Companies are confronted with increasingly demanding environments, including globalization, technologization, intergenerationality, and crises such as the coronavirus pandemic. To accept uncertainties as a challenge and to recognize opportunities for development, well-educated and resilient founders are needed who can foster innovation and sustainable development within society and the economy. The majority of today’s entrepreneurs have an academic background. Hence, institutions for higher education need to provide comprehensive educational offerings and support initiatives to train and sensitize future entrepreneurs. Therefore, since 2013, agile teaching formats have been developed in our project at a Bavarian university of applied sciences. In two stages, we founded a limited company for hands-on experimentation with entrepreneurship and also conceptualized an elective course and an annual founders’ night. Based on a theoretical model and continuous teaching evaluations, we adjusted the individual modules to suit the target group. The objective is to promote the acquisition of key competencies and exert a positive influence on the startup quotient in the region. There are six startups by students who can be traced back to our project. This indicates that a target-group-oriented educational program encourages motivation and awareness of entrepreneurial thinking and action among students.

Nowadays, additive manufacturing processes are becoming more and more appealing due to their production-oriented design guidelines, especially with regard to topology optimisation and minimal downstream production depth in contrast to conventional technologies. However, a scientific path in the areas of quality assurance, material and microstructural properties, intrinsic thermal permeability and dependent stress parameters inhibits enthusiasm for the potential degrees of freedom of the direct metal laser melting process (DMLS). Especially in quality assurance, post-processing destructive measuring methods are still predominantly necessary in order to evaluate the components adequately. The overall objective of these investigations is to gain process knowledge make reliable in situ statements about component quality and material properties based on the process parameters used and emission values measured. The knowledge will then be used to develop non-destructive tools for the quality management of additively manufactured components. To assess the effectiveness of the research design in relation to the objectives for further investigations, this pre-study evaluates the dependencies between the process parameters, process emission during manufacturing and resulting thermal diffusivity and the relative density of samples fabricated by DMLS. Therefore, the approach deals with additively built metal samples made on an EOS M290 apparatus with varying hatch distances while simultaneously detecting the process emission. Afterwards, the relative density of the samples is determined optically, and thermal diffusivity is measured using the laser flash method. As a result of this pre-study, all interactions of the within factors are presented. The process variable hatch distance indicates a strong influence on the resulting material properties, as an increase in the hatch distance from 0.11 mm to 1 mm leads to a drop in relative density of 57.4%. The associated thermal diffusivity also reveals a sharp decrease from 5.3 mm²/s to 1.3 mm²/s with growing hatch distances. The variability of the material properties can also be observed in the measured process emissions. However, as various factors overlap in the thermal radiation signal, no clear assignment is possible within the scope of this work.

Even though assistance systems offer more potential due to the increasing maturity of the inherent technologies, Automatic Speech Recognition faces distinctive challenges in the industrial context. Speech recognition enables immersive assistance systems to handle inputs and commands hands-free during two-handed operative jobs. The results of the conducted study (with n = 22 participants) based on the counterbalanced within-subject design demonstrated the performance (word error rate and information transfer rate) of the HMD HoloLens 2 as a function of the sound pressure level of industrial noise. The negative influence of industrial noise was higher on the word error rate of dictation than on the information transfer rate of the speech command. Contrary to expectations, no statistically significant difference in performance was found between the stationary and non-stationary noise. Furthermore, this study confirmed the hypothesis that user acceptance was negatively influenced by erroneous speech interactions. Furthermore, the erroneous speech interaction had no statistically significant influence on the workload or physiological parameters (skin conductance level and heart rate). It can be summarized that Automatic Speech Recognition is not yet a capable interaction paradigm in an industrial context.

In the context of urban production and sustainable reuse of existing buildings, a detailed planning of the later usage is indispensable. One approach is to enable large-scale AR simulation on site with a sufficient Level of Detail (LoD) and stability. To determine performance metrics, a technology-stack is created and presented that enables a realistic field experiment in an industrial environment (area of 1,314 m²) using Microsoft HoloLens 2. For the experiment, a 3D model was instantiated as often as possible up to the limit of system stability and in different LoDs (100% down to 10%). The result shows that it is feasible to represent 2.63 million polygons (equivalent to about 1,909 m³ of augmented space) on LOD-35%; LoD-100% is equivalent to 327.38 m³ and 1,284 million polygons. Polygonal density [polygons/m³] is introduced as new indicator for better comparability when using 3D models. Thus, it is possible to immersively visualize urban production planning processes in large-scale scenarios. This expands the functional planning space of Urban Production and overcomes previous technical limitations.

Emerging assistance systems are designed to enable operators to perform tasks better, faster, and with a lower workload. However, in line with the productivity paradox, the full potential of automation and digitalisation is not being realised. One reason for this is insufficient training. In this study, the statistically significant differences among three different training scenarios on performance, acceptance, workload, and technostress during the execution of immersive measurement tasks are demonstrated. A between-subjects design was applied and analysed using ANOVAs involving 52 participants (with a statistical overall power of 0.92). The ANOVAs were related to three levels of the independent variable: quality training, manipulated as minimal, personal, and optimised training. The results show that the quality of training significantly influences immersive assistance systems. Hence, this article deduces tangible design guidelines for training, with consideration of the system-level hardware, operational system, and immersive application. Surprisingly, an appropriate mix of training approaches, rather than detailed, personalised training, appears to be more effective than e-learning or ‘getting started’ tools for immersive systems. In contrast to most studies in the related work, our article is not about learning with AR applications but about training scenarios for the use of immersive systems.