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The paper aims to investigate the possibilities to control friction in lubricated systems by surface patterning, making use of a multi-scale approach. Surface patterns inside the tribological contact zone tend to directly reduce friction, whereas surface patterns located in the close proximity of the contact area can improve the tribological performance by avoiding lubricant starvation and migration. Finally, optimized surface patterns were identified by preliminary laboratory tests and transferred to a journal bearing, thus testing them under more realistic conditions.

Surface patterns on a large scale (depth > 10 µm) were fabricated by micro- and roller-coining, whereas surface patterns on a small scale (depth < 2 µm) were produced by direct laser interference patterning. The combination of both techniques resulted in multi-scale surface patterns. Tribologically beneficial surface patterns (verified in ball-on-disk laboratory tests) were transferred onto a journal bearing’s shaft and tested on a special test-rig. To characterize the lubricant spreading behavior, a new test-rig was designed, which allowed for the study of the lubricant’s motion on patterned surfaces under the influence of a precisely controlled temperature gradient.

All tested patterns accounted for a pronounced friction reduction and/or an increase in oil film lifetime. The results from the preliminary laboratory tests matched well, with results from the journal bearing test-rig, both tests showing a maximum friction reduction by a factor of 3-4. Numerical investigations, as well as experiments, have shown the possibility to actively guide lubricant over patterned surfaces. Smaller periodicities, as well as greater structural depths and widths, led to a more pronounced anisotropic spreading and/or greater spreading velocities. Multi-scale surfaces demonstrated the strongest effects regarding the lubricant’s spreading behavior.

Friction, as well as lubricant migration, can be successfully controlled by using micro-coined, laser-patterned and/or multi-scale surfaces. To the best of the authors’ knowledge, the study demonstrates for the first time the unique possibility to transfer results obtained in laboratory tests to a real machine component.

The purpose of this study is to investigate the influence of hemispherical structures fabricated by hot micro-coining on the resulting wear performance. Hemispherical structures with different area densities (20 and 30 per cent), depths (50 and 100 µm) and diameters (100 and 200 µm) were fabricated by hot micro-coining on stainless steel samples.

The wear performance of these samples was studied using a ball-on-disk tribometer in rotational sliding mode using a normal load of 30 N and a fixed sliding velocity of 2 cm/s. Two different poly-(alpha)-olefin (PAO) oils without any additive having a kinematic viscosity of 4 and 40 cSt, were used to study the influence of the oil viscosity on the wear behavior.

Concerning the polished reference, an enlarged wear volume with an increase in the cycle number and the oil viscosity was observed. In the case of the micro-coined surfaces, all samples demonstrate a pronounced reduction in the wear volume (up to a factor of 100 for PAO 40) compared to the polished reference irrespective of the oil viscosity used.

This study details new research work studying the wear behavior of hot micro-coined surfaces.

This paper serves to present the Swiss data in the framework of the international project “Civil–Military Gap” of ERGOMAS Working Group “Military Profession”. Its theoretical basis has been developed in a common working paper (Jelusic, Caforio, Haltiner, Moelker, & Szvircsev Tresch, 2003) and will be presented in a more detailed way in a forthcoming common cross-national analysis. The main research hypothesis and its implied research questions refer to the existence of a growing cultural gap between the military and its parent democratic society: Is there such a gap between the armed forces, mainly its professional bodies and democratic society? If yes, what is the nature of that gap? According to the planning of the research project, the following research steps are carried on in the participating countries: (1) investigate the political culture of future (civilian and military) elites by simultaneously surveying cadets at military academies and students at civilian universities; (2) carry out semi-structured interviews with present elites (an expert survey) in order to assess changes in civil–military relations over time; and (3) elaborate and compare results at cross-national level and compare and contrast them with data from the American (Feaver, Kohn, & Cohn, 2001) research. This paper first outlines some peculiarities of the Swiss military system, considering them as somewhat important for the question of the nature of a possible civil–military gap. It then presents the methodological procedure and the main findings of Switzerland.

Various iron loss models can be used for the simulation of electrical machines. In particular, the effect of rotating magnetic flux density at certain geometric locations in a machine is often neglected by conventional iron loss models. The purpose of this paper is to compare the adapted IEM loss model for rotational magnetization that is developed within the context of this work with other existing models in the framework of a finite element simulation of an exemplary induction machine.

In this paper, an adapted IEM loss model for rotational magnetization, developed within the context of the paper, is implemented in a finite element method simulation and used to calculate the iron losses of an exemplary induction machine. The resulting iron losses are compared with the iron losses simulated using three other already existing iron loss models that do not consider the effects of rotational flux densities. The used iron loss models are the modified Bertotti model, the IEM-5 parameter model and a dynamic core loss model. For the analysis, different operating points and different locations within the machine are examined, leading to the analysis of different shapes and amplitudes of the flux density curves.

The modified Bertotti model, the IEM-5 parameter model and the dynamic core loss model underestimate the hysteresis and excess losses in locations of rotational magnetizations and low-flux densities, while they overestimate the losses for rotational magnetization and high-flux densities. The error is reduced by the adapted IEM loss model for rotational magnetization. Furthermore, it is shown that the dynamic core loss model results in significant higher hysteresis losses for magnetizations with a high amount of harmonics.

The simulation results show that the adapted IEM loss model for rotational magnetization provides very similar results to existing iron loss models in the case of unidirectional magnetization. Furthermore, it is able to reproduce the effects of rotational flux densities on iron losses within a machine simulation.

For the electromagnetic simulation of electrical machines, models with different ranges of values, levels of detail and accuracies are used. In this paper, numerical and two analytical models of an induction machine (IM) are analysed with respect to these aspects. The purpose of the paper is to use these analyses to discuss the suitability of the models for the simulation of various physical quantities of an IM.

An exemplary IM is simulated using the two-dimensional numerical finite element method, an analytical harmonic wave model (HWM) and an extended HWM. The simulation results are analyzed among themselves in terms of their level of detail and accuracy. Furthermore, the results of operating map simulations are compared with measured operating maps of the exemplary machine, and the accuracy of the simulation approaches is discussed in the context of measurement deviations and uncertainties.

The difference in the accuracy of the machine models depends on the physical quantity of interest. Therefore, the choice of the simulation method depends on the nature of the problem and the expected range of results. For modeling global machine quantities, such as mean torque or losses, analytical methods such as the HWM s are sufficient in many applications because the simulation results are within the range of measurement accuracy of current measurement systems. Analytical methods are also suitable for local flux density curves under certain conditions. However, for the simulation of the influence of local physical effects on the machine behavior and of temporally highly resolved quantities in saturated operating points, the accuracy of the analytical models decreases and the use of the finite element method becomes necessary.

In this paper, an extension of the HWM is used to calculate the IM, which, in contrast to the HWM, models the saturation. Furthermore, the simulation results of the different electromagnetic IM models are put into the context of the uncertainty of a measurement of several identical IMs.

While the understanding of customer satisfaction is a key success factor for service enterprises, existing elicitation approaches suffer from several drawbacks such as high manual effort or delayed availability. However, the rise of analytical methods allows for the automatic and instant analysis of encounter data captured during service delivery in order to identify unsatisfied customers.

Based on encounter data of 1,584 IT incidents in a real-world service use case, supervised machine learning models to predict unsatisfied customers are trained and evaluated.

We show that the identification of unsatisfied customers from encounter data is well feasible: via a logistic regression approach, we predict dissatisfied customers already with decent accuracy—a substantial improvement to the current situation of “flying blind”. In addition, we are able to quantify the impacts of key service elements on customer satisfaction.

The possibility to understand the relationship between encounter data and customer satisfaction will offer ample opportunities to evaluate and expand existing service management theories.

Identifying dissatisfied customers from encounter data adds a valuable methodology to customer service management. Detecting unsatisfied customers already during the service encounter enables service providers to immediately address service failures, start recovery actions early and, thus, reduce customer attrition. In addition, providers will gain a deeper understanding of the relevant drivers of customer satisfaction informing future new service development.

This article proposes an extendable data-based approach to predict customer satisfaction in an automated, timely and cost-effective way. With increasing data availability, such AI-based approaches will spread quickly and unlock potential to gain important insights for service management.