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In the production and operation of complex, one- of-a-kind products, disruptions inevitably occur. In practice, there are often deficits in terms of transparency and information flow when it comes to disruption management. Digital assistance systems facilitate disruption documentation: they increase the quality of information by locating it in the CAD model and in the overall product plan, thus accelerating targeted fault elimination. A generic data model makes it possible to use digital assistance systems for different products, trades and processes and in different product life phases. (Only in German)

Digitization and sustainable development are playing an important role in many areas, especially in production, although it is still unclear how they influence each other. First studies already addressed the question of how digitization can impact sustainability. It became clear that an evaluation method with indicators from all sustainability perspectives is needed. In this article, we will present a model-based evaluation method especially for ecological and economic sustainability, taking digital textile printing as an example. (Only in German)

Energy efficiency is becoming increasingly important in all sectors of the manufacturing industry. Companies are currently feeling the pressure of exorbitant energy prices very clearly, as well as the additional challenge of becoming CO2-neutral by 2045. With technologies from the field of machine learning (ML), innovative solutions can be developed that enable energy-efficient product manufacturing. In this way, ML-supported process control can make a decisive contribution to increasing the sustainability and competitiveness of a company. Decisive for ML-supported process control are the process- and raw material- dependent parameters, which are significantly responsible for the quality of the final product. The subject of this paper is a procedure for analyzing the complex relationships between the relevant influencing parameters for increasing energy efficiency in the manufacturing industry. (Only in German)

The Circular Economy (CE) is a form of economy that extends the use of products and resources by developing the linear supply chain (SC) to a circular SC. However, additional input factors are required for remanufacturing and recycling. Furthermore, these processes generate additional environmental impacts. It is questionable whether the circulation of products is only worthwhile from an economic point of view or whether it also brings environmental advantages. An approach that relates the economic impact of a product to its environmental impact is the Green Productivity Index (GPI). In the following, this index is developed for CE. Furthermore, this article examines how digitalization can positively affect the Green Productivity (GP) of CE. (Only in German)

The use of industrial robots in additive manufacturing has been increasing in recent years. Particularly due to the voluminous installation space and the great flexibility, they are predestined for the production of large-volume, individualised components. The multi-axis movement options of the print head attached to the end effector in conjunction with a swivel-tilt unit of the build platform mean that support structures can be dispensed with, which represents a major economic advantage.

Climate neutrality and digitisation are two future-relevant and interlinked topics that are gaining in importance for manufacturing companies. However, especially for small and medium-sized enterprises (SMEs), it is often difficult to get an overview of the concepts and practical measures in these fields. This article presents a maturity model that offers companies practical assistance in combining the goals of climate neutrality and digitisation and in identifying suitable (digitisation) measures for the company to support the transformation towards climate-neutral production. (Only in German)

Additive manufacturing is an increasingly important manufacturing technology with huge economical potential. However, its popularity is accompanied by high material and time losses, as defects are often detected at a very late stage. One solution for a more sustainable production is the automated detection of manufacturing defects using artificial intelligence. This article describes the digitization of the defect detection process in additive manufacturing using a system based on a neural network. In addition to the steps for automated defect detection, system performance is also discussed.

The competitive pressure in the contract logistics industry is intense. Logistics providers must respond to tenders quickly and with convincing concepts. This article presents initial approaches to how logistics process planning in tender management can be supported using supervised learning methods. Under the premise that similar processes from past projects can be transferred and adapted to a project to be planned, an AI-based assistance system suggests appropriate process steps and MTM (Methods-Time Measurement) codes during planning. This procedure can accelerate process planning and lead to an increased quality of logistics processes to be planned. (Only in German)