What is the difference between classic and ML-based visual quality control?
Classic systems are given rules to recognise defects. A ML model, on the other hand, learns from examples whether a part is OK or NOK. In this learning process, the model derives implicit rules that do not require explicit programming (e.g. "NOK, if scratch on top left"). Machine Learning allows Maddox AI to automate even very complex use cases, such as surface recognition.
As a user, can I use Maddox AI without a technical or artificial intelligence background?
Yes, Maddox AI's software is designed in such a way that everybody can train AI models and put them into use after an initial thirty-minute training session.
How much does Maddox AI cost?
This depends on the specific use case. Before we start with a Maddox AI project, we show you transparently how much the use of Maddox AI would cost. Based on this, you can calculate your business case.
Can Maddox AI reliably inspect even at very high production speeds (e.g. several parts per second)?
In the vast majority of cases, high production speed is not a problem. Our models run on our industrial PC at your site, which means that the AI model analyses images within milliseconds. In our experience, it is the part handling rather than the AI evaluation that becomes the possible automation bottleneck.
How long does the implementation process of the Maddox AI system take?
This depends very much on how quickly we get enough data (pictures of OK/NOK parts). Since Maddox AI is an AI system, data is very important as a basis. We therefore always ask our customers to collect bad parts in advance. We can usually record these parts directly on the installation day and ideally already have enough data to train the model. In this case, the implementation only takes a few days. If we need to collect more data, the system can also run for a few weeks before the implementation is completed.
How much data (=annotated images) does Maddox AI need?
The important thing is not necessarily the amount of data, but its diversity. This means that different appearances of error types are relevant for a good and representative sample of defecets. We do not need many OK images, as the diversity in these images is usually smaller than for NOK images. Accordingly, our focus is on images with defects. We can give you the following rough rule of thumb: In 90% of the cases, 50 images per task (e.g. 50 NOK images of a defect class) are sufficient. In 10% of the cases, another 50 images (i.e. 100 in total) are needed to reliably adapt the Maddox AI system to your use case.
Can Maddox AI integrate with my software systems (MES, PLC, Data-Lake, etc.)?
Yes, this is not a problem. Maddox AI makes the results of the AI available via several protocols that are common in the industry, such as OPC-UA, REST API, MQTT, Modbus TCP or simple digital I/O.
Does Maddox AI make sure that there are no people visable in the images it takes?
Yes, Maddox AI places great emphasis on this. The Maddox AI system provides the ability to recognise people in the images to blur or black them out before uploading them to the cloud. No one has access to the people's identities - not even we can recover these images.
Will I receive new software updates regularly over time?
Yes, Maddox AI is a classic SaaS (software as service) solution. Accordingly, you will receive software updates regularly and free of charge.
Do I have to pay separately for software updates, maintenance and other after-sales services?
No, the software updates, maintenance and other after-sales services are included in the licence fee.