Machine learning is crucial for Industry 4.0 because it allows machines to perform tasks independently. How exactly this works and the benefits for industry are explained below.
What is machine learning?
Machine learning is a branch of artificial intelligence (AI). It focuses on an algorithm that evaluates large amounts of data and draws conclusions from it. Instead of a prescribed (programmed) solution, the algorithm works out its own approach by evaluating the data. In this context, we also speak of intelligent machines.
What types of machine learning are there?
A distinction can be made between different types of machine learning:
Supervised machine learning
In supervised machine learning, the algorithm is specifically trained using examples. One example: drill holes in components. Put simply, the algorithm is shown a large number of images of drill holes in components. It also receives information as to whether the drill holes have been drilled in accordance with the quality specifications or not. The algorithm then develops a system to independently classify the quality of drill holes in the future.
Partially supervised machine learning
Partially supervised machine learning is similar to supervised machine learning. However, the data set with a target is limited and is therefore supplemented by data for which no target is available. In relation to the example with the boreholes, this would mean that the algorithm is trained partly with images that contain information about the correct placement of the boreholes and partly with images that do not contain this information.
Unsupervised machine learning
In unsupervised machine learning, the algorithm works exclusively with data that does not describe any additional information about the correct state. It must therefore analyze the data itself and recognize certain patterns from which it makes deductions.
Reinforcing machine learning
Reinforcement machine learning is based on a fundamental principle: the system is supposed to master a task and is rewarded for doing so (positive reinforcement). At the beginning, the correct solution is not clear; the machine has to find it independently using the trial-and-error method by orienting itself towards the reward. The advantage: machines can develop and test countless proposed solutions within a very short time.
What areas of application and benefits does machine learning offer?
In the future, it can be assumed that artificial intelligence and machine learning will change industrial processes on a large scale. Some areas of application and the resulting benefits have already been tried and tested.
Production optimization
Machine learning offers a whole range of opportunities for industry: valuable resources can be saved by handing over tasks to machines equipped with artificial intelligence. Thanks to machine learning, the algorithm can independently optimize and accelerate production.
Predictive maintenance
Another important aspect is predictive maintenance. Machine learning enables machines to optimally read out their own condition at any time. By evaluating data, they can also determine the best possible time for maintenance work, thereby optimizing maintenance processes and reducing repair downtime.
Quality checks
Machine learning enables machines and systems to carry out efficient quality checks. The machine uses data to learn what a drill hole in a component should look like, for example. It then independently recognizes whether the produced components or their drill holes meet the quality criteria or whether improvements are required. This not only makes quality control more efficient - it also enables higher safety standards.