Manufacturing industries must address the needs of their human resources to maintain efficiency and profitability in the global markets. By fostering safe, psychologically supportive working conditions, companies can improve work performance and retain their workforce in the face of labor shortage. →

Modern Industry 4.0 applications can be implemented with the Local-First Cooperation design principle. Proven methods of artificial intelligence (AI) can be used to solve problems. Distributed software agents communicate directly, coordinate themselves with others, and cooperate to solve local problems. Symbolic and sub-symbolic AI complement each other in problem solving. In this way, complex planning and optimization tasks can be implemented using resilient systems. These systems of the Industrial Internet of Things increasingly show self-x properties of organic computing. →

A machine learning application predicts patient volumes and thus facilitates nursing staff scheduling in order to efficiently manage staff shortages.

In cooperation with XITASO GmbH, an application was developed that uses machine learning to predict and visualise patient volumes in order to improve nursing staff scheduling. Based on historical data, the time series forecasting method is used, whereby models such as Random Forest, SARIMA and Holts Winters seasonal method are utilised. The model quality is evaluated by RMSE, MAPE and MAE. The predictions are presented in a user-friendly web interface, which enables care facilities to optimise staff planning in line with demand. →

The production industry has the potential to make a significant contribution to the transition towards a more sustainable future. Given the pressing nature and growing popularity of this topic, a considerable number of producing companies have already implemented sustainability goals. Nevertheless, at the operational level, decisions are still largely driven by economic considerations. →

In collaboration with XITASO, we have developed a trade fair showcase that presents object recognition and segmentation using neural networks. By using RGB and infrared cameras, road users such as cyclists and pedestrians are recognised under different environmental conditions. Pre-trained models such as YOLOv8 and Mask R-CNN are used, which have been subsequently fine-tuned to infrared data to enable reliable detection even in poor lighting conditions. →

Employees at swa Netze lack an overview of the stock of usable spare parts and emergency materials. A tool for cross-departmental inventory management would be desirable, with which material stocks can be consolidated and sustainably reused in order to save money and resources. →

Predictive maintenance optimizes the maintenance of machines by preventing unplanned downtime and unnecessary repairs. The implementation is made possible by modern machine learning methods and a well-developed data infrastructure. In the research project PAS4PCM – in cooperation with Rational AG and on the basis of collected device data – the research group HSA_dsg of the Faculty of Computer Science at the Technical University of Augsburg is developing a concept and an implementation of such a system. →

Following the development of the prototype in the summer semester of 2023, the Pyramid app has now been officially published in the app stores. The app modernises communication between students and companies at Augsburg University of Applied Sciences' Pyramid Fair by supplementing existing sources of information such as the website, print and PDF catalogues. →

The increasing demand for components with tailored properties and functionally graded materials featuring adjustable interfaces is raising the requirements in gravity sand and die casting processes of aluminium. In response to these challenges, a novel controlled process based on injector casting is being developed. →

This paper presents a conceptual approach and a successful implementation guide for a digital twin of a modular manufacturing system. →