ALD Vacuum’s EXPERT GRID – Smart Furnace Monitoring in Practice 

Hello, dear friends of IoT, and welcome to this 15-minute short episode of the IoT Use Case Podcast – the channel for real-world IoT applications in industrial practice.
I’m your host, Madeleine Mickeleit. My background is in mechanical engineering and IoT business development. As you know, I love to focus on IoT applications that are already working in practice.
That’s why I’ve invited a guest today from the machinery and metal processing industry, based in Hanau near Frankfurt, Germany. This market segment has huge potential when it comes to reducing costs and creating real value with IoT.
So what kind of value exactly? You’re about to find out.
Joining me today is Frederic Schum, Product Manager for Digital Solutions at ALD Vacuum Technologies. He’ll walk us through how their customers are using IoT in real-world projects, which use cases they’re tackling, and what to keep in mind when starting a similar initiative.
You’ll find all implementation info and contact links as always in the show notes and on iotusecase.com. Let’s dive in!

Hi Frederic, great to have you on the podcast. How are you today?

Frederic

Hi Madeleine, thanks for the introduction. Nice to hear and see you again! I’m doing great, thank you. How are you?

Nice, I’m good too. I’m just checking when we last recorded an episode, since you said “again.”
It was episode 133, but it’s been a while. Really nice to have you back!
So Frederic, you’re the Product Manager Digital Solutions at ALD. In your role, you’re driving digital transformation within your company. With a background in industrial and mechanical engineering, and years of experience in automation and industrial IT, you’ve been shaping ALD’s digital product portfolio from day one, right?
It’s great to have you here today with some fresh insights.
Let’s start with a personal question: What excites you most about your work at ALD?
And have you had any – I don’t know if you say that in English – any “aha moments” in your experience working with customers?

Frederic

Yeah, definitely, Madeleine. Thanks for the summary.
As you said, my background is in mechanical engineering. I finished my studies a few years ago, and I’ve now been at ALD for seven years in two months.
Right after completing my master’s degree, I started working at ALD. I was the first employee in a newly established department called Automation and Industrial IT.
The idea was to build up a new group within this rather conservative market, focused on automation and digitalization solutions.
I was responsible for business development – building up the new area, creating products, and designing a service and product portfolio.
After launching the first customer projects, ALD quickly recognized how important this topic was. The team kept growing.
For the past two and a half years, I’ve been working in the sales department. We created a new division within sales, alongside powder atomization plants, coating plants, and melting and remelting plants. This new division is called Digital Solutions.
Since then, I’ve been – you could say – the area manager, because this digitalization department keeps growing.

I see. Cool. So, what are we focusing on today?

Frederic

Let’s talk about a specific part of process monitoring: the so-called Golden Batch functionality.

What does that mean in practice with your customers?
Your core business is, of course, in the steel industry, and you’re building these big machines that operate directly on the customer side.
So, what does Golden Batch mean in your day-to-day work with customers?

Frederic

Golden Batch means comparing current process parameters – basically, live trending – with a defined target value.
We call this the “golden curve” or “golden batch” or “golden process.”
This golden curve can be determined automatically by the application based on statistical comparisons from previous high-quality batches.
Alternatively, the user can define upper and lower limits for each process parameter manually.

I see. When I visit your website, I see various types of machines.
Can you explain a bit more about the kind of machines we’re talking about here?
And what exactly does the melting process involve?

Frederic

We can use this application for nearly all of our furnaces.
We already discussed this in our podcast last year – the process monitoring and Golden Batch functionality can also be used for our coating furnaces.
But today, let’s focus on the melting and remelting sector.
Here, we’re talking about so-called VAR furnaces. In these VAR furnaces, the process involves melting an ingot. This ingot is a pure, cylindrical material – typically about half a meter in diameter and six to eight meters long.
As you can imagine, the melting process takes time – sometimes eight hours, sometimes almost two full days.
And that’s exactly why it’s so important to monitor the process parameters against the benchmark curve over the entire duration.
We’re dealing with critical parameters like voltage, melting rate, and others.
It’s important to track them continuously in order to generate an automatic report at the end that reflects the process performance.

Okay.
Can you explain how your customers currently handle this without your system and why that’s a problem?

Frederic

Our customers, as I mentioned, are producing these ingots during the melting process – which takes a long time.
In the end, they get a large cylindrical ingot.
They use our furnaces with a pre-selected recipe, where all process parameters are predefined.
Then, they start the process – and that’s it.
Currently, they have no system in place to automatically track these process parameters over time.
They also don’t have automated quality control – at least not in detail.
Don’t get me wrong – some basic quality checks are programmed into the furnace itself.
But there’s no automatically generated quality report.

I see. Some listeners might now be thinking about MES systems – systems where they’d say, “Hey, I already have some data there.”
So why is it important to not only store the data in those systems, but to actually do more with it?
Can you explain a bit where your customers are losing time or money by not using that data more effectively?

Frederic

You bring up a really good point.
In recent years, our customers have had increasing requirements when it comes to integrating Level 2 or Level 3 systems – especially MES systems. And that’s exactly where our application comes in.
We receive the order from the MES system.
Let’s take the melting example: we get an ingot ID, the alloy that’s to be produced, the work order – all this information comes from the ERP or MES system.
This production data is then linked to our ALD EXPERT system.
The furnace starts production just like it would without ALD EXPERT, but all the process parameters that are tracked over time via the Golden Batch functionality are now linked to this data – including the ingot ID and everything else – and are reported back to the MES or ERP system.

I see. And with this data, I could also do more in the next step, right?
You mentioned the Golden Batch. But I could also access historical data, for example, or analyze failures in an eight-hour process?

Frederic

Yes, absolutely. We can always go back, select a specific recipe, melt name, or batch ID, and retrieve all the data from past runs.
The big strength of this application is how it links all the data together – recipe-related, batch-related, time-related – and creates an automatically generated report with all relevant process parameters.

Okay. How do you handle technical challenges?
Do you often hear from customers, “I’ve got an old control system, and I can’t access the data”?

Frederic

Absolutely. We sell nearly 70 furnaces per year, so you can imagine – we have a huge installed base around the world.
I think we’re talking about more than 5,000 furnaces in operation, and many of them are 30 years old or even older.
We have two ways of connecting to them:
In some cases, we read data from the OIP – the Operator Interface Panel. This is the classic furnace control system that operators use.
Data there is stored in CSV or so-called log and dot files. We read that data using our own log file parsers.
This works well for data visualization, live trending, and so on.
But when you need high-frequency data – for example, to monitor drive systems – it depends on the use case and customer requirements. In those cases, we connect directly to the PLC.
There are newer PLCs on the market.
As you know, in the automotive sector you’ll often find Siemens PLCs, which already have OPC UA. But we still have many furnaces that are 20 or 30 years old, as I mentioned.
Globally, customers use a wide variety of systems – some have Mitsubishi PLCs, others Rockwell.
So we sometimes need specially programmed adapters to connect directly to the PLC.
Sometimes we use preconfigured solutions like Kepware from Rockwell Automation; other times we build custom connectors ourselves. It depends on the use case.

Got it. I’ll include your contact info in the show notes.
And please, feel free to reach out to Frederic directly – because best-practice sharing is key to learning and growing with these types of projects.
We also have our own community where you can share insights with other users.
Frederic, I’m guessing you’re open to best-practice exchange?

Frederic

Definitely. You can connect with me via LinkedIn, or Madeleine can help make the introduction.
I’m always open to conversations about use cases – how we’ve solved them in the past – and I’m happy to discuss new challenges as well.

Very cool. So, to wrap up, I’ve got two questions about ALD EXPERT, since you’ve already mentioned the solution earlier.
I’d love to understand a bit more:
What exactly is ALD EXPERT, and what kind of software modules come with it?

Frederic

Okay, so with ALD EXPERT, we offer a wide range of different solutions for ALD’s core business areas: vacuum metallurgy and heat treatment.
The first goal is always to implement the platform – the so-called ALD EXPERT GRID.
We need to connect all installed equipment on the customer’s production side. This includes ALD equipment and also third-party systems.
That means making a connection either directly to the operator interface computer or to the PLC, extracting the data, and making it available on the platform.
The EXPERT GRID platform provides all the necessary IT infrastructure:
Installed databases, user authentication is set up – usually via a direct connection to the customer’s LDAP system – so users can simply log in with their regular Windows credentials.
Once the platform is in place and all the necessary IT setup is secured, we can add different types of software modules, depending on customer requirements.
These modules can include live process data visualization, condition monitoring of plant components, process monitoring with statistical comparison, and of course, the Golden Batch functionality that we discussed earlier.
And as you know, Madeleine, from previous conversations:
This also includes what we call the AOS, Advanced Observation System, a camera-based system to monitor the evaporation process of the melt pool, combined with an intelligent algorithm.
So yes, we offer a lot of software modules, always tailored to customer needs.

How do you now define the Golden Batch with this solution – as a kind of ideal reference for future production runs?
And how do you handle the data analytics behind it? Because you bring in your expertise, but I’m sure the customer does too. So how do you do that, exactly?

Frederic

Visualizing process data from the PLC is not the challenge. That part is easy.
When we talk about process monitoring and the Golden Batch, we use our own expertise to define an upper and lower limit over time.
Alternatively, the customer can tell us which batches had good quality.
From there, our application automatically calculates a mean value based on those historical batches. Then we calculate the standard deviation from that mean, and define the upper and lower limits for the current process values accordingly.

Okay, that’s cool.
So for that, all I need is the ALD EXPERT GRID as a platform.
I’ll put the product link in the show notes so everyone can check it out.
And, as mentioned – feel free to share best practices!
Frederic, last question for today: What can we expect from you going forward? Are you leaning into the AI hype? What’s planned for this year or next?

Frederic

Well, we already have our AOS, the Advanced Observation System, with cameras installed to monitor the process.
It’s supported by an intelligent algorithm that detects anomalies and displays them to the operator.
I don’t want to reveal too much yet, but we’re currently working on expanding that algorithm.
The goal is to develop an assistant that actively supports the operator – for example, suggesting: “Please make this adjustment based on the observed video data.”
And yes, I believe that’s going to be one of the first truly meaningful AI use cases in our specific segment.

That sounds exciting.
Thanks, Frederic, for this session. I really appreciate your time.
And to everyone listening – thanks for tuning in.
Have a great week, and don’t forget to subscribe to the podcast!

Frederic

My final word – feel free to reach out via LinkedIn or give me a call. Or connect through Madeleine, she’s great at building bridges.
I’d be happy to join another episode – maybe to talk more about the camera system.
Thanks again, Madeleine. Take care. Bye-bye!

Thank you – bye-bye!