machine learning

AKWI-Tagungsband zur 35. AKWI-Jahrestagung. Jahrgang, 2022, Seite S. 287–303 Systematic testing of every single component and interface is undoubtedly an important measure to handle the complex nature of current software systems. However, this comes with often neglected computational costs. The aim of this paper is therefore to cut time and resource needs by predictive testing, i.e., predicting test failures with machine learning using a surprisingly simple statistical feature representation. Furthermore, we present the first open research benchmark for pre- dictive testing to enable and foster future research in this area

How can artificial intelligence (AI) automate production planning and control? This study examines its potential to enhance efficiency in modern production environments. The focus is on establishing a robust data infrastructure that integrates real-time, historical, and contextual data to create a solid basis for AI models. Reinforcement learning (RL) is applied to aid automation. A roadmap for implementation, focusing on practical application, is presented. This roadmap incorporates simulation-based training methods and outlines strategies for continuous improvement and adaptation of production processes.

Global competitive pressure is forcing companies to use resources efficiently, especially in high-wage countries. This is further intensified by market and legislative pressure for sustainable products and processes. In the face of digital and ecological change, holistic approaches to optimizing manual work processes are essential. An AI-supported assistance system for work plan creation is intended to remedy this and thus enable more efficient process design.

With the increasing availability of IoT devices and significantly greater incorporation of Internet-enabled technologies into manufacturing processes, the idea of improving factory control through the use of artificial intelligence (AI) is also coming to the fore. Using the example of high-variation series manufacturing, this article describes which steps need to be taken to improve factory control with AI.