#138 Examining with Dr. Phil Gralla on how "Hardware in the Loop" significantly increases data analysis
29.08.2024 40 min Staffel 5 Episode 95
Zusammenfassung & Show Notes
Welcome to our two part episode where Composites Lounge invited Dr Dr. Phil Gralla of sensXPERT - Optimizing Plastics Manufacturing to explain how "Hardware in the Loop" (HIL) increases data analysis.
Ilkay Özkisaoglu found this a fascinating way to utilize data and if you are not familiar with HIL and how your manufacturing of plastics and composites can benefit from it, listen to this episode.
sensXPERT opened its doors end of April 2024 and it took a while to compile this live demo, because we wanted to create the best possible experience, know-how transfer and you to understand the practice of AI, more precisely ML applied to inmold plastics and composites components.
There are also some developments that are contactless, since requirements in trenchless technologies assume a test from a certain distance after installation.
A little more background on HIL to warm you up on this particular episodes, because these are truly technical:
What is hardware-in-the-loop (HIL)?
According to MathWorks.com "HIL (Hardware-in-the-loop) simulation is a technique for validating your control algorithm to be run on a specific target controller by creating a real-time virtual environment that represents the physical system to be controlled. HIL allows you to test the behavior of your control algorithms without physical prototypes.
How does HIL simulation work?
You create and simulate a real-time virtual implementation of physical components—such as a production plant with sensors and actuators—on a target computer.
You run the control algorithm on an embedded controller and run the model of the plant or environment in real time on a target computer connected to the controller. The embedded controller interacts with the plant model simulation through multiple I/O channels.
You refine software representations of your components and gradually replace parts of the system environment with the actual hardware components.
With this approach, HIL simulation can avoid expensive iterations in hardware manufacturing.
Where are HIL simulations used?
HIL simulations are particularly useful when testing your control algorithm on the real physical system would be expensive or dangerous. HIL simulations are commonly used in automotive, aerospace and defense, industrial automation and engineering to test embedded designs.
Examples of commonly used HIL simulations include:
Aerospace and defense: flight simulators and flight dynamics control where it would be too complex to test the control algorithm on the actual aircraft
Automotive: vehicle dynamics and control where it would not make sense to test functionality in the early stages on the road
Industrial automation: plant control testing when stopping production or assembly lines to test control algorithms would mean high resource costs and business losses" (Mathworks.com online accessed 23 Aug 2024, Link in the comments)
Ilkay Özkisaoglu found this a fascinating way to utilize data and if you are not familiar with HIL and how your manufacturing of plastics and composites can benefit from it, listen to this episode.
sensXPERT opened its doors end of April 2024 and it took a while to compile this live demo, because we wanted to create the best possible experience, know-how transfer and you to understand the practice of AI, more precisely ML applied to inmold plastics and composites components.
There are also some developments that are contactless, since requirements in trenchless technologies assume a test from a certain distance after installation.
A little more background on HIL to warm you up on this particular episodes, because these are truly technical:
What is hardware-in-the-loop (HIL)?
According to MathWorks.com "HIL (Hardware-in-the-loop) simulation is a technique for validating your control algorithm to be run on a specific target controller by creating a real-time virtual environment that represents the physical system to be controlled. HIL allows you to test the behavior of your control algorithms without physical prototypes.
How does HIL simulation work?
You create and simulate a real-time virtual implementation of physical components—such as a production plant with sensors and actuators—on a target computer.
You run the control algorithm on an embedded controller and run the model of the plant or environment in real time on a target computer connected to the controller. The embedded controller interacts with the plant model simulation through multiple I/O channels.
You refine software representations of your components and gradually replace parts of the system environment with the actual hardware components.
With this approach, HIL simulation can avoid expensive iterations in hardware manufacturing.
Where are HIL simulations used?
HIL simulations are particularly useful when testing your control algorithm on the real physical system would be expensive or dangerous. HIL simulations are commonly used in automotive, aerospace and defense, industrial automation and engineering to test embedded designs.
Examples of commonly used HIL simulations include:
Aerospace and defense: flight simulators and flight dynamics control where it would be too complex to test the control algorithm on the actual aircraft
Automotive: vehicle dynamics and control where it would not make sense to test functionality in the early stages on the road
Industrial automation: plant control testing when stopping production or assembly lines to test control algorithms would mean high resource costs and business losses" (Mathworks.com online accessed 23 Aug 2024, Link in the comments)
Transkript
Good morning.
I'm here to have fun with sensXPERT
here in Schwanthalerstraße in Munich.
As you know, guys, I've been
with Composites Lounge,
#Composites360onTour my vlogging style.
I've been at Hannover Messe this week.
I've been at TechTextil.
Then I had birthday.
I'm 53 years old and today on the Saturday
I have an expert here
and I would like to interview him
on what's going on with testing materials,
particularly in plastics and composites.
So and if you are watching now
from the United States,
my expert here is Doctor Phil.
But it's not Doctor Phil,
you know, from TV,
who was the psychologist.
This is Dr Phil Gralla
and with Dr Phil Gralla
we will dive now deep
into analyzing systems
and inspection systems.
I'm here in the laboratory.
We have not a polished environment here.
This is a laboratory.
And in the laboratory, as you know,
you have a lot of devices,
a lot of cables,
a lot of electronics here.
You have shelves where parts are into it.
And I love it,
because this is engineering,
and engineering
is the heart of our material science.
So let's deep dive
with
Dr Phil Gralla.
And now we are turning back
to our inspection of materials here.
And I'm really keen
to see what the result is.
-So the first one
is our dielectric sensors
because we are measuring in process
and this one is an example.
Well, actually it is not just an example.
This is a real sensor you can see here.
This is the sensor we are using.
And for the lower frequencies,
especially used for thermosets,
this one will be integrated into the mold.
So it needs contact to the material.
That's a requirement we have.
And for injection molding
we would place this one directly
in the mold as part of the mold.
You can put it in later
after you already finished the mold,
but it needs to be physically altered.
To demonstrate how it works
and what we can see.
We actually go to this one.
This is a laboratory press.
So this is not a real mold.
It's so small.
But we use this one for testing
and analyzing.
And we have.
As we see before, we have our plate.
We have the sensor.
Now we have this ring we put on top of it.
Need to close it a little.
And into this kind of mold because it is,
we can just put our material.
So I will have some epoxy and mix it,
put it inside.
And then we do our isolation.
We close. And then
we start our program.
It will go down, heat a little
and apply some pressure.
And we can see what actually happens
with the material.
And this is a very interesting part,
because if you look we have also a DSC,
a typical laboratory equipment
for testing material.
And this is kind of a golden standard.
A lot of people use it
for analyzing material beforehand.
The problem is
you always need
to take out part of your product,
of your final composite,
grind it very small,
and then you can analyze inside.
So you cannot do it in process only
after or before.
And here we can actually see
in the process.
We have two tools.
One is for controlling the press.
So this is the first one
I will actually start
where we have here always our press.
Not only do it right now.
Later we will start it with a heat
and go down.
And since we switched to our new sensor,
we actually don't see anything here.
There's nothing connected.
This is only for controlling
and for checking what happens,
we use this tool.
It's a developing tool that we have
for our newest kind of sensor.
So this is something
where I'm quite involved
in gathering the data,
displaying the data
to make sense of what is coming out.
And so this is a little tool
we wrote in STL, pure STL in C++.
We didn't use anything
that was already done.
And the reason is
that we get a lot of data,
especially when starting.
So we have a analyzer board
which can already decimate the data,
which is good
because otherwise
we have to stream gigabyte per cycle.
And when we have this one most
of our graphics tools
were not able to keep up.
So we had to make some small adjustments.
That's why we have this tool
to actually be fast enough,
gathering the data and displaying it
with some ring buffers
and mathematical tricks
to make it fast enough
to process all the data coming
in and analyzing it.
We have Fourier transformations
actually on FPGAs directly
to get rid of the noise
to extract the most important data.
And all this one is then being transferred
in CAN interface.
Why CAN? Well, our devices are also
made to go for sewer rehabilitation.
And in sewer rehabilitation,
we have cable lengths
of around 300m and temperature
around 120 to 140.
So we cannot work
with a regular network cable.
We can also not use any
like Wi-Fi controlled elements.
That's why we actually use
this CAN technology,
which for some people
that are into cars might know.
That's also how your components in the car
communicate with each other.
All right. So I mixed some epoxy here.
No, actually resin.
And we can fill something in.
Okay. So we fill some of our material
inside here.
And we want to see the curing.
So what is actually happening
whiile it cures.
So we'll close this one for a moment
and we can focus on this machine.
Then I will close this one.
You can stay focused here.
I will start the press.
Okay, the room
has to heat up a little
so it's not inside.
And now we can start.
We already started our press.
It's heating up
and pressing
the
material down to see actually
what happens with the material.
Right now, we don't see anything.
And that would be quite normal
in your process as well.
So what we can measure is of course
some temperature
if you have temperature sensors,
the machine and the pressure itself.
But this does not tell us directly
what happens with the material.
We can use the sensors
that I showed you at the beginning
and to see the viscosity of the material,
actually the ion viscosity.
You have a dielectric field
applied with a frequency,
and we check how our material,
the ions, ionized
and how they align.
Like how do they respond to the frequency.
And this tells us
how the material is curing
and the data is gathered live.
So you actually have the possibility.
The process also
is 15 minutes.
All right. So I showed an example
about our press here -
the
laboratory press.
So this is a very small device
if compared to injection molding.
And for the next step
we actually want to have a look
at the machines that we are using.
our IPCs. So the idea from sensXPERT was
and still is
to bring technology and analysis
from the lab to our real production
and use it in production.
So we can not just use the press
that you have seen,
but instead we have to integrate
our technology
into already existing processes.
And to do that, we have our sensors.
But the second part that is important
is our edge device
or we call it the edge device.
The edge device consists of two parts.
One is actually an analyzer.
So it takes the analog signals
and the signals from the different sensors
to transform them to something we can use.
And the second part is an IPC.
An IPC is a computer basically
without any screen
which does all the processing.
Now you might wonder if you say,
we want to do something in process
and we can show what happens.
How does that work if there is no screen?
And the trick is
you can access this IPC
on the local network.
It has two network connections,
one for local, one for online,
if you want to upload data to the cloud,
which I do recommend
because then we can actually work
with historical data.
You can access this and then basically
like a web page open it.
And there you have
all the information live.
We will show that later
so that you have an idea
of what's happening.
And for everyone who can actually see it,
the lab here is small
like it's for 4 or 5 people.
It's one room,
so there is no mold machine next to me.
But what we have instead is a mini HIL
and a NI-HIL.
What are these?
Well, HIL stands for a hardware
in the loop. And this hardware in the loop
we use to simulate the real machine,
but by actually sending signals.
That's why it's called hardware
in the loop.
So this one sends us trigger signals,
sends us analog signals
that simulate being a real machine.
So our IPC and our analyzer
has no idea that he is somewhere else.
And this one is done for testing.
So we can in here test
different scenarios.
We can also check if there is something
happening at the customer
that we replicate.
What is it doing? And for some of you
who might have been at a fair
and seen measurements and have wondered
that where this one is coming from,
if it's live measurement,
it's probably coming from one of our HILs.
This little blue box
here is our so-called IPC,
which actually is two parts.
On one side is an analyzer.
On the other side we have our IPC
and they come as a box combined,
makes it easier for install.
And our sensors are being connected
to this channel one and two.
So you can have two sensors
to the same IPC.
Either if you would like to have
two measurements at the same part,
or you might have other purposes,
or you can also only use one of them.
Then you see here I talked about it
about before, two LANs.
Why two? One for local access,
if you do not want to have it
in your regular network,
and the other one
we use to have connection to our cloud.
We do use the cloud to upload data,
to retrain our models, to monitor models,
and also to give the option
for every customer to get an overview
about different installations
or one installation over the time.
How is it performing?
How is it performing in comparison?
You can also detect
if things are out of order.
So that is the purpose of cloud
in comparison
to what is happening on this IPC.
And for our IPC
that is already all.
It's the box.
You connect it
and to see what happens inside,
you will need
to take a different computer.
Maybe you have one in your network
or a laptop,
and then you can work on this one
to see what happens.
What I will do to show you
what actually happens inside this box
during a measurement,
we have connected this one
to one of our HILs.
So it is simulating a production
by sending signals
or the box doesn't know,
it's not a real production.
For it, it is in a production
and gets an analog signals.
So it is really
a hardware-made simulation.
And it is gathering this information.
And we can see live what happens.
What can you see and what can you use.
I have a question to you.
The box here.
There's a lot of technology inside it.
But who would be responsible
for this box at your customer.
Is that the IT department?
Is that a production engineer?
Describe me: Who at your customer
will be operating
or setting this up and operating this.
We have to distinguish
between installation and operation.
Prior to the installation,
we have a couple of requirements
that must be met. One is how is this one
connected to a network
and if it has internet access.
Why is the internet access important?
Well, first of all, only this way
we can upload data reliable to the cloud.
But second is, if there is something
that you would like us to check,
we can use internet access
to directly access the box by ourself
and to help you operate this one
and to answer questions.
Now during the production your
IT should not be involved anymore.
So everything is set up.
We have a team from our application
& services helping with the installation
coming to make sure
everything is set up correctly.
When they have done their work
and gave you an introduction,
the person at the machine floor
themself can operate this machine
and the reason is
that you do not have to do much.
So there's only very few buttons.
They're mostly online.
Well, it's not really online looks online
because it's in your web browser,
but you are connected
directly to this computer
and you can check live your measurements,
the relevant data that has been collected.
You can see the status of your sensor,
also of the IPC itself,
and you will get predictions.
And that is actually an interesting topic.
Predictions. Now it's the mathematician
in me coming out.
Just gathering data is not enough.
So we have to make sense of this data.
And a lot of times just knowing
what is happening inside your process
helps you to define your process,
or maybe to make sure
that you are confident
the parts of your producing are correct.
They do not have any issues.
That is good, but the other part is
you can use it
to detect something is out of order.
Something is already not happening
correctly to ideally
have time to react to it.
So you might see we need more time
or more energy to have some more heat.
And the second part is
if you produce parts,
usually you like to bake it
a little bit longer just to make sure.
And how would it be if you now - actually,
we already succeeded
our goal of a certain degree of cure
or a certain TG and we can stop,
we can open our mold now
and already continue.
So this is something where we say
it's a cycle time reduction.
So you can use this technology
to reduce the cycle
and open the mold
when your part is already ready.
That sounds nice.
How long time do we have to react?
We can send a signal trigger ourselves
when it is reached and you are done.
Some processes are not alone.
You do something secondary next steps.
So the earlier you know
that you open the mold
in maybe a few minutes,
depending on the process,
or even if you have a long process
in an hour,
you have time to set up everything else.
This is where prediction is important.
So we use machine learning models
to predict the degree of cure
what is currently the cure,
but also to tell you
how your degree of cure
will look in five minutes
and ten minutes and 40 minutes.
Depending on your process,
we make predictions into the future
where you can see
how your process is behaving
and when it will be ready,
when your part is done
or as done as you defined,
and be ready for the next step.
And this is the beauty
of this predictions,
where now you have the possibility
to really not just open something,
but actually react in time
and also be prepared
what is happening at which time points.
I'm here to have some deep dive
into deep learning,
decision trees and algorithms.
A world that I'm not necessarily
very iterate on, not skillful on.
And with Dr Phil, I had very
inspiring talks the past days
about mathematics,
algorithms, deep learning,
deep machine learning.
And 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 15 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 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?
Yes, 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 at the core,
what we're looking at is statistics.
At the at the end, it is statistics.
And we have different way to do it.
We have very classical things
like 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 part is support vector machines
here for over 20 years
being used a lot still
and they're not as fancy as deep learning,
but they have the 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 ChatGPT,
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 are working with data
that's way 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 100 wings
for a first start,
and I need half a million
to actually train a model,
I'm pretty sure I'm not going
to 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 toolset
that we have.
And there we go back to regression models.
We have auto regression models
with random things.
That's something we are using,
especially if we have little data
for analyzing
and making predictions in the future.
We have the 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 that
if you are getting sick
and you not only going to one doctor,
but you have a general doctor
and he says you go better to heart doctor.
And then you go to the heart doctor
and he checks and he's like, yes,
but we need someone to do the surgery.
So you have to go the next step
to a surgery doctor.
And these 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 why we make our own models,
because this is the approach 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 it can only do this one.
I'm happy it does not have
to talk with me.
It only has to fulfill a job.
And that's something
to always keep in mind.
It's very nice to have a hype
and it makes my work much easier
talking to customers
if they come to me requesting models.
But don't be surprised
if it is not deep learning
but some other technique instead.
-Dr 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 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 of...
I will call it support.
Yeah, 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?
Okay, so one thing is,
like you already pointed out,
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 a PID controller
inside that is already mathematics,
making decisions for you
to have this control of your machine.
And we are comfortable with this,
and I think that is something
that 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 are 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
with what is happening,
and also understand why decisions
are not made the same way
as I as a human would make it.
But it is helping you to control
a very difficult process.
-That's a 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 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
when 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 100 graphs,
100 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, Dr 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 and 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 a student,
we had a company we were 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 the second robot
if you 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 could talk about
let's add some robots.
But that's something where we realize
that the efficiency
sometimes it's already there,
but it just gets lost.
No one has a whole picture
about the whole process.
Also, here 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 perfectly excellent,
there's some mis functioning.
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 here.
You actually have a look
at my own working laptop,
but what we did is: I connected via remote
onto the network
where our blue box is right now.
So this is the box I showed before.
We have our IPC, the blue box,
and it's connected to one of our HILs.
And here we can now see what happens
while we have our measurements by the box
is running what you can actually see.
And this application
that is running in your web browser
when you connect to the IPC
and we always see our signals.
So what is coming.
So now it just switched to a new cycle.
And it will gather something.
We see temperatures.
We can also see the ion viscosity
here called a master curve.
You have to know
when we do the measurements.
It's not just one frequency
we're measuring.
We measure a lot of frequencies
and we only display the one relevant.
Interesting here
we can now see our temperature
how that is starting in the process
which material you have it will change.
The sensor for the temperature
is actually inside our own sensor as well.
So this is additional information
that we capture.
The ion viscosity.
And what will happen
is when this one keeps
running we take the data the live data
and also make a prediction.
At some point,
you will see that here will be
a prediction telling you
how it will look like into the future.
And we have information
about the current measurements,
what is being measured on which channel.
You can select
different measurement methods
if you want to select
which frequencies you have,
what you're looking for.
We have our critical points.
So this is something
if you do not want
to have the whole timeline,
it's certain points
in the timeline that are of value.
That's called the critical points.
The minimum.
You have maximums. You have the change
in the in your derivative.
All this information.
And then also here is of course
this prediction
where we can see when something ends.
This is how you should look like
if everything is set up.
But you have the options to log in
and actually work on all the information.
I can do this one here.
It's already saved.
So you can make changes.
You can see about your hardware.
We have overviews
which hardware is being used.
You can make setups.
You can do calibrations.
We have for the measurements
that we can see what is available
and which signals do we want to catch.
We can also set up
for our measurement methods.
All these kinds of stuff can be done.
Why would you do this one?
Well, you might have more
than one process. So if you use this one
to manufacture different parts,
then you can do use different methods.
If you have different materials,
then it makes sense to adjust
the frequencies we're looking at.
If you have some are more
for lower frequencies,
other ones go a little bit better
with higher frequencies.
That's also where we can help
which frequencies make sense,
which one you should measure.
It depends on the material,
on the cycle time.
And that is two main reasons
why you would change the frequencies.
For this part, you might notice
that I do not start any measurements.
So here we can already see a change
to our laboratory equipment -
that this does not need to be done
manually but instead
we actually get signals from the machine
where we get information
about opening, closing, start of a cycle.
So this one will react accordingly
and by itself.
Then start measurements,
save measurements,
upload the measurements when a cycle ends.
There is the option
to also trigger an end of a measurement.
We call this one an open mold command,
because for most application
it means stop and open the mold
and this one is implemented
so you can even send a signal
if you define beforehand what criteria
you need to match
to actually send this one.
I would like to show one more thing,
and I think
we have to switch to the other side.
All right. So this is it from my side.
We showed some of the machine.
We had to look at our IPC.
You could see some of the real data
being captured. And I have one more thing,
because when you were carefully listening,
you might have noticed I was talking
about frequency,
frequency bands and also being in contact.
Most of these things
apply for thermosets.
That is where we are coming from.
That is the main focus we have.
Our cycle times are long enough
that we can go with this low frequency.
And the question is what
if you work with thermoplastics.
Two things: So one part is
we are too slow, right?
And we might not see enough.
But the good part is
and that's what I would like to show,
is we do have our standard sensor
that I showed already.
And this one is actually modified.
And this is modified in a way
that we use high frequency
or to be more precise, radio frequency.
So we can go into the gigahertz
frequency spectrum,
which allows us to work with processes
that are much faster.
That is especially important
for working with the thermoplastics.
The other part is what it allows us.
And that's the second thing I would show,
is what if you have a process
that actually
cannot be measured in contact,
maybe you have extrusion,
maybe you have some kind of rehabilitation
of sewage systems.
There you cannot get in contact.
And this is where we are working
with a new sensor we are developing.
It's also based on this radio frequency.
And the neat part about this one is
we do not need to be in contact anymore.
We can have air in between
so we can see it looks
a little bit different.
Right? You still have the circular,
but it's two parts
and this one can measure with a distance.
Air can be in between.
And we can now start measuring
different kind of processes
which do not allow to be in contact
for our sensor
with the material in the production.
This one looks very big.
There's different versions of it
under development.
We do have first customers
using it already, and I'm very excited
that this one is progressing
the way it does and hopefully
soon we are a lot more
out of our thermostets,
but also in the thermoplastics.
So while inspecting the lab
and showing Ilkay what we have here,
he came across this little device
and was wondering what it is for.
He guessed correctly
that it is sucking air
and does something with it.
We call it our elephant,
and when here in the lab
we work with different materials
to make sure that we have the safety,
that no one gets poisoned.
We use this elephant that can move around.
You can adjust the size, go up,
down and work with hazardous material
without getting in harm.
Don't be surprised.
I will not do this one.
I do some of the machines,
but most times I hide in my own room
on the other side
and work on the computer.
So when walking through the lab,
we have seen on this wall
some nice pattern
and we were just discussing
what this one is.
This one actually is a real measurement
from an epoxy. And here you can see in red
the temperature
and in the other colors
we can see the frequencies
and ion viscosity.
And we are very proud of this one
as it is looking nice
and gives us a feeling every morning
to show what we are working with.
On the other side, right at the beginning,
there is even a y-axis,
which gives you some indication
that this is a real measurement.
And indeed it is,
so we are very happy to have it here.
Okay,
so now measurements.
Now the time is over.
We also see in the measurement
that nothing is curing anymore.
So we can open up our mold.
Let's go until it's completely open.
So when it is open not moving anymore,
then we can
actually remove our safety glass.
Okay. It should
unlock. And there we are.
Good. Now let's put it all apart
and we can already, well I can see it
already that it is cured.
And when I find the tool.
It must be somewhere.
There it is.
Now we can unscrew it
so that we can
also see
that
it actually did cure.
And is it hot?
Yeah, that does
feel like it.