The Plot Nobody Could Defend
A few years ago, I sat in a design review where a young engineer presented a beautifully rendered stress plot of a machined part. The mesh was fine, the colors were clean, and the report was polished. The peak von Mises stress sat comfortably below the allowable, and the margin of safety was positive.
I asked one question: “Before you ran the model, what stress did you expect?”
Silence. Not because the engineer was lazy or incompetent. Quite the opposite: bright, motivated, and fluent in the software. They had simply never been asked to predict anything. The model was the prediction.
When I sketched the load path on the whiteboard and ran a quick beam calculation, the hand estimate came out roughly three times higher than the FEA result. The boundary conditions were wrong. The part had been fully fixed at a location where, in reality, it could rotate. That part had already passed two internal checks before it reached me.
So here is the position I want to put in front of you, knowing that many of you will disagree, some of you strongly:
No engineer should be allowed to run a finite element model until they can estimate the answer by hand within a reasonable margin.
Not “understand the theory.” Not “have taken a course.” Predict the answer. Write down a number before pressing Solve, and be able to explain why the software agrees or disagrees with it.
I am not saying this to protect senior engineers’ territory, and I am not nostalgic for slide rules. I have spent more than 25 years in aerospace structural analysis, and the last several years training more than 1,000 engineers from 50 countries. The pattern I see is consistent, and it worries me more every year.
Software First, Physics Later
Most engineering graduates today meet finite element software before they have developed any real intuition for how structures carry load. The reasons are understandable. Universities are under pressure to teach the tools that appear in job postings. Students can learn meshing, element menus, and contour plots in a single semester, while learning to see a load path takes years.
Hiring reinforces the same pattern. Job ads list software names. I have never seen one that asks for “can estimate the bending stress in a cantilever in five minutes.” So a new engineer’s value is measured by software fluency, and that is what they optimize for.
The outcome is a dangerous combination: high confidence and low verification ability.
Here is the uncomfortable truth about FEA. The software never refuses to give you an answer. Apply the wrong constraints, mix your units, forget a load case, and it will still return a converged, color-coded result. A wrong plot looks exactly as authoritative as a right one. FEA output carries no built-in credibility signal.
The only credibility check that exists is the analyst’s independent expectation. Without one, the analyst is not analyzing. They are transcribing.
Think about the errors that reach reviews in your own organization:
• Supports that are over-constrained and stiffen the structure artificially
• Point loads applied to a single node, with the resulting singularity reported as the peak stress
• Shell thicknesses or material properties entered in the wrong units
• Reaction forces that were never summed and compared to the applied loads
Every one of these would be caught by a ten-minute hand estimate made before the model was solved. Not after, when the plot has already shaped everyone’s expectations.
Prediction Is the Analysis
We need to change how we think about what FEA is for. It is not a machine for discovering answers. It is a tool for refining and verifying a hypothesis you already hold.
Every engineer learned the scientific method: hypothesis first, experiment second. A model solved without a prediction is an experiment without a hypothesis. You cannot be surprised by the result, which means you cannot learn anything from it.
When your hand calculation and your FEA agree within a reasonable margin, you gain confidence in both. When they disagree, that disagreement is the single most valuable piece of information in the entire analysis. Either your mental model of the structure is wrong, and you learn physics, or your finite element model is wrong, and you find the error. Both outcomes make you a better engineer. Without a prediction, neither happens.
Let me be precise about “reasonable margin,” because this is where critics will push back first. I am not asking anyone to hand-calculate a stress concentration at a fillet or the load distribution in a complex bolted joint. That is exactly what FEA is for. I am asking for the global behavior:
• The reaction forces and where they go
• The order of magnitude and location of the maximum displacement
• The nominal stress in the primary load-carrying members
• The way load flows from where it is applied to where it is reacted
If you cannot estimate the nominal stress, you have no way to judge whether the peak stress in your plot is plausible. And if you cannot judge that, you should not be the one signing the report.
Experienced analysts do this almost unconsciously. It is what we usually call “engineering judgment,” and it is often described as something mysterious that only comes with gray hair. It is not mysterious. Engineering judgment is simply a large collection of predictions that were checked against results. Which means it can be trained, deliberately, from day one.
“Isn’t This Just Gatekeeping?”
I know the objections, because I have heard every one of them in workshops and coaching sessions. Let me take them head on.
“This is gatekeeping.” Yes, it is a gate. Every profession where mistakes carry real consequences has gates. The question is what the gate is based on. A gate based on seniority, title, or years served is unfair. A gate based on demonstrated competence, one that a motivated engineer can pass in a few months, is the opposite. It gives juniors a clear, objective path instead of waiting for someone senior to decide they are “ready.”
“Universities don’t have time for this.” Universities don’t have time not to do this. Button sequences can be learned from a video in an afternoon, and every software vendor offers tutorials. What only a university can build is physical intuition. Every lecture hour spent navigating menus instead of drawing free body diagrams and tracing load paths is a misallocation of the most valuable teaching time an engineer will ever get.
“We need people who are productive on day one.” Productive at what? Producing results that someone else must recheck? Or, worse, results that nobody rechecks? The cost of one undetected error in a flight-critical part, a pressure vessel, or a lifting structure dwarfs the cost of a few months of structured onboarding. Speed without verification is not productivity. It is deferred risk.
“AI will soon set up the models for us.” This is the strongest argument for my position, not against it. As model setup becomes automated, the only human contribution left is judging whether the result makes sense. An engineer who cannot predict the answer cannot supervise an AI either. They simply add a second layer of unverified confidence on top of the first.
“How am I supposed to learn if I can’t touch the software?” Touch it all you want. Build models, break them, experiment. The restriction applies to results that feed decisions: design reviews, stress reports, and sign-offs. Learning is unrestricted. Accountability is earned.
Earn Your License: A Progression You Can Use Tomorrow
Criticism is easy. Here is what I recommend instead, and what I have seen work in teams of every size. Think of it as a license that engineers earn by demonstrating competence, not by accumulating years.
1. Level 0: Predict on paper. Work through a set of classic cases: a cantilever beam, a plate with a hole, a thin-walled pressure vessel, a simple frame, a single-shear bolted joint. For each one, estimate reactions, maximum displacement, and nominal stress by hand. The target is agreement within about 20 percent of a reference solution.
2. Level 1: Shadow models. Build finite element models of those same cases and compare them to your own predictions. For every discrepancy, write one paragraph explaining its source. None of this work leaves the team. Its only purpose is calibration.
3. Level 2: Prediction first on real work. For every production model, submit a one-page prediction sheet before solving: the expected load path, reaction forces, location and magnitude of maximum displacement, and nominal stress in the critical members. The reviewer checks the prediction sheet as carefully as the results.
4. Level 3: Independent analyst. After a consistent track record, meaning several consecutive models where predictions and results were reconciled and every discrepancy was correctly explained, the engineer signs off their own work under normal peer review.
5. Level 4: Reviewer. Only now does the engineer review the work of others. And most of that review boils down to one question: “What did you expect, and why?”
Two points matter here. First, the progression is based on competence, not calendar time. Some engineers reach Level 3 in six months. Others need two years. Both are fine. Second, it costs almost nothing to implement. The core tool is a single-page prediction template. The return is a team that catches its own errors before they reach a review, a customer, or a certification authority.
The Software Is Not the Problem
Let me be clear about one thing. I love these tools. Finite element analysis has transformed what engineers can design, and I have built a career on it. The problem is not the software. The problem is the sequence.
Software amplifies whatever judgment you bring to it. Bring strong physical intuition, and it makes you extraordinarily capable. Bring none, and it amplifies nothing, very convincingly, in beautiful colors.
This is the principle behind everything we teach at FEA Academy: fundamentals first, software second, judgment always. If you are curious where you stand today, the FEA Competence Diagnostic on fea-academy.com asks exactly the kinds of questions I described above. It takes a few minutes, and many engineers tell me the result surprised them.
Now I want to hear from you, especially if you disagree:
• Junior engineers: Is this fair, or does it feel like the profession pulling up the ladder behind itself? Tell me where I am wrong.
• Managers and team leads: Would you implement a prediction-first rule on your team? What would stop you?
• Educators: What would you remove from your curriculum to make room for this?
• Senior analysts: Be honest. When was the last time you wrote down a prediction before pressing Solve?
Leave a comment below. I read every one, and the strongest arguments on both sides will shape a follow-up piece.



It’s going to be interesting when the inflection point Anthropic worries about comes to pass. Right now human + AI leads to better outcomes than humans alone or AI alone
What happens to accountability and liability when an inflection point occurs, when human in the loop causes more negative outcomes, more errors and is statistically more dangerous/error prone.
This perspective only lasts as long as we are in the Goldilocks zone of human + AI collaboration. And their concerns are warranted, how will society be able to deal with notions like liability and responsibility when the day comes that human in the loop itself becomes a liability and the main source of error. Right now humans do need to error check AI, soon that process of error checking will be where the majority of errors are introduced due to the human element
This echos concerns I have been discussing several times this week.