#142 sensXPERT about Deep Learning in Engineering and Manufacturing Industry
16.09.2024 12 min Staffel 5 Episode 99
Zusammenfassung & Show Notes
Exciting discussions with Dr. Phil on industrial machine learning, algorithms, and deep learning in today's podcast episode.
Dr. Phil Gralla and I discussed industrial machine learning, deep learning, and algorithms. We emphasized the importance of understanding the use case and the machines in use before delving into advanced technologies. Not everything that is labeled AI is actually AI. Many other techniques, such as decision trees and support vector machines, are valuable. It's crucial to choose the right approach for specific applications rather than following trends. In traditional industries like composites and plastics, there may be apprehension about adopting AI, but it should be seen as a tool to support human decision-making, not replace it. Utilizing available data can lead to significant efficiency improvements, as demonstrated by a case involving robot utilization.
Dr. Phil emphasized the significance of statistics in decision-making by machines and the diverse tools available for different applications. Applying AI and machine learning in manufacturing is about supporting efficiency, not replacing human workers.
Stay tuned for more insights from our material characterization journey! Overall, the key is to understand the processes and make informed decisions using the available tools and data.
If you want to discuss this in person, then let's meet at JEC Forum DACH 2024 in Stuttgart on October 22-23, 2024 where Dominik Riescher of sensXPERT - Optimizing Plastics Manufacturing will be available for meetings. See Outro for details on JEC Group's format in collaboration with AVK.
Composites Lounge will be there, too.
Dr. Phil Gralla and I discussed industrial machine learning, deep learning, and algorithms. We emphasized the importance of understanding the use case and the machines in use before delving into advanced technologies. Not everything that is labeled AI is actually AI. Many other techniques, such as decision trees and support vector machines, are valuable. It's crucial to choose the right approach for specific applications rather than following trends. In traditional industries like composites and plastics, there may be apprehension about adopting AI, but it should be seen as a tool to support human decision-making, not replace it. Utilizing available data can lead to significant efficiency improvements, as demonstrated by a case involving robot utilization.
Dr. Phil emphasized the significance of statistics in decision-making by machines and the diverse tools available for different applications. Applying AI and machine learning in manufacturing is about supporting efficiency, not replacing human workers.
Stay tuned for more insights from our material characterization journey! Overall, the key is to understand the processes and make informed decisions using the available tools and data.
If you want to discuss this in person, then let's meet at JEC Forum DACH 2024 in Stuttgart on October 22-23, 2024 where Dominik Riescher of sensXPERT - Optimizing Plastics Manufacturing will be available for meetings. See Outro for details on JEC Group's format in collaboration with AVK.
Composites Lounge will be there, too.
Transkript
Deep Dive into Industrial Machine Learning
I'm here to have some deep dive
into deep learning,
decision trees,
and algorithms.
With Dr. Phil,
I had very inspiring talks
the past days
about mathematics, algorithms,
deep learning,
deep machine learning.
Phil, I would like to ask you
to explain our community
before we dive into your
laboratory equipment here.
Because community, you have to know,
we have set up a few things,
and the machines are working right now.
Crystallization / hardening of
the composites takes place
within fifteen minutes.
And we thought we use the
time to talk about algorithms
and machine learning.
Now, Phil, one of the questions is,
I'm reading everywhere
now machine learning,
deep learning,
everything is machine learning,
deep learning,
neural networks.
Tell us, and give us some
insights from an expert perspective:
is everything that we are presented
with deep learning,
really deep learning?
Or is it that people maybe do
some simple decision trees
and just label it AI?
That's a very good topic.
So, most likely not everything that
you hear deep learning is
actually deep learning.
And, we have a saying
here in the company from us,
we say everything is statistics,
and that's also the most
important part about this one.
If we look into how
do machines make decisions,
and that's the core what
we're looking at,
is statistics.
At the end, it is statistics.
And we have different way to do it.
We have very classical things,
decision trees where information is taken
and based on this information,
you either go a or b.
And then you keep going.
You have this one decision.
Okay.
What is the next data you do again?
And, this is already some kind of AI,
but it is not deep learned.
You might make simple steps
deep learning if you want.
The other parts is for vector machines,
here for over 20 years being
used a lot, still.
And they're not
as fancy as deep learning,
but they have their advantages
for not using as much data.
And that's also a point where
Don't get me wrong.
Deep learning is very exciting,
especially for a mathematician,
if I look at Chat GPT,
if I look at Image Generation,
it is cool.
I really like it,
but it might not be
what we need,
especially not if we're working
with data that's very different.
If we look at our cycle data,
if we look at material data,
you cannot go online on GitHub
and just download a sample of
a million annotated data
and start training.
You most likely have to
do it yourself.
And then if I go to a customer,
be it like aviation and tell you,
you have to produce
one hundred wings for a first start,
and I need half a million
to actually train a model.
I'm pretty sure
I'm not gonna sell you
this algorithm anymore.
So there is something
where it is a lot of times
a good decision
to actually take a step back
and look at the whole tool set
that we have.
And there,
we go back to regression models.
We have auto regression models,
with random syncs.
That's something we are using,
especially if you have a little data
for analyzing and making predictions
in the future.
We have some typical decision trees.
We have ensembles
where we use a lot of different
machine learning algorithms
that are all good
in their own way,
and together they
form a good decision.
You can compare this
if you are getting sick,
and you're not only going to
one doctor, but you
have a general doctor,
and he says,
you go better a heart doctor,
and you're gonna go out
to a heart doctor,
and he checks, and he said,
like, yes.
But we need someone
who's a surgery.
So you have to,
they're good to go the next step
to a surgery doctor.
And this is techniques
that we can use in the same way
where we have a more general model
that points us into the
direction which one to take next.
None of them is perfect,
but the combination together makes it.
And that's something where we,
a lot of times,
look at it,
and also we make our own models,
because this is a process we take.
We look into the material,
into the information we have,
and then choose models
that actually are appropriate
for our application and
also feed the requirements.
And that is something
always keep in mind.
We do not need a
model that can tell
us and translate languages
because that's not
what we're doing.
We do not need a model
that can make images.
We are not doing images.
We need a model
that understands composites,
thermoplastics,
and thermosets.
And if it can only do
this one,
I'm happy.
It doesn't have to
talk with me.
It only has to
fulfill a job.
And that's something
to always keep in mind
why I'm very nice
to have a hype,
and it makes my work
much easier talking to customers
if they come to me requesting all this.
But don't be surprised
if it is not deep learning,
but some other technique instead.
Doctor Phil, now the
composites industry and the
plastics industry are very,
very conservative industries.
With conservative,
I mean, they have
capital expenditures,
CapEx investments in plants
and operations and machines.
It could be. I'm just just
throwing this out
of my own observations.
It could be that engineers
that are operating these machines,
setting up these machines.
They are looking for a fast output,
higher output that
they are a bit scared,
challenged with
all these new forms.
I will call it support,
because it will not
take over the job,
then and make the job better,
for the human being.
It will support the operations.
What would be your one tip
for people that are scared about
applying AI deep learning
or even simple decision trees
in their manufacturing?
So one thing is,
like you already pointed out,
the it is a tool
to help you,
and you probably have used tools
that are much closer to
machine learning than you
expected beforehand.
Computers do it all the time,
making decisions,
even though they don't use
deep learning,
And if you have something like,
PID controller inside,
that is already mathematics
making decisions
for you to have this,
like, a control of your machine.
And we are comfortable with this.
And I think that is something
to keep in mind,
your machine is already doing this,
and we just apply it now
on more steps.
And the first thing you should have
is get rid of this idea that
machine learning
AI has to be like a human.
It doesn't have to be,
And if you get rid of this idea
that we're trying to replicate a human,
but instead take it as another tool,
another step like a PID controller,
I think it makes it much easier
to feel comfortable
about what is happening and
also understand why decisions are
not made the same way
as I as a human would make it,
but it's this helping you to
control a very difficult process.
That's, really a great conclusion.
It's the mindset of the engineers
has probably to adapt and understand.
We are not replacing any workforce,
but we are supporting
a more efficient production.
And this is all what sensXPERT
is about their process optimization.
And it helps you understand
the process
because even if you have 20 sensors
and you see exactly what's happening,
it's overwhelming.
It's something that I noticed before,
and people were asking,
why do I take so long
with analyzing
some data and make some nice plots?
And I said,
because I have to extract data,
if you look at what we are collecting,
we have a hundred graphs,
a hundred plots for each cycle.
No human can understand
that easily anymore.
We have to make it relevant,
to make it understandable.
And this is already a tool
which we use without realizing
how much mathematics,
how much of the machine learning is already
behind it to just make sense
of all the information
that is being gathered.
So, Doctor. Phil, I have
one final question
on this small excursion,
which I thank you a lot for
highlighting this
The last question
I'm having for this part is,
I'm assuming,
just assuming,
seeing walking around the
manufacturing processes of
our customers' inspection.
You know,
they invite me to look
at the production,
and I have a lot of insights
And I'm seeing everywhere
machine control rooms.
I guess there's a lot of data
today already available.
Now, if you think that all
this data is available
and nothing happens with it,
tell us
what is the chance or the opportunity
that is missed by not utilizing this data?
That's a very good question.
And I do have an example
about this one,
which is actually not
related to sensXPERT,
but work I'm doing.
And when I was student,
we had a company we're working with,
and they were asking for,
we need a new robot,
which takes very heavy parts
from one machine to the next.
And they had all these information
already when machines are done
when they're ready.
And no one actually
had a clear look at it.
So we started looking into it,
and the first conclusion was,
like, you do not need a second robot.
He wanted one,
but actually a lot of your machines
are stopping not doing something.
So by just more efficiently sorting
where the robot is going
and where he's putting stuff,
we already had a 40%
increase of production.
And then you can talk about,
let's add some robots.
But, that's something where we,
realize that, the efficiency
sometimes is already there,
but it just gets lost.
No one has a whole picture
about the whole process.
Also here we at sensXPERT,
we look at only
the curing of the material,
but there is more steps
involved coming afterwards.
So if you want to
have a whole process optimization,
this is only one step.
And, understanding how machines feed
into each other,
understanding early enough,
when something is happening,
like maybe a part that you have
is not perfect getting.
There's some misfunctioning.
Maybe it breaks early.
Maybe it didn't bind correctly
or it didn't get hard enough.
All of these things
can have many causes.
And some of the stuff you might
already monitor without realizing it.
So it can help to make use
of all these collected data
by applying proper tools.
Wonderful. A nice conclusion.
Thank you so much
for listening to our short episode,
and now we are turning back
to our inspection of materials here,
and I'm really keen to see
what the result is.