Artificial intelligence - the heart of Industry 4.0

Anyone who has visited a trade fair or conference aimed at the industry in recent years, or read the relevant trade magazines, has certainly come across the topic of artificial intelligence (AI) at some point. What is behind it?

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A large number of market suppliers offer solutions for almost every area of industry. In this article, however, we would first like to show what is behind the topic of AI and what benefits and challenges AI projects can bring.

What is artificial intelligence (AI)?

The term artificial intelligence (AI)dates back to the 1950s and today essentially describes a program's independent ability to learn from input data and work out results on its own, without instructions dictating the entire process.

AI is to be distinguished from "Artificial General Intelligence", which is an attempt to actually develop human-like competencies and task processing capabilities. In the aggregate, this is not the goal of the technologies subsumed under AI, even though conceptual overlaps may occur in some cases, such as in the area of neural networks.

In most cases, AI in industry describes various subcategories of machine learning, such as deep learning. This comprises a series of procedures by which a program learns, by means of various mathematical algorithms, to establish relations between data, some of which are difficult or impossible for humans to penetrate.

AI in industry and production

Modern production and other industrial processes generate a considerable amount of data: sensors provide constant signals, process parameters and results are stored and controlled on controllers, quality results are stored in databases, production specifications and results are reconciled, and much more. The usually massive amount of data that is generated in this process is often referred to as Big Data. AI can be used to generate Smart Data from Big Data.

AI can be used in a wide variety of ways in industry and production. In this way, AI can make a significant contribution to process optimization. Different parameters, such as recipe, machine speed and temperature can be constantly monitored and adjusted. In order to consequently individually adjust the scrap depending on the environmental parameters, so that optimum results can be achieved even when conditions change. For example, if heating in the production process leads to a change in mold properties, the AI can react appropriately to this changed property and adjust other parameters, such as recipe and speed, live. This ensures that scrap is minimized and the planned production quantity is affected as little as possible.

Process automation represents another potential area of AI application. For example, the condition of tools used in the machine can be monitored. This can be done directly via sensors or with the help of derived metrics, such as changes in power consumption or processing times. If a problem occurs, this can be detected at an early stage, whereupon the software can determine an optimal maintenance window and order the required spare parts in good time. This forward-looking action is also referred to as Predicitive Maintenance. In this way, production downtime and quality problems can be kept to a minimum.

These process automation and optimization methods can be extended to an entire factory or even a network of factories. Information and experience gained at one machine, factory or location can be passed on to other, even new, locations in a fully automated manner.

Challenges in the use of AI

The use of AI and related technologies presents a wide range of opportunities for widespread optimization for companies, but the application of these technologies also brings new challenges.

The simplest challenge may already be to gain an understanding of where AI can be usefully applied in the company. In addition, expertise is needed for implementation as well as, in many cases, communication of results to decision makers. The presentation of these results again requires appropriate software.

Internal processes also need to be adapted to new structures. Various documents and data points, such as maintenance intervals, are still often kept on paper today. Consequently, these are not available for analysis. Accordingly, this includes training the responsible employees, as well as adapting the downstream processes.


There is no way around digitization and the use of AI or machine learning-based optimization in the medium and long term. Internationally, these and related technologies are already being actively used, and the trend is for their use to increase. Companies must actively prepare for these changes today in order to remain competitive in the future. Companies should therefore conduct an analysis of their digitization and AI potential and take measures derived from this.

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