Typeset

Manufacturing companies of all sizes have been a popular target for hackers and cyber criminals for years - and the trend is rising. In addition to security gaps in the IT systems, OT systems, i.e. operational technology, are increasingly being targeted by attackers in the course of digitalisation and networking. The consequence: companies should secure themselves as quickly as possible. Hans-Peter Bauer, Senior Vice President Cyber EMEA at BlackBerry, explains how this is possible. (Only in German)

While the digital world has already made its way into many other production areas, the bakery industry has so far benefited little from these technological developments. This is because many baking processes are manually controlled and rely on expert knowledge. In addition, the process data from the machines is still not automatically captured via sensors. This paper describes a procedure for digitizing baking processes by developing an IoT framework consisting of an IoT device including measurement methods, an edge gateway, and a simulation-based solution for process optimization.

Industry 4.0 and Smart Factory have become a real source of hope and are the technological answer to some of the biggest challenges of our time: sustainable production, global interconnections, intelligent exchange of knowledge. This special issue discusses research questions relating to process improvement, artificial intelligence and factory software.

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.