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Application of Use Cases in Process Manufacturing - Digital Twin vs. Machine Learning / AI

The manufacturing process in a factory is subject to constant change. The primary goal is to ensure that production runs as fluently as possible in order to generate maximum output. Ideally, all machines run trouble-free.

The reality is somewhat different; the production manager is constantly confronted with problems in the production process on the shop floor. Malfunctions, worn out spare parts, missing components that throttle the performance of the machines, or in the worst case bring them to a standstill.

If you want to actively improve production, the path often leads to a question of how a particular sub-process can be modified to achieve the greatest possible benefit for the low-fault sequence.

In the digitized manufacturing process, the development of use cases is therefore an important part of management, accompanied by software development.

To design a use case, it is important to first obtain an overview of the requirements for the system. This includes identifying the actors that interact with the system.

The implementation of the use case can be done in different ways. Does one now create a digital twin of the plant, or does one make use of machine learning, or algorithms to detect faults using AI.

Depending on your wallet and time budget, all of these tools can make perfect sense.

A digital twin is a digital representation of a real object that provides a detailed and accurate representation of the object's condition and composition. The digital twin can play an important role in a manufacturing company by providing a reliable basis for analyzing production equipment and products. It serves as a virtual prototype to understand and optimize manufacturing processes. By simulating certain scenarios, the digital twin is a valuable tool when creating the use case. However, its development requires detailed process know-how and metrics of high data quality to create the most accurate copy possible. If the digital twin comes close to representing 100% of the machine's reality, a wide variety of scenarios can be simulated, limits can be explored, and well-founded predictions about machine behavior can be made. In addition, by combining several machines, it is possible to determine predictions about the reaction behavior of plants.

The use of machine learning applications and algorithms to detect faults in complex interlinked plants are very useful tools to optimize manufacturing processes. These tools can help improve plant reliability and performance by validating data, monitoring processes, and detecting faults early. These tools offer several advantages over the digital twin. For one, they can process and analyze data much faster and more efficiently than a digital twin. For another, they can help identify and solve specific problems in a production environment by detecting potential problems early and validating the data via anomaly detection. They can also help optimize processes by monitoring production equipment online and making necessary changes. In addition, the algorithms and applications learn from the raw data available. In most cases, 20% of the raw data can already be used to generate 80% accurate statements of a required use case. The prerequisite for this is high data quality and the use of data relevant to the use case.

Both methods have their justification. You have to weigh up what suits the creation of a specific use case. The factors of money and time often play a decisive role. At the beginning, therefore, one will tend to turn to machine learning or the use of AI algorithms in order to quickly achieve good results by selecting the appropriate algorithm and modeling the question.

Leaving these factors aside, the digital twin is a solid basis for optimizing manufacturing processes. The prerequisite is that significantly more resources are required to map the reality and check the correct behavior through validation.

Both methods require extensive process knowledge in order to be implemented successfully, otherwise large sums of money are quickly wasted and in the end no satisfactory result is achieved with any of the options.

The company Quantis relies on machine learning and AI with its IIOT platform. The knowledge has developed from decades of process experience in the beverage bottling industry. The experience gained from digital twins and their simulations led to the algorithms and applications used extremely successfully today.

Even though the know-how for a digital twin tends to reside on the part of the OEM, Quantis is able to create high-quality digital twins if the customer so desires.

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