Edmund Marth

In the development of resource-efficient and user-centered power tools, it is essential to identify a set of operating points corresponding to specific work processes, as performed under real-world field conditions. For power tools like chainsaws, the operating point is influenced by the applied load, which results from the interaction between the machine, the work piece, and — most critically — the user’s behavior. To effectively support the development through a digital twin (DT) prototype, it is vital to incorporate the human factor into the digital model. Current research lacks straightforward methods for modeling user behavior. Most existing literature focuses either on the DT of the product itself or on complex, large-scale approaches to modeling human behavior. This article explores the integration of a human controller (HC) framework into the DT environment for power tools. The objective is to generate added value in the early stages of the product life cycle with minimal effort, while also enabling a more detailed representation of a human twin. As a case study, the use of a chainsaw during the processing of a felled tree is examined. Relevant human behavioral characteristics are identified through empirical measurements. The functionality of the integrated HC is then verified within the DT prototype.

This manuscript presents a reinforcement learning (RL) agent method to optimize the geometry of a coaxial magnetic gear using a 2D finite element magnetic (FEM) simulation. The proposed optimization algorithm aims to improve the maximum torque within given boundaries of the magnetic gear geometry by adjusting parameterized radii. A linear actor–critic gradient algorithm is implemented, where the actor learns a policy to adjust and discover the values of five geometric parameters of the magnetic gear model, and the critic evaluates the performance of the resulting designs. The RL agent interacts with an environment integrated with a 2D FEM simulation, which provides feedback by calculating the total torque of the new geometry discovered. The optimization algorithm uses a greedy exploration method that uses the total torque as a reward system, which the RL agent aims to maximize. The results obtained for the magnetic gear optimization demonstrate the effectiveness of the proposed RL algorithm, which can be applied to automate multiparameter geometric optimization using artificial intelligence systems.

In this study, non-highly compressed iron powders in a ring-shaped sample container are characterized. This approach involves the dimensioning of a non-magnetic ring-shaped container to measure the magnetic properties of ferromagnetic powders with different bulk densities. Therefore, an optimization of the container’s dimensions is presented. Finally, the precise data acquisition systems for determining the material’s B-H characteristics and loss curves in relevant operating points are discussed and first measuring results for several distinct material densities are presented.

End-of-line tests and defect detection are vital for ensuring the reliability of electric motors. However, automated defect detection methods, e.g., data-driven approaches, face challenges due to the limited availability of real data from failed motors. Simulated data, though beneficial, lacks the complexity of real motors, impacting the performance of these methods when applied to actual observations. To tackle this challenge, we introduce a visual analysis tool designed to facilitate the analysis of measured and simulated data, presented in the form of time series data. This tool helps identify domain-invariant features and evaluate simulation data accuracy, assisting in selecting training data for reliable automated defect detection in real-world scenarios. The main contribution of this work is a design proposal based on visual design principles, specifically tailored to address the unique requirements of electric motor professionals. The visual design is validated by findings from a think-aloud study with specialized engineers

Magnetic gears (MGs) emerged as an interesting alternative to conventional mechanical gears, owing mainly to their high torque densities and contactless operation. This paper presents a novel observer-based position control system for a magnetic-geared servo drive. The presented control system is based on two well established control strategies—field-oriented control (FOC) and state feedback control. The former is used to achieve effective torque control of a permanent magnet synchronous motor (PMSM) which is considered as an actuator that drives the high-speed rotor, whereas the latter is used to control the position of the low-speed rotor. A reduced-order extended state observer is used to estimate the position and speed of the low-speed rotor, thereby reducing the number of sensors required for the implementation of the controller. The whole control system is implemented on a microcontroller and tested on an existing prototype with a gear ratio of 18:1. The experimental results show that the presented control system guarantees precise positioning within a short amount of time and excellent disturbance rejection.

Surrogate model (SM)-based optimization approaches have gained significant attention in recent years due to their ability to find optimal solutions faster than finite element (FE)-based methods. However, there is limited previous literature available on the detailed process of constructing SM-based approaches for multi-parameter, multi-objective design optimization of electric machines. This paper aims to present a systematic design optimization process for an interior permanent magnet synchronous machine (IPMSM), including a thorough examination of the construction of the SM and the adjustment of its parameters, which are crucial for reducing computation time. The performances of SM candidates such as Kriging, artificial neural networks (ANNs), and support vector regression (SVR) are analyzed, and it is found that Kriging exhibits relatively better performance. The hyperparameters of each SM are fine-tuned using Bayesian optimization to avoid manual and empirical tuning. In addition, the convergence criteria for determining the number of FE computations needed to construct an SM are discussed in detail. Finally, the validity of the proposed design process is verified by comparing the Pareto fronts obtained from the SM-based and conventional FE-based methods. The results show that the proposed procedure can significantly reduce the total computation time by approximately 93% without sacrificing accuracy compared to the conventional FE-based method.

In the field of electrical machine design, excellent performance for multiple objectives, like efficiency or torque density, can be reached by using contemporary optimization techniques. Unfortunately, highly optimized designs are prone to be rather sensitive regarding uncertainties in the design parameters. This paper introduces an approach to rate the sensitivity of designs with a large number of tolerance-affected parameters using cumulative distribution functions (CDFs) based on finite element analysis results. The accuracy of the CDFs is estimated using the Dvoretzky–Kiefer–Wolfowitz inequality, as well as the bootstrapping method. The advantage of the presented technique is that computational time can be kept low, even for complex problems. As a demanding test case, the effect of imperfect permanent magnets on the cogging torque of a Vernier machine with 192 tolerance-affected parameters is investigated. Results reveal that for this problem, a reliable statement about the robustness can already be made with 1000 finite element calculations.

This paper deals with the analysis and optimization of radial, coaxial magnetic gears. Multi-objective optimizations of the modulator shape of two magnetic gear configurations, one with and one without back iron on the outer rotor, are presented and discussed. The focus of this publication is to show that a magnetic gear with the same total volume and output torque can provide a much higher efficiency when the outer rotor is built with no back iron. To guarantee comparable results, equal start geometries, objectives, and appropriate optimization parameters are considered. After the optimization process, the geometries of the two gear configurations show different optimal modulator shapes and, therefore, different flux distributions. The gear configuration without back iron achieves a comparable torque capability in comparison with the gear configuration with iron yoke. However, 2-D finite elements calculations estimate for the latter design a loss reduction of 70%.

When designing passively magnetically stabilized rotor systems, introducing sufficient damping is key. We investigated two different dynamic systems—with one mass and two masses—to determine their theoretical optimal behavior and how they can be realized considering real-world specifications and limitations. Based on dimensionless formulations of reduced dynamic systems, we present fundamental correlations between, and the optimal choice of, physical parameters. Furthermore, we compare the two systems in terms of feasibility and efficiency of different damping methods. Our investigations used an eddy current damper and viscoelastic damping elements as exemplary damping methods for the one-mass and two-mass systems, respectively. Passive stabilization was realized by means of permanent magnetic bearings. We found that the two-mass system is preferable due to the broader range of damping possibilities.