• deevio

The Path to Automated Industrial Quality Control

Damian Heimel, CEO of deevio, describes the individual stages a company goes through in order to automate its industrial quality control with deevio.


If I had a manufacturing company and wanted to automate my final quality control, what would be the first step I take?

First and foremost, it is important that you, as managing director, production manager or head of quality assurance, think about what exactly the application is, i.e. at which point the quality control is still carried out manually. We don't need to know much more at the beginning. Subsequently, we define what the process has looked like so far. For this purpose, we have a conversation where we answer the following questions: what are the requirements of quality control? What defects need to be detected? Is there already an error list? What cycle time is necessary? How fast do the products go through the inspection? How many parts do I produce? Are these always the same or very different parts? If we realize when answering these questions that there is a high potential for automation, then you have already taken the first step to work with us.


What happens next? What happens until the actual automation?

Once we have come together - via whichever channel - a first conversation takes place in which we talk about the use case. We discuss the questions mentioned earlier in detail and define together how the production volume, the error rate, the diversity and the distribution of the errors as well as the system requirements look like. In addition, you have to decide whether your quality control should be fully automated or whether you prefer a stand-alone solution. At this point it is helpful to already see pictures of good and defective products in order to get to know your specific use case.

Once all questions have been answered, we will always visit the factory. Every factory looks different and needs a solution that is individually tailored to the respective processes. That's why we take a close look on site at how the process is running so far and what needs to be done.

If we want to work together, we start by using the already existing pictures or by taking the first pictures. To do this, we take good and defective products with us and consider which camera and which light are suitable. For this purpose, we also have a large network of machine vision experts who contribute their expertise.

Based on the products and the pictures of the products, we carry out a first feasibility study. The aim of the feasibility study, on the one hand, is to find out which hardware is suitable for your specific case and, on the other hand, whether your use case can be solved well with our software. In this stage, we are already able to train a first deep learning model and deliver results. If these initial results are positive, we proceed to a proof-of-concept phase. We order the required hardware and install it at your factory as a stand-alone solution. In this phase we do not interfere in the production process, instead the system runs independently, and we take pictures of the products on site. The pictures already have the quality they will have later on and we collect them in such a quantity that we are able to train a deep learning model.

Then we start to create the deep learning model. Our team of Data Scientists in our office in Berlin trains a model that is optimized for this particular application. Depending on the application, this process takes 1 to 2 weeks. The software is then placed on our AI-Box – a mini-computer designed for the use of our software – and tested in the factory. An expert from your company will also be present during the test to evaluate our classification. If our system says that this product is defective and belongs into defect category 3, for instance, he evaluates this assessment and agrees with it, or says no, this is actually defect category 2. This feedback makes the model much better and more accurate. We repeat this process until we have a model that operates within an accuracy range of over 99 percent.

And then comes the final factory acceptance test. We'll make an appointment on site to test the model with new images it has never seen before. If everything went well from the first meeting to the factory visit, the feasibility study and the proof-of-concept, this is the starting signal for industrialization. In this step, we work together with system integrators, machine builders and machine vision experts who take care of the hardware and machine control, among other things. Hence, we offer you complete automation. After this last step, the system can be put into operation and runs automatically.


How long does it usually take from the first contact with deevio to the actual automation of the quality control?

There are two use cases: if the company already has a machine vision system and, therefore, already has pictures of the products, we can be very fast. We conduct a feasibility study within a week and carry out the proof-of-concept in 3 to 4 months, because the customer naturally wants to test for a correspondingly long time. Afterwards, we can go directly into industrialization. This takes another 1 to 2 months. All in all, you have the running system in 4 to 5 months. If you don't have any hardware yet and you come into contact with the topic for the first time, we have to order the hardware first, which takes about a month, and then test and calibrate it. In total, it takes 6 to 7 months until your quality control is automated.


How does the transition from manual to automated quality control take place in my production and what do I have to keep in mind?

Above all, we should consider relatively early in the project what the final automation should look like in the end. There are two possibilities: one would be to fully automate. If that is the case, we work with an automation company to fully automate the visual inspections. The second option is to support the people who are responsible for quality control. To do this, we put together a system that does not replace the position but supports it. This system makes a recommendation, but the person working in quality control gets to decide. In this case, the production process does only change slightly. You can imagine it that way: I have the camera next to me, a screen is connected to it, a photo is triggered and I see the picture with the classification good or bad on the screen.


In the next interview we will talk about what happens on the technical level during the process presented here and take a closer look at the role the data plays in it.

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