands no chance against a powerful sea that seeks to level everything it can touch. If the sandcastle is to be admired for more than a few hours, it has to be rebuilt. Time and time again, one must construct the sculpture, fully aware that it will erode as high tide sweeps in. Of course, the joy of construction disappears after a few days. But the destructive nature of the tides is unrelenting, so one must build the sandcastle once more.

Although I’ve long since forgotten where I picked up this sandcastle metaphor, I often use it when thinking about the life cycle of engineering projects. It is deceptively easy to believe that the job is finished once a device, or system, or methodology has been designed. However, as soon as that design is released into the real world, it will begin to erode. Surfaces idealized as perfectly smooth by the designer acquire minute ripples during the machining process. Electronic signals pick up noise, hydraulic pumps leak, bearings seize, and chemical solutions degrade. As a result, maintenance is a large portion of the engineering workload. Many engineers spend their careers doing nothing but keeping industrial processes operating smoothly. And anyone responsible for maintaining a house built more than twenty-five years ago knows that there is a significant cost, both in time and currency, associated with keeping a once-pristine structure in proper working order.

Although nature can do significant damage to an engineered system, the most severe problems are often people-related. Managers forget the caveats placed on equipment specifications, and begin demanding unrealistic production rates. Operators forget rules about proper usage, and begin to utilize machinery in applications for which it was not intended. Engineers fail to ask enough questions when integrating equipment into larger systems. And so the erosion accelerates. Devices, systems, and methodologies, once so lovingly designed to serve a particular purpose, begin to break down as they are misused, misapplied, or misappropriated.

System failure is not always a bad thing, as it may lead to knowledge of how the scheme might be improved. But it is usually a unwanted outcome, and keeping a system running smoothly requires diligent observation. A supervision style known as Management By Wandering Around involves checking in with employees, in a casual and unstructured manner, and asking questions about how things are going—so as to discover how processes and procedures might be improved. This methodology emphasizes identification of unexpected complications, as it focuses on newly-arisen issues, and is not intended as a replacement for standard performance reports. In a similar manner, frequent inspections are required to keep engineered systems operating on a reliable basis.

Although failures can be reduced via analysis during the conceptual and design phases, the most difficult problems are usually unanticipated. Thus, one can never assume that a system is “done.” I regard as cruel any engineer who tosses a design “over the wall” and walks away without regard for others who must subsequently maintain system functionality. All physical processes have to be observed, analyzed, maintained, and tweaked to offset the unrelenting tendency of nature to maximize randomness. Steel will rust, capacitors will short circuit, and out-of-spec materials will find their way into the process.

Likewise, interpersonal understandings may have to be reconstructed on a regular basis. Managers may need to be reminded as to their original agreements about specified performance. (Keeping a contemporaneous notebook is quite useful in this regard.) Operators may require a bit of nudging to return to proper operating procedures—although this should be done with an open mind, as they may be ready and able to show why the procedures should be revised. Colleagues may benefit from a better understanding of how a prior-generation system was intended to operate. But none of this can happen if an engineer holes up in a cubicle, and refuses to interact with others. There has to be a willingness to walk the beach, just to see how the sandcastle is fairing.

When engineering students are asked to carry out design projects in a period of a few weeks, just getting their design to function properly is a sufficient challenge. However, during semester-long activities, such as a senior design project, young engineers need to be made aware of the multitude of forces that may cause their designs to decay. Where possible, designs should account for the eventualities of repair, maintenance, and disposal. These issues are certainly of less immediate importance when one is thrashing about, trying to get a new design to simply work. However, as a design is refined and improved, the life cycle of the system deserves serious consideration.

Engineering graduates should also understand that, during the course of their careers, they will likely run into financially-focused managers who will tell them to put off maintenance, or goal-driven managers who may ask them to run systems outside of specification. There are sometimes quite legitimate reasons for doing such things, and the political clout to countermand such decisions is frequently beyond the engineer’s reach. But they should attempt, to the best of their abilities, to reconstruct the agreements and understandings that constrained the original design work. In a perfect world, this should not have to be done. But it usually falls to an engineer to deal with the physical consequences that result from such managerial decisions. So engineers should learn early on that, sooner or later, someone will have to go out and rebuild the sandcastle.

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