Artificial Intelligence

While generative artificial intelligence has gained more visibility and achieved initial successes, it is largely unused in the industry context. In contrast, its development and versatility point to a promising application for industrial manufacturing – especially in cases where complex challenges such as decisionmaking or process optimization are present. Showcasing the various development horizons and several example case studies provides a particularly illuminating illustration of its potential for the field of technology management.

The design of gear pumps causes gap flows, which counteract efforts to achieve exact dosing. However, due to the complex relationships between pressure, temperature, manufacturing tolerances and the material properties of the pumped medium, these gap flows cannot be reliably described in real time using physical equation systems. By using new algorithms and artificial neural networks, it was possible to solve this problem and replace a cost-intensive flow measurement method at the same time. This new approach has been tested on a pilot plant scale, and has already demonstrated that it can achieve the accuracy of a classic flow meter, while at the same time significantly decreasing the response time and increasing the application range of the dosing system.

Interoperable automation can benefit cybersecurity certification processes that result from the EU Cybersecurity Act (e.g. EUCS) so that they represent less overhead for the stakeholders involved. The development of key standardization efforts involving relevant stakeholders (e.g. regulators) is needed to fully realize these benefits. EU projects like H2020 MEDINA, HEU COBALT and communities such as EUROSCAL are well on the way to achieving this goal. However, more practical experience is needed to make continuous certification with automation a reality.

The complex dynamics of block warehouses pose major challenges to the manual stocktaking process. Frequent relocation of pallets, crates or pallet cages without fixed storage locations leads to a time-consuming and error-prone inventory process, wherein goods often have to be searched for and damages due to improper storage can occur. The use of (semi-)autonomous drones offers a promising solution to enable automated stocktaking, especially if these are appropriately equipped for optical goods detection.

Motion-Mining® is a technology that uses motion sensors and pattern recognition to enable automated process mapping and analysis of manual work. This article evaluates the advantages and limitations of its use in manufacturing and logistics processes. To this end, Motion-Mining® is compared with traditional lean management tools used to analyze manual activities. Experiences derived from four use cases provide decision support for selecting the appropriate method for a specific use case.

Correctly recycling obsolete refrigeration devices plays an important role in environmental and climate protection efforts. Recycling plants are subject to regular audits to ensure their compliance with strict environmental regulations. However, the collection of audit-related data is a challenging and time-consuming task, as it is usually done manually and is prone to errors. One solution for more sustainable and efficient monitoring is to automate digital data collection using sensors and artificial intelligence. This enables a direct estimate of the expected level of pollutants. This paves the way for continuous performance monitoring and efficient management of refrigeration appliance recycling plants.