Autor: Till Becker

Drone technology and the use of artificial intelligence (AI) offer promising advantages in various sectors, including in inspection. The use of innovative inspection technologies can make inspections more efficient overall. This research project examines various legal and economic aspects of AI-based autonomous drone inspections. It also develops a target process that represents the use of an AI-based drone inspection and controls the use of such inspection technology. In particular, this article focuses on a collaborative approach to this new inspection methodology.

Due to the change of work in manufacturing caused by the introduction of Cyber-Physical systems, there is a need for adequate design principles for user interfaces between humans and machines. Within a research project, a method for the determination and evaluation of such design principles was developed. The method can be used to create a catalogue of rules regarding the successful integration of human factors into Cyber-Physical production systems.

The network analysis is a promising approach for assessing the behavior of material flow systems based on large data volumes. Previous studies focus mainly on static network analysis. This means that all the events that occur in a specific time period are aggregated to a single material flow network. As material flow systems are rapidly changing systems, a static view is not sufficient. The aim of this paper is to present existing concepts to obtain such structural changes and to assess their suitability for material flow networks by using real data.

Current developments of Industry 4.0 offer new possibilities for coordination and coope-ration in production networks. Information technology allows for an ad-hoc documentation and analysis of system states. Hence, manufacturing resources can be offered to other companies in the short term, and coordination, scheduling, and billing can be done quickly. Industry and science now need to find appropriate procedures and practices how to implement and run networks of shared resources.

The use of cyber-physical systems in production and logistics is an important component of Industry 4.0. The merger of production and logistics systems with information technology facilitates the transition from centralized to decentralized production planning and control. However, the need to equip logistic objects with the necessary technology is associated with high investment costs for production companies. This paper presents a method using dynamic clustering, which is suitable for identifying those parts of the production system, where an investment in technology for autonomous control leads to the largest increase in performance of the overall system.

Customers in online shops return at least half of the placed orders. This huge amount of return shipments results in high costs e.g. from a logistic point of view. To predict the return rate based on customer data and order information, machine learning techniques can be applied which are able to learn a powerful model for return prediction based on historical order data. This article introduces a hands-on approach for successfully applying machine learning in real world processes and shows a case study to predict the return shipment probability in an e-commerce scenario.