Typeset

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.

Abrasive blasting is often used to clean work pieces. During the process an abrasive medium is propelled with compressed air toward a given surface. Common abrasives are sand, glass beads, steel or corundum. For safety reasons the blasting process is carried out in closed blast cabinets or rooms. Abrasives and cut off material are filling the air so that the visibility is limited. Quality assurance and safety monitoring of workers in blast rooms are therefore difficult which is essential e. g. in atomic power plant demolition. This article describes the development and test of a camera to improve this situation. Compressed air flows through the camera housing to keep particles away from the lens. The air flow was optimized by computational fluid dynamics. A prototype was made by 3D printing and tested in an blast cabinet.

Procurement has the task to supply an organization with required but not self-produced goods. The goods vs. payment exchange with suppliers is laid down in contracts. “Electronic contracts" or “Smart Contracts” represent the logic digitally and thus enhance transparency. This can still evolve. In the future, improved algorithms and artificial intelligence will not only be able to administer contracts, but also to design them. This article presents the status quo of "Smart Contracting", places it in the "Legal Tech" topic and shows how artificial intelligence could be used in procurement.

Optimisation is an essential task in many situations. The class of evolutionary algorithms is a population-based, heuristic technique for optimisation. They allow the optimisation of multi-modal problems even with distorted search spaces. They can propose several solutions instead of just one. An important aspect of evolutionary algorithms is encoding search space candidates. In the optimisation of processes, this is a non-trivial task. This article describes a successfully tested encoding.

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)