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Artificial Intelligence: 5 ways to improve your processes

How to use Artificial Intelligence in your production plants?

Artificial Intelligence, Big Data Analytics and Computer Vision are becoming buzzwords.

Artificial Intelligence is very often associated with something extremely complicated (and in fact it is!) And distant from our everyday life, especially if we work in productive contexts where, alas, innovation and new technologies are implemented with a certain slowness and inertia.

In an extremely synthetic way, artificial intelligence consists in the implementation of a series of algorithms that allow to replicate, albeit in an often limited way, some features of human intelligence, such as image interpretation, multi-translation language or data processing and analysis.

However, there are now several concrete applications that can give substantially immediate results with very minimal investments.

In this post, we will provide an overview of 5 artificial intelligence applications within production systems.

# 1: Real-time translation of work instructions

Let’s start with the apparently simpler application, namely the real-time translation of work instructions. Let’s say simpler as we have all experienced the usefulness of tools such as Google Translate or Deepl that allow us to translate any document in an extremely easy, fast way and with an ever higher translation quality. These algorithms are now integrated into software and applications to support front-end operations within the workshops. The advantage is considerable, as it saves valuable time in translating documentation within the sites of multinational companies or translating support chats between operators of different languages. In summary, these algorithms are implemented in the following solutions:
  • Multilingual digital work instructions
  • Remote assistance applications, where the chat is automatically translated into different languages

# 2: Analysis of work flows and work force

We now enter the world of artificial vision systems, that is systems capable of extrapolating information from the images acquired by cameras.

By installing a simple camera inside the workshop, it is possible to implement algorithms capable of extrapolating critical information regarding, for example:

  • process flow management, creating spaghetti charts in real time
  • analysis of value-added and non-value-added activities of the workforce
  • compliance with standard operating instructions, monitoring the position of personnel during the work phases

# 3: Final Inspection

More and more companies are offering solutions that exploit artificial intelligence for the final inspection of components, both at the end of processing and at the end of assembly. For example, thanks to deep learning algorithms it is possible to detect surface defects that are not acceptable according to the reference standards. Among other things, this operation is often subjective, therefore influenced by the interpretation of the testing inspector, although the procedures try to make it as objective as possible. Or, it is possible to identify the presence or absence of components following an assembly operation. These solutions are implemented in the BMW plant in Munich, or by the European Space Agency for the verification of electronic components in satellites.

# 4: Predictive Maintenance

By analyzing the vibrations and therefore the frequencies coming from the critical mechanical components within a machine (typically bearings) it is now possible to predict with some accuracy if and within how long the component will break.

In terms of TPM, this represents a considerable benefit. In fact, we move from the concept of preventive maintenance (for example, a car service), to predictive maintenance, that is, we only intervene before a machine stop.

This paradigm shift, in fact, saves considerable time and resources, as the wear of components largely depends on their use, so it is possible that predictive maintenance requires more downtime than is actually necessary, or not. intervene when it would be more appropriate.

Several companies on the market are launching themselves in this sector, providing both the required hardware and support in their positioning and in the analysis of the collected data.

# 5: Real-time root cause analysis

We conclude our series of artificial intelligence applications with perhaps the most concrete example regarding the qualitative improvement of processes, namely the use of algorithms capable of identifying the main causes of defects within a process.

Who is familiar with the DMAIC stages of Lean Six Sigma processes knows how the most boring and energy-consuming part is the collection and analysis of data through the appropriate use of statistical tools..

Artificial Intelligence is able to perform statistical analysis in real time based on an enormous amount of data, the so-called Big Data.

By appropriately correlating data from different processes, it is possible to identify causes that would otherwise be impossible to discover.

In this sense, we are in contact with several companies that do just that: they collect and analyze data using the statistical tools of Six Sigma providing process engineers more accurate results in a fraction of time compared to the “human” analysis of the same.

How to implement Artificial Intelligence in your company?

In this post we have described 5 concrete applications of artificial intelligence within a production plant.

It should be remembered that for each of these applications there are different suppliers able to provide their own technological solution at very affordable costs even to SMEs.

However, the solution must be chosen on the basis of a series of precise criteria, mainly the goal to be achieved.

In case you want to try your hand at implementing artificial intelligence algorithms, we recommend that you take a look at our online courses.

If, on the other hand, you are interested in the more applicative side of new technologies, we recommend the course “the 9 key technologies of industry 4.0

Stay tuned

Nicola Accialini

Hi there! I am Nicola, founder and admin of SkillS4i. Aerospace Engineer, technology enthusiast and industrial expert. I live in Spain and I like travelling, cycling, hiking and reading.

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