AI in Design: Coming Full Circle

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As a Generation X engineer, I've watched design processes evolve from manual drafting kits and hand-derived equations to sophisticated CAD systems powered by artificial intelligence. What fascinates me most isn't the replacement of skills but their enhancement. The engineering fundamentals I learned decades ago haven't become obsolete. They've become more powerful when paired with AI and machine learning tools.

Today's design engineers have unprecedented autonomy. Tasks that once required specialized computing power and expertise are now accessible through AI-enhanced software. This democratization of advanced capabilities doesn't diminish the value of engineering judgment; it amplifies it. Understanding the underlying principles remains crucial for effectively leveraging these powerful tools.

If you're feeling overwhelmed by the pace of technological change in engineering design, start with foundational machine learning and AI prompts. Take a course that is an overview of how it works (I doubt you'll need to learn to code). From there, focus on applications most relevant to your work. Start with the software tools you already use for engineering. Is there an option to enhance it? Critically think about the assumptions and models it's using and always evaluate the result.

Text me about how you're incorporating AI into your design processes or what concerns you have about adopting these new tools. 

Visit the podcast blog for a graphic about AI hierarchy, ideas of how to use AI in different steps of product development, and my extra thoughts about this topic. 

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About me
Dianna Deeney is a quality advocate for product development with over 25 years of experience in manufacturing. She is president of Deeney Enterprises, LLC, which helps organizations optimize their engineering processes and team perform...

Episode Transcript

Dianna Deeney (00:00):
Hello, I'm Diana Deeney and this is the Quality
During Design podcast.
I was scrolling through socialmedia admit it, you do it too
and I came across a post or alink to an opinion article about
Generation X.
I'm Generation X and the postwas sort of like this Generation
X is angry because we grew upin one era, learning the skills

(00:24):
that we needed in one era ofhumanity, and now we are leading
in a completely different eraand that the skills that we've
learned earlier to apply to ourjobs and workspaces just don't
apply anymore.
I kind of got a little bit of achuckle out of it and then I
moved on and kept scrolling, butI kept coming back to that and

(00:46):
thinking about that.
We are in a different era, verydifferent from the one that I
grew up in, but I don't thinkthat the skills that I learned
when I was younger and earlierin my career are displaced.
I think instead, we're comingfull circle.
Ai machine learning is givingus more autonomy with design.

(01:07):
Let me explain more about thisafter this brief introduction.
Hello and welcome to QualityDuring Design, the place to use
quality thinking to createproducts.
Others love for less.
I'm your host, diana Deeney.
I'm a senior level qualityprofessional and engineer with
over 20 years of experience inmanufacturing and design.

(01:28):
I consult with businesses andcoach individuals and how to
apply quality during design totheir processes.
Listen in and then join us.
Visit qualityduringdesigncom.
Welcome back.
We're talking about engineering, design, designing products and
how AI and machine learning isaffecting those processes, but I

(01:51):
view these changes as a cycle.
We're coming full circle backaround to somewhere we've been
before, except now we have moretools and power to be able to do
additional things that maybe wewouldn't have been able to do
by ourselves.
A very relatable cycle thatrecently happened is with music.

(02:12):
This was another post about.
Why Generation X seemed soangry is because we've had to
purchase our music over and overagain.
Now my consumption of musicstarted with radio and then
records.
I remember having MichaelJackson's Thriller record album
my sister bought it waslistening to it on our record

(02:34):
player at home.
Then we moved to cassette tapesand we had Walkmans.
Then it evolved to CDs and Iremember listening to my first
CD.
Again, my sister introduced meto that and it was Madonna in
her car and it was so crystalclear and vibrant.
It was amazing.

(02:55):
At some point I had a 200 or 300CD Sony device where I would
load all my CDs into it.
I could just randomly play frommy whole library of CDs that I
had.
That's how many CDs I had atone point.
But then I needed to digitizemy library to be able to put it
on an iPod.
And then I went to streaming,where I didn't need my library

(03:19):
anymore and sometimes I had tobuy the album again.
And now just this last winter Igot my kids a record player
because that's what they wanted.
They wanted a record player.
And now, just yesterday, whenmy youngest was digging through
a closet and found my old AM FMradio and said hey, can I use
this and plug it in?

(03:39):
I wanna listen to the radio.
So it just seems like all ofour music, the way that we
consume music, is foreverchanged.
We all do it a little bitdifferently, but it's kind of
coming back full circle again.
What was old is new again.
What was old is still relevantand still wanted, and I kind of

(04:00):
relate that to this iteration ofdesign, engineering and
engineering development thatwe're going through.
When I was in high school I waslearning Fortran and Pascal, but
then when I got to collegethose weren't the things anymore
Now it was Visual C++.
I remember in college I wouldwake up from dreaming about

(04:20):
deriving equations by hand andthen, as I progressed in my
career, finite element analysiscame to a point where it was
accessible to people in industry.
But it wasn't that we could doit in house.
We had to send it out.
A specialist had to have thecomputing power, the programming

(04:43):
capabilities and just theknowledge of the program to be
able to run a simulation forfinite element analysis.
It was something we had to farmout.
We couldn't do it ourselves.
We think of computer-aideddesign software.
Again, when I was in college Imight be aging myself here, but
part of the graphic design is wehad a kit where we would

(05:05):
manually draw out graphics ofthings, and CAD software has
come a long way since then, notjust being able to draw but also
testing form, fit and function.
Those started out withspecialists needing to be able
to do that to more accessible toa lot more people.
And then the reliabilitysoftware.

(05:28):
Well, just reliability analysesused to be really difficult
until computing power and thenstatisticians developed some
software solutions for that thatmade it more accessible to
people, packages for statisticalcomputing and graphics.

(05:49):
I never got into it because Ididn't want to learn a new
programming language.
But now, now I can use AI toprogram my own package or I can
check someone else's package forsome of those important
statistical assumptions.
You know things like normalityand independence, that kind of
thing assumptions.

(06:11):
You know things like normalityand independence, that kind of
thing.
Now, going back to the CAD andfinite element analysis, now
there's AI and generativealgorithms can optimize designs
for weight, strength andmanufacturability.
That whole topologyoptimization.
It's a whole specificgenerative AI focused on finding
the optimal materialdistribution within a given

(06:32):
defined space.
Now our CAD systems arebuilding these kind of analyses
into them.
Now we no longer have to farmit out to a specialist.
Same with things likesimulation and analyses.
Building out simulations can bea big deal.
Ai can make it easier for us todefine those surrogate models.

(06:57):
I think AI and machine learningis making these more advanced
analyses more accessible for therest of us, which is why I
think it's giving us moreautonomy with design.
It's not that the skill setsthat we've been learning or that
we used to apply are no longerapplicable.
Now they could be a little morepowerful because we have tools

(07:21):
to help us do it.
In fact, having the baselineknowledge of how these things
fundamentally work and howthey're supposed to work, the
kind of analyses that they'resupposed to work, the kind of
analyses that we're supposed todo, the things that are
important, that's reallyimportant inputs into using
these tools to create these newoutputs that we can evaluate our

(07:44):
designs with.
It's sort of like thestatistical software where, yes,
you can use statisticalsoftware to create an answer and
graph and plot all of thesethings, but you still need to
understand what it is you'relooking at and you still need to
verify the assumptions of youranalysis to make sure that it's

(08:04):
accurate.
The statistical software canhelp you do it faster and that's
how I'm seeing AI and machinelearning for other design
engineering applications.
On the manufacturing shop floor,computer vision for inspection
has been around for a long timeNow.

(08:24):
Can we use AI to automate thosetasks?
Cnns or convolutional neuralnetworks are a subset of machine
learning.
Can they help us process imagesbetter and do a better job at
this computer vision and forinspection and do a better job
at this computer vision andfore-inspection?
Still on the manufacturing shopfloor, predictive maintenance

(08:45):
is a really hot topic right nowUsing AI to analyze sensor data,
to predict equipment failuresand optimize maintenance
schedules.
More on the design side ofthings, with AI we can create
our own scripts to be able to doshortcuts.
With a little bit of thatprogram language that we

(09:05):
developed, we can reapply it ina new way to make things easier
a little bit faster.
But where do we even start Now?
I know machine learning and AIhas been around for a while.
It's not like these are brandnew they just came out last year
but they are being more andmore integrated with the tools

(09:27):
that we use every day.
So if we're feeling behind onall of these advancements and
are not sure what to develop forour design engineering career,
I think a beginning step is totake a foundational course in
machine learning.
The course that comes up a lotfor me is in Coursera and it's

(09:50):
called Machine Learning byAndrew Ng and that's spelled N-G
.
Machine learning is thefoundation of a lot of these
different techniques that we canuse for design engineering.
But then, after that generalbaseline knowledge, we need to
get a little more specific.
What is it that we want to do?
What kind of engineering do wewant to explore that will allow

(10:13):
us to dive deeper into specificAI applications that are going
to be relevant for the work thatwe're doing In the world of
design engineering.
I'm really seeing three areaswhere we use this the most.
One is topology Learning howartificial intelligence and
generative algorithms canoptimize designs for weight,

(10:34):
strength and manufacturabilityIn simulation and analysis.
Investigating how surrogatemodels and AI can accelerate the
simulations and improve theaccuracy of our analyses.
And, with material selection,finding the optimal materials
for a design based onperformance requirements.

(10:54):
We've been doing this for awhile, but now we can do it a
little easier and a littlefaster.
Doing this for a while, but nowwe can do it a little easier, a
little faster.
Speaking of doing things alittle bit faster and easier, we
can also start to automate someof our design processes.
Ai can automate our repetitivedesign tasks, freeing us up for
more creative work, and thatinvolves some scripting, api

(11:19):
usage and machine learning to beable to create automated
workflows.
So back to our theme ofeverything old is new.
Again, I would say everythingold has not gone out of style.
We've just developed somedifferent ways to do it.
Ai and machine learning aregiving us more control over
design than ever, if we onlyreach out to take it.

(11:43):
So if this is new to you, thenthink about taking a machine
learning class.
That will give you a betterunderstanding of the inner
workings and how you can applythis to your work.
If you're already using thesetools for your work, let us know
how it's going.
Leave us a comment on the blogor, if you're listening from

(12:04):
your podcast player, send me atext with how it's going and
what you like about it.
If you're resistant to changes,then let me know what is the
resistance about.
What concerns you aboutadopting some AI and machine
learning in your designprocesses?
This has been a production ofDini Enterprises.

(12:25):
Thanks for listening.