Antriebsstrangprüfstände zur Ableitung von Konstruktionszielgrößen in der Produktentwicklung handgehaltener Power-Tools

In dieser Arbeit werden zwei Antriebsstrangprüfstände zur Ableitung von Konstruktionszielgrößen für Antriebssysteme handgehaltener Power-Tools entwickelt, verifiziert und beispielhafte Zielgrößen für die Konstruktion ermittelt. Durch physische Komponententests unter Einbindung in die Umgebungssysteme wird es möglich, Antriebskomponenten früh im Produktentwicklungsprozess zu untersuchen und aufeinander abzustimmen. Besondere Vorteile bietet dies in frühen Produktentwicklungsphasen, in denen noch nicht alle Komponenten der Maschine funktionsfähig vorliegen. Damit wird das Frontloading in der Produktentwicklung unterstützt und gefördert. Die Arbeit beschreibt das Vorgehen zur Entwicklung zweier Antriebsstrangprüfstände für handgehaltene Power-Tools. Zunächst werden in manuellen Tests mit Hilfe von mit Messtechnik ausgestatteten Maschinen belastungsabhängige Systemgrößen experimentell erfasst und Erkenntnisse zum Systemverhalten in der Anwendung gewonnen. Es erfolgt die Entwicklung eines Prüfstands für Dynamikuntersuchungen an einem Winkelschleiferantriebsstrang und eines Prüfstands für die Untersuchung des Auslöseverhaltens von Überrastkupplungen in Akkubohrschraubern. Für beide Antriebsstrangprüfstände werden Lastmodelle aus den manuellen Tests generiert. Anhand der Anforderungen an die Antriebsstrangprüfstände werden diese verifiziert. Die Ableitung von Konstruktionszielgrößen wird in Experimenten an den Antriebsstrangprüfständen durch beispielhafte Anwendungen gezeigt. Für den Lagersitz eines Winkelschleiferantriebs werden Bauteilvarianten, welche sich in ihrem Elastizitätswert unterscheiden, hinsichtlich dem Schwingungsverhalten des Triebstrangs untersucht und Konstruktionszielgrößen daraus abgeleitet. Für die Überrastkupplung eines Akkubohrschraubers werden Geometrievarianten hinsichtlich deren Einfluss auf das dynamische Verhalten des Triebstrangs untersucht und aus den Erkenntnissen Konstruktionszielgrößen abgeleitet

Frontloading in Aircraft Development Process by Integration of a new Validation Method

In product engineering Frontloading can be supported by the implementation of new methods in a product development process. In this contribution the integration of a new validation method called $\textit{Scaled-Components-in-the-Loop}$ in an aircraft development process is presented. The requirements for the application of the validation method are discussed

New Control Strategy for Heating Portable Fuel Cell Power Systems for Energy-Efficient and Reliable Operation

Using hydrogen fuel cells for power systems, temperature conditions are important for efficient and reliable operations, especially in low-temperature environments. A heating system with an electrical energy buffer is therefore required for reliable operation. There is a research gap in finding an appropriate control strategy regarding energy efficiency and reliable operations for different environmental conditions. This paper investigates heating strategies for the subfreezing start of a fuel cell for portable applications at an early development stage to enable frontloading in product engineering. The strategies were investigated by simulation and experiment. A prototype for such a system was built and tested for subfreezing start-ups and non-subfreezing start-ups. This was done by heating the fuel cell system with different control strategies to test their efficiency. It was found that operating strategies to heat up the fuel cell system can ensure a more reliable and energy-efficient operation. The heating strategy needs to be adjusted according to the ambient conditions, as this influences the required heating energy, efficiency, and reliable operation of the system. A differentiation in the control strategy between subfreezing and non-subfreezing temperatures is recommended due to reliability reasons

Requirements for Sensor Integrating Machine Elements : A Review of Wear and Vibration Characteristics of Gears

For condition monitoring of machines sensor integrating standard machine elements provide advantage in acquiring high-quality, robust data from individual machine elements and reducing effort in signal processing. However, research covering small and inexpensive consumer-grade MEMS sensors with respect to integration and measurement requirements for wear detection is limited. In order to define such requirements, the state of the art of vibration-based condition monitoring of gears is reviewed and summarised. The focus is on the characteristics of progressive wear and how it might show in the vibration signal. The review finds that correlation between wear and vibration characteristics of gears exist, but the interpretation of the vibration signals is challenging and requires purpose-built signal processing methods. The review also concludes that integrated MEMS acceleration sensors are theoretically able to measure the vibration characteristics of gears to detect wear. Important characteristics are the gear mesh acceleration with its frequencies and harmonic multiples (GMFi). Frequency range requirements for the sensors depend on the operating conditions of gears, the upper frequency limit needs to be greater or equal to 1.3 GMF$_{i,max}$. For the measuring range requirements, upper limits of 20 g RMS can be extracted within certain conditions. Data analysis requires a minimum frequency resolution which affects the size of memory needed for an integrated sensor system. However, there is a lack of research whether the sensitivity and internal noise behaviour of available MEMS sensors is good enough to measure relative changes in the vibration signals caused by wear

A New Dynamical Test Bench for Multi-Axial Loading of Angle Grinders

Automated testing with test benches plays a major role in the development of power tools such as angle grinders. Previous test benches for testing the drive train of an angle grinder replace the load from grinding a workpiece by dynamometer or servo motor in rotary direction and by linear motors in radial and axial direction. These can only apply forces up to 10 Hz and thus no speed-dependent force components. The aim of this paper is to develop a test bench for dynamic mechanical loading of the drive train of an angle grinder in the rotational, radial and axial axes up to 200 Hz, which corresponds to the maximum speed of an angle grinder. For this purpose, the modelling of the force application on the test bench and the resulting mechanical design is presented. In addition, the generation of test cases from measurement data of manual tests and the verification of the test bench are presented. Subsequently, a case study is presented to investigate the load pattern on the test bench with multi-axial load compared to pure torque loading on the same test bench and manual tests. It can be seen that the load pattern of the multi-axial load is qualitatively similar that of the load pattern from the manual test. Through using the developed test bench, it will be possible to investigate load patterns as well as wear or vibration of the drive train of an angle grinder

Prediction of tool forces in manual grinding using consumer-grade sensors and machine learning

Tool forces are a decisive parameter for manual grinding with hand-held power tools, which can be used to determine the productivity, quality of the work result, vibration exposition, and tool lifetime. One approach to tool force determination is the prediction of tool forces via measured operating parameters of a hand-held power tool. The problem is that the accuracy of tool force prediction with consumer-grade sensors remains unclear in manual grinding. Therefore, the accuracy of tool force prediction using Gaussian process regression is examined in a study for two hand-held angle grinders in four different applications in three directions using measurement data from an inertial measurement unit, a current sensor, and a voltage sensor. The prediction of the grinding normal force (rMAE = 11.44% and r = 0.84) and the grinding tangential force (rMAE = 18.21% and r = 0.82) for three tested applications, as well as the radial force for the application cutting with a cut-off wheel (rMAE = 19.67% and r = 0.80) is shown to be feasible. The prediction of the guiding force (rMAE = 87.02% and r = 0.37) for three tested applications is only possible to a limited extent. This study supports data acquisition and evaluation of hand-held power tools using consumer-grade sensors, such as an inertial measurement unit, in real-world applications, resulting in new potentials for product use and product development

Sensor-integrating gears: wear detection by in-situ MEMS acceleration sensors [Sensorintegrierende Zahnräder: Verschleißdetektion durch In-situ MEMS Beschleunigungssensoren]

Gear tooth wear is a common phenomenon leading to malfunctions in machines. To detect wear and faults, gear condition monitoring by vibration is established. The problem is that the measurement data quality for detection of wear by vibration is not good enough with currently established measurement methods, caused by long signal paths of the commonly used housing mounted sensors. In-situ sensors directly at the gear achieve better data quality, but are not yet proved in wear detection. Further it is unknown what analysis methods are suited for in-situ sensor data. Existing gear condition metrics are mainly focused on localized gear tooth faults, and do not estimate wear related values. This contribution aims to improve wear detection by investigating in-situ sensors and advance gear condition metrics. Using a gear test rig to conduct an end of life test, the wear detection ability of an in-situ sensor system and reference sensors on the bearing block are compared through standard gear condition metrics. Furthermore, a machine-learned regression model is developed that maps multiple features related to gear dynamics to the gear mass loss. The standard gear metrics used on the in-situ sensor data are able to detect wear, but not significantly better compared to the other sensors. The regression model is able to estimate the actual wear with a high accuracy. Providing a wear related output improves the wear detection by better interpretability