Artificial Intelligence

Manufacturing companies must work economically while delivering quality - in some industries with a zero-defect tolerance. Quality control often is carried out manually and with a time delay, thus errors can only be corrected at a late stage. Using artificial intelligence (AI), visual quality control can be automated, carried out in real time and integrated into the production process - making it more accurate, efficient and cost-effective. A case example shows the advantages of tackling AI issues in interdisciplinary teams with partners.

Artificial intelligence (AI) and self-learning systems have immense economic potential and are a driving force for digitalisation. Artificial Intelligence is radically changing value chains, business models, and employment in industry. Data-driven services are added to traditional products in almost all industries. Integrating Artificial Intelligence in products and services as well as using data from the production process provides opportunities for new business models in an increasing competitive international environment.

The use of autonomous systems is not efficient in all applications due to variable system environments or small quantities. Semi-autonomous systems are able to bridge this gap. This article presents a digital twin-based approach for human-machine interaction using adaptive automation. A case study shows how a modular digital twin can support the operator of a CPS in specific tasks. This method allows for a distinction between short-term signal changes and long-term behavior modification. Thus, semi-autonomous systems can support operators in scenarios in which autonomous systems are not viable.

Due to competitive pressure pharmaceutical companies are also driven to increase the efficiency of their processes. In this paper an approach for the predictive detection of malfunctions of filling systems for powdery pharmaceutical products using machine learning is presented. The focus is on the prediction of filling deviations with recurrent neural networks, with the objective to detect a drift in the process stability to intervene accordingly.

Due to the advancing digitalization of business sectors and increasing competitive pressures, industrial companies are forced to promote their own digital transformation to sustain on the market. Here, the literature regards the CFO as a key corporate function to induct digitization initiatives within organizations. The blockchain technology, due to its features of transparency, immutability and cryptography combined with its ability to coordinate data flows of e. g. the IoT or AI, constitutes a suitable instrument for the CFO to meet the requirements of the Industry 4.0. The results are improvements of business processes in regard to efficiency and automation, a relocation of the CFO’s strategic role, improvements of CFO-relevant KPIs through integrating machines into payment networks as well as the emergence of integrated business ecosystems facilitating new forms of inter-organizational collaboration.

In order to support small and medium-sized enterprises (SME) in the field of large-scale plant construction, the requirements relevant for construction site logistics planning as well as the planning and controlling of logistics processes in large-scale plant construction using digital models are being investigated within the framework of the research project BIMLog. In this paper, the planning-relevant requirements and their description are presented in an ontology as the basis of a digital planning.