Deep learning: the “game changer” for automation and production efficiency in industry

Jun 12, 2023

Deep learning will change industry. Machines will perform tasks that require human intelligence. As the digitalization of processes and the capturing of data in companies increases, so too will the use of Deep Learning, which will allow a more effective collaboration between humans and machines. This will revolutionize automation and production and lead to more efficient and precise decision making processes as well as higher productivity while at the same time significantly lowering development costs.

In our “SICKnificant” podcast, we spoke to Dr. Christoph Eichhorn, Strategic Product Manager for Digital Services and Solutions, about the potential of deep learning to relieve humans of tiring tasks and to increase quality.

Dr. Christoph Eichhorn, Strategic Product Manager for Digital Services and Solutions at SICK.
Dr. Christoph Eichhorn, Strategic Product Manager for Digital Services and Solutions at SICK.
Deep learning, as a subset of artificial intelligence and machine learning, has been gaining importance in recent years due to the increased availability of data and computing power. The technology is revolutionizing automation in production and other areas where it is enabling machines to perform tasks that previously required human intelligence. Dr. Christoph Eichhorn, responsible for AI solutions at SICK, explains it this way: “Deep learning is a subset of machine learning. It uses artificial neural networks, so-called “deep neural networks”, that are able to manage even complex situations. They can perform complicated decision making processes, for example in quality control, and allow companies to automate and digitalize more and more processes. By doing so, they are raising the efficiency in production to a new level.”

Digitalization and artificial intelligence


The concept of digitalization in industry has advanced enormously in recent years. Data from sensors and other sources that have previously been used mainly for immediate process control are stored as part of the digitalization, and thereby available on a more abstract level. Collecting the data is not sufficient. Artificial intelligence plays an important role when it comes to extracting the essence from these data, which can lead to further optimization.

An example of this is an application from the timber industry where several gigabytes of training data were able to be used to train a neural network that can make decisions more accurately, quickly and untiringly than possible with the human eye. This network is smaller than one megabyte but nevertheless has access to a wealth of experience. The concept can be applied to an arbitrary number of applications.

Added value of AI-based automation

It doesn't always require such a large volume of data, however, to benefit from artificial intelligence. The implementation of deep learning projects can vary greatly and depends on the specific customer requirements. Formulating this requirement and expectation is not always easy, but a necessary prerequisite for successfully employing artificial intelligence. “Put simply: you can only get the desired result using AI if you know what you want. Once that has been clarified, customers can expect to obtain a simple and flexible solution for their problem,” says Eichhorn.

“Thanks to AI, our customers can automate for themselves tasks that in the past were extremely difficult to automate. Good examples of this are quality and assembly control for reflective parts, checking solder joints, or sorting natural products. These are usually laborious tasks that use up a lot of the value time of qualified personnel and can therefore often only be performed by sampling.”

Training of artificial neural networks

The use of deep learning can bring about a paradigm shift in automation. Instead of determining what details are relevant for a decision and then applying a series of specific rules, it uses examples. The algorithm independently learns to make decisions. “We train a solution, rather than programming it, which is significantly faster and more efficient. It is important to emphasize, however, that deep learning is not an alternative to human expertise. This will remain necessary in order to exploit the full potential of the technology. Deep learning supports and extends human capabilities,” explains Eichhorn and finishes: “Thanks to our easy-to-use deep learning tools, users familiar with a problem will be able to solve it independently even without deeper programming knowledge. Because only they know what matters for the solution – and what doesn’t – and are better able to select suitable training examples than anyone else. With our tools, you can start training an artificial intelligence really intuitively and thereby solve an extremely specific and individual task.”

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