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The Status Quo in Industrial Quality Control

Updated: Nov 22, 2021

deevio's CEO Damian Heimel talks about the problems of manual processes in the manufacturing industry and the resulting potential for automation in industrial quality control and explains why traditional machine vision is not suited for all applications.

With deevio you have decided to focus on a specific problem in a specific area. Why did you choose industrial quality control?

Europe - and especially Germany - has a large industrial base. As we wanted to use this base, we took a closer look at how production processes work today, how new technologies are used, but also where technologies are not yet used and which processes are still carried out manually. To get an overall picture, we went to factories to see for ourselves on site. We noticed that, despite the existence of new phenomena such as predictive maintenance and digital twin, reality in 2019 looks different and many processes are still manual.

One of the processes we have observed in various factories is manual quality control. We have seen time and again that there are people whose job it is to decide whether a product is good or bad. Considering the quality standards that German companies – in particular small and medium-sized businesses – have, we were surprised by this observation. After all, manual quality control entails several problems.

Can you describe these problems? What are the disadvantages of manual quality control?

On the one hand, manual quality control is extremely inefficient. If, for example, I have to judge a metal part according to its quality, a scratch looks different on a Monday morning than it does on a Friday night. I get tired, the lighting conditions are different, I may be particularly happy or sad and, thus, distracted. The resulting inconsistencies in the assessment of quality are a huge problem.

In addition, it is very difficult for smaller companies in structurally weak areas to find people who still want to do this job. Here in Berlin, in Hamburg, Munich or Cologne, this is not yet so serious, but when we look at the countryside in Saxony or Thuringia for example, things are different. Companies can no longer find anyone who wants to work in quality control because it is very strenuous. It's impossible to concentrate for 8 hours on metal parts. One overlooks mistakes at some point. Once a company has found new employees, the training is also very time-consuming, as the large number of possible defects can only be seen by a trained eye.

We further noticed that there is no documentation of the decision-making processes. If you say that a metal part is broken, two hours later I can no longer understand why you said that. This lack of traceability is another problem and important information is lost which, if properly documented, could be used to continuously improve production processes.

All these aspects indicated that there is a huge potential for new solutions in industrial quality control, especially at the end of the production line. While the processes within the production line are often already automated, at the end of the line there are almost always people who carry out the visual inspection and decide whether a product is good or bad.

Quality control with camera technology and image processing has been in place for some time now. Why is there still manual quality control?

Visual inspections have indeed been carried out with machine vision for several years and the technology works very well in certain applications. Traditional machine vision reaches its limits, however, when there is a high variability of defects, e.g. when scratches on surfaces take on different shapes and colours or occur at different locations on the product. Such a variance can only be covered to a limited extent with traditional machine vision, since the algorithms are written rule-based and, therefore, rules for a scratch, such as the exact location, must be defined in advance. It is precisely with these defects on surfaces that we can see that machine vision systems have a high pseudo error rate. This means that the systems are set "too sharp" and mistakenly indicate good products as defective, which significantly increases the amount of rejects and leads to high follow-up costs in production. Despite the high acquisition costs of such systems, they are ultimately ineffective and worsen the condition rather than drive the desired automation of quality control.

In the next interview Damian explains how deevio solves the problem of manual quality control and compares their solution to traditional image processing.

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