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Practical teaching model
Make sensors, machine data, connectivity and dashboards visible and understandable for students and trainees.
Barista | Educational retrofit demo
From an ordinary coffee machine to a smart machine.
The RealTimeBox Barista was developed as a test and teaching model for Koninklijke Metaalunie. We show how an existing, non-smart coffee machine can be digitised with reliable sensors, real-time data and an open data layer. The same principle can be applied as a retrofit to real production machines.
One BoxOne NetworkOne Data Layer
From teaching model to industry
A coffee machine as an example. The possibilities go much further.
We built this Barista as a test model and teaching material for Koninklijke Metaalunie: a recognisable example of how an ordinary, non-smart coffee machine becomes a connected device. The machine is the demonstration; the underlying sensor technology and data architecture are also applicable in real production environments.
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Industrial retrofit
Give existing production machines a digital upgrade without replacing the complete machine.
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Scale and demo models
RealTimeBox can develop compact demonstration set-ups for vocational, higher and practical education.
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Open and expandable
Start with a few stable measurement points and expand the set-up later with a database, analyses and smart functions.
Projectreferentie & lesmateriaal
Developed as a demonstration model for Koninklijke Metaalunie
The RealTimeBox Barista was developed as a test and teaching model to show how an ordinary coffee machine can become a smart device with sensors, data and connectivity. This approach is also interesting for technical education and industrial retrofits.
Space for logoKoninklijke Metaalunie
Koninklijke Metaalunie
Reference: RealTimeBox Barista — educational test and demonstration model.
Add the official logo asimages/metaalunie-logo.png to show it here.Why retrofit?
From traditional machine to intelligent asset
Even for existing production machines, working smarter starts with reliable measurement signals. RealTimeBox provides a scalable data foundation without replacing the complete installation.
Without real-time insight
×Failures often only become visible when production or quality has already been affected.
×Maintenance is performed by interval or feeling, not by condition.
×Process quality is difficult to compare objectively.
×Valuable machine data remains unused.
With RealTimeBox retrofit
✓Real-time monitoring of the key process parameters.
✓Historical data enables deviation detection and future AI-based maintenance advice.
✓Data becomes historically comparable and useful for optimisation.
✓An existing asset gets a digital layer and a longer economic lifetime.
Why RealTimeBox
The same reliable foundation, tailored to this application.
The hardware can be configured to suit each customer. Sensors and devices remain vendor-independent and can easily be replaced or expanded later.
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Retrofit
Keep the existing machine and add only the required sensors and connectivity.
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Live dashboard
Temperature, cycles, usage and deviations immediately visible.
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Predictive insight
Build enough process history to investigate deviations and develop predictive analyses.
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Quick to apply
The same approach is usable for many existing industrial assets.
How it works
From stable sensor signal to a self-learning system.
A good AI application does not start with AI, but with reliable measurements. We collect the data in a structured way, build process history and can then add analyses and self-learning models.
1. Measure reliably
Determine which process and quality parameters are relevant. Connect robust sensors and existing machine signals, and verify measurement quality.
2. Connect signals via RealTimeBox
Collect signals reliably and in real time in one open, vendor-independent data layer, also alongside existing machine controls.
3. Store and compare data
Record measurements and events in a database. Comparing trends, conditions and product quality creates valuable process history.
4. Improve through self-learning
Use enough representative data to recognise deviations, investigate causes and eventually enable self-learning analyses or maintenance advice.
Our vision
What if you could simply talk to your machine in the future?
Imagine product quality slowly declining. Instead of only looking at an alarm or graph, you ask the machine: “Why is the quality of my product getting worse, and where can we solve that?”
A smart assistant could then compare measured values, production results and historical patterns, point out possible causes and suggest targeted investigation or adjustments. Think of temperature deviations, changing cycle times, wear or a sensor value that is slowly drifting.
This is the development direction, not an existing standard function of the Barista test model. First build a stable sensor base and reliable data, then data quality and analysis, and only after that self-learning models and natural interaction with the machine. Recommendations and any adjustments remain testable and under the user’s control.
Partners
Our partners
Netvion and Seeed Studio are partners of RealTimeBox.
From idea to pilot
From educational demo to smart production machine.
Whether it is a scale model for vocational, higher or practical education, or a retrofit on an existing production line: we start with reliable signals, build the database and data layer, and develop the required analyses and intelligence from there.