Machine Learning in Industry 4.0: Five Use Cases

For a long time, people had to learn how machines work, how to operate them and how to use them as efficiently as possible. Today, it’s the other way round: machines learn to understand processes, interact with their environment and intelligently adapt their behaviour. Robotics, sensor technology, big data and artificial intelligence make machines in industrial production smarter than ever before. In particular, methods such as machine learning have been responsible for significant progress in recent years. Machine-learning algorithms bring two major advantages to the production process:

  • Improvement of product quality
  • Flexibility of the production process

Machine Learning has become the main driver of innovation in certain industrial sectors. This is reason enough to take a look at five of the most important applications for machine learning in industry 4.0.

1. Transformation of the production process: Smart Manufacturing

Machine Learning is one of many data science methods that fall within the wide range of artificial intelligence. Machine Learning allows production processes to be newly understood and intelligently transformed. Data collected and evaluated during the production process forms the basis for this. The individual process can thus not only be better understood, but also optimized. Data evaluation results in processes being continuously adapted to the current production conditions. Accordingly, Smart Manufacturing is characterized by the fact that optimizations are carried out automatically and adjustments can be made on the level of individual components.

For example, one of our industrial customers wanted to improve the relatively error-prone painting process for automotive parts. Incorrect varnishing is a challenge because it requires a lot of manual rework. Our solution appeared after we first digitally recorded the painting process. In the course of this process, data sets were created for parameters such as paint thickness, the PH values of the coatings or drying times of the painted parts, with which the coating process could be analyzed. The results obtained here were subsequently used to optimize critical targets such as a certain gap dimension.

2. Predictive Maintenance

Sensor data provide valuable information about the condition of machines. In addition, sensors have been becoming smaller and cheaper in production for many years. The monitoring of machines is thus becoming more and more affordable for companies. In a production machine, thousands of individual measuring points can produce a digital image of the current “healthy” condition of the machine. Data sets from this healthy state can be used afterwards to train machine learning algorithms. The aim is to use machine learning to identify patterns in many petabytes of sensor data that indicate possible malfunctions or failure of individual components. The overriding goal is to be able to repair machines before they are defective.

3. Autonomous vehicles and interactive machines in production

There is one characteristic that makes Machine Learning such a powerful tool: the ability to independently recognize patterns and regularities and apply them to new, unforeseen situations. Road traffic is just one of the many environments in which new situations are constantly arising that have to be assessed on the basis of the trained rules. Autonomous vehicles and autonomous machines are, however, only one possible scenario that is made possible by this. At least as important will be collaborative machines that are intelligent enough to interact with people. Intelligent, interactive systems are central to the transformation of the manufacturing process in the networked factory. The production of products with the “batch size 1” is thus possible.

4. Optimized energy management for climate and energy change

Climate change and energy transition are two of the biggest challenges facing us today. Industry, especially the players in the energy market, must deal with the consequences. The energy mix from conventional and renewable energy sources such as solar energy and wind power, resulting from the transformation of energy systems, result in more frequent fluctuations in the electricity grid than before. As a result, electricity producers face fines for overproduction and underproduction, which must be avoided by all means. The following graphic illustrates these relationships:

Machine Learning helps to make the increasingly becoming more complex energy market manageable. The guiding question is: How is it possible to satisfy the demand for energy in an optimal way? When it comes to gaps in demand arising from renewable energy sources, the framework conditions of energy production and energy demand must be analysed. Machine-learning algorithms help to optimally match demand and supply. This has three main advantages:

  • Based on historical energy consumption patterns, the expected demand can be derived.
  • An intelligent control system ensures a price-optimised strategy for power generations
  • Production control is possible in real time

5. Quality control or „test automation 2.0“

Particularly in Germany, the quality of products produced in the context of industrial production is of outstanding importance. Traditionally, the quality of products is only checked at the end of the production process. Machine Learning turns this relationship upside down: The use of sensor technology and the continuous evaluation of data at component level make it possible to check and ensure the quality of work parts during operation. Particularly when sources of error can be determined beforehand and their variables influenced, individual measurement data can be collected during production and tests can be integrated into the production process. A test automation based on Machine Learning can significantly improve the quality of production. The mechanical engineering sector in particular can benefit from machine learning and continuous quality control.

Machine Learning and the Revolution of the Manufacturing Industry

Machine Learning is one of the most important trends of the coming years and will fundamentally change the manufacturing industry. Not only large corporations will be affected by the transformation, but also small and medium-sized enterprises. Hardware for data processing which is ontinuously becoming cheaper  as well as large amounts of data are the two main prerequisites for the transformation that Machine Learning will bring to the manufacturing industry.