Noninvasive characterization methods for ultra-short laser pulse induced volume modifications

We present two noninvasive characterization methods to investigate laser induced modifications in bulk fused silica glasses. The methods discussed are immersion microscopy and scanning acoustic microscopy (SAM). SAM shows merits in measuring the distance from sample surface to the first detectable density change of the modification, while immersion microscopy offers a look into the modification. Both noninvasive methods are preferred over conventional polishing or etching techniques due to the facts, that multiple investigations can be done with only one sample and lower time expenditure. The type II modifications were introduced by focusing laser pulses with high repetition rates into the fused silica

Vector hysteresis loops of rare earth magnets measured in a biaxial vibrating sample magnetometer

In many permanent magnet applications, there are regions in the magnet volume where the magnetic field acts at large angles to the direction of magnetization. The hysteresis curves, however, are determined almost exclusively with the magnetic fields applied along the preferred axis. This lack of information severely impairs the numerical modeling of important aspects of electrical machines, such as the risk of demagnetization or a reliable prediction whether a rotor can be assembled with unmagnetized magnets and subsequently magnetized in a multipole magnetizing fixture. In the rare cases where measurements with the field applied at an angle are performed, only the magnetization component in the direction of the applied field is detected. A reliable simulation would require measurements of the complete magnetization vector in fields applied at all angles. To this end, a commercially available vibrating sample magnetometer with a superconducting solenoid providing up to ±9 T was modified, replacing the existing detection coils with a twofold coil system measuring two orthogonal components of the magnetization. Details of this modification are reported, along with some examples of vectorial hysteresis curves measured on different types of rare earth magnets

Shape Anisotropy of Grains Formed by Laser Melting of (CoCuFeZr)17Sm2

For permanent magnetic materials, anisotropic microstructures are crucial for maximizing remanence Jr and maximum energy product (BH)max. This also applies to additive manufacturing processes such as laser powder bed fusion (PBF-LB). In PBF-LB processing, the solidification behavior is determined by the crystal structure of the material, the substrate, and the melt-pool morphology, resulting from the laser power PL and scanning speed vs. To study the impact of these parameters on the textured growth of grains in the melt-pool, experiments were conducted using single laser tracks on (CoCuFeZr)17Sm2 sintered magnets. A method was developed to quantify this grain shape anisotropy from electron backscatter diffraction (EBSD) analysis. For all grains in the melt-pool, the grain shape aspect ratio (GSAR) is calculated to distinguish columnar (GSAR 0.5) grains. For columnar grains, the grain shape orientation (GSO) is determined. The GSO represents the preferred growth direction of each grain. This method can also be used to reconstruct the temperature gradients present during solidification in the melt-pool. A dependence of the melt-pool aspect ratio (depth/width) on energy input was observed, where increasing energy input (increasing PL, decreasing vs) led to higher aspect ratios. For aspect ratios around 0.3, an optimum for directional columnar growth (93% area fraction) with predominantly vertical growth direction (mean angular deviation of 23.1◦ from vertical) was observed. The resulting crystallographic orientation is beyond the scope of this publication and will be investigated in future work

The Microstructure and Magnetic Properties of a Soft Magnetic Fe-12Al Alloy Additively Manufactured via Laser Powder Bed Fusion (L-PBF)

Soft magnetic Fe-Al alloys have been a subject of research in the past. However, they never saw the same reception in technical applications as the Fe-Si or Fe-Ni alloys, which is, to some extent, due to a low ductility level and difficulties in manufacturing. Additive manufacturing (AM) technology could be a way to avoid issues in conventional manufacturing and produce soft magnetic components from these alloys, as has already been shown with similarly brittle Fe-Si alloys. While AM has already been applied to certain Fe-Al alloys, no magnetic properties of AM Fe-Al alloys have been reported in the literature so far. Therefore, in this work, a Fe-12Al alloy was additively manufactured through laser powder bed fusion (L-PBF) and characterized regarding its microstructure and magnetic properties. A comparison was made with the materials produced by casting and rolling, prepared from melts with an identical chemical composition. In order to improve the magnetic properties, a heat treatment at a higher temperature (1300 °C) than typically applied for conventionally manufactured materials (850–1150 °C) is proposed for the AM material. The specially heat-treated AM material reached values (HC: 11.3 A/m; µmax: 13.1 × 103) that were close to the heat-treated cast material (HC: 12.4 A/m; µmax: 20.3 × 103). While the DC magnetic values of hot- and cold-rolled materials (HC: 3.2 to 4.1 A/m; µmax: 36.6 to 40.4 × 103) were not met, the AM material actually showed fewer losses than the rolled material under AC conditions. One explanation for this effect can be domain refinement effects. This study shows that it is possible to additively manufacture Fe-Al alloys with good soft magnetic behavior. With optimized manufacturing and post-processing, further improvements of the magnetic properties of AM L-PBF Fe-12Al may still be possible

Deep learning and correlative microscopy for quantification of grain orientation in sintered FeNdB-type permanent magnets by domain pattern analysis

Based on a data-driven approach, a computer-assisted workflow for the quantitative analysis of optical Kerr microscopy images of sintered FeNdB-type permanent magnets was developed. By analyzing the domain patterns visible in the Kerr image with data-driven approaches such as traditional machine learning and advanced deep learning, we can quantify grain orientation and size with a better trade-off between accuracy and higher throughput than electron backscatter diffraction (EBSD). The key distinction between traditional machine learning and advanced deep learning lies in feature extraction. Traditional methods require manual, user-dependent feature extraction from input data, while advanced deep learning achieves this automatically. The predictions from the trained models were compared to the measurements from EBSD for performance evaluation. The proposed data-driven model is trained on the dataset created from the correlative microscopy technique, which requires the images of grains extracted from the Kerr microscopy and corresponding EBSD grain orientation data (Euler angles). The fine-tuned deep learning model shows better generalization ability than the traditional machine learning models trained on the manually extracted features and resulted in a mean absolute error of less than 5° for grain orientation of the anisotropic magnet samples when evaluated against the measured EBSD values. The developed approach has reduced the measurement effort for grain orientation by 5 times and have sufficient accuracy when compared to the EBSD

SensAA—Design and Verification of a Cloud-Based Wearable Biomechanical Data Acquisition System

Exoskeletons designed to assist patients with activities of daily living are becoming increasingly popular, but still are subject to research. In order to gather requirements for the design of such systems, long-term gait observation of the patients over the course of multiple days in an environment of daily living are required. In this paper a wearable all-in-one data acquisition system for collecting and storing biomechanical data in everyday life is proposed. The system is designed to be cost efficient and easy to use, using off-the-shelf components and a cloud server system for centralized data storage. The measurement accuracy of the system was verified, by measuring the angle of the human knee joint at walking speeds between 3 and 12 km/h in reference to an optical motion analysis system. The acquired data were uploaded to a cloud database via a smartphone application. Verification results showed that the proposed toolchain works as desired. The system reached an RMSE from 2.9◦ to 8◦, which is below that of most comparable systems. The system provides a powerful, scalable platform for collecting and processing biomechanical data, which can help to automize the generation of an extensive database for human kinematics

Optimization of Practicality for Modeling- and Machine Learning-Based Framework for Early Fault Detection of Induction Motors

This paper addresses the further development and optimization of a modeling- and machine learning-based framework for early fault detection and diagnosis in induction motors. The goal behind the multi-level framework is to provide a pragmatic and practical approach for the autonomous monitoring of electrical machines in various industrial applications. The main contributions of this paper include the elimination of a fingerprint measurement in the processing of the framework and the development of a generalized model for fault detection and diagnosis. These aspects allow the training of neural networks with a simulated data set before even knowing the specific induction motor to be monitored. The pre-trained feed-forward neural networks enable the detection of several electrical and mechanical faults in a real induction motor with an overall accuracy of 99.56%. Another main contribution is the extension of the methodology to a larger operating range.As a result, various faults in a real induction motor can be detected under different load conditions with accuracies of over 92%. As a further part of the paper, a concept for a prototype is presented, which enables the autonomous and practice-friendly application of the framework

VR-SDLC

As systems grow in complexity, so does their associated lifecycle and with it the need to manage the various elements, relations, and activities involved in the Software (or Systems) Development Life Cycle (SDLC). Various notations for system, software, or process modeling have been specified such as the Systems Modeling Language (SysML), Unified Modeling Language (UML), Business Process Model and Notation (BPMN) respectively, yet due to their two-dimensional (2D) diagram focus, they are ill suited for visualizing a comprehensive contextualized view of the entire systems engineering or software engineering lifecycle. To address this need, the Lifecycle Modeling Language (LML) utilizes a relatively simple ontology and three primary diagrams while supporting extensibility. Yet lifecycle comprehension, analysis, collaboration, and contextual insights remain constrained by current 2D limitations. This paper contributes our Virtual Reality (VR) solution concept VR-SDLC for holistic visualization of SDLC elements, relations, and diagrams. Our prototype implementation utilizing LML demonstrates its feasibility, while a case study exhibits its potential

Swift Prediction of Battery Performance

In this study, we investigate the use of artificial neural networks as a potentially efficient method to determine the rate capability of electrodes for lithium-ion batteries with different porosities. The performance of a lithium-ion battery is, to a large extent, determined by the microstructure (i.e., layer thickness and porosity) of its electrodes. Tailoring the microstructure to a specific application is a crucial process in battery development. However, unravelling the complex correlations between microstructure and rate performance using either experiments or simulations is time-consuming and costly. Our approach provides a swift method for predicting the rate capability of battery electrodes by using machine learning on microstructural images of electrode cross-sections. We train multiple models in order to predict the specific capacity based on the batteries’ microstructure and investigate the decisive parts of the microstructure through the use of explainable artificial intelligence (XAI) methods. Our study shows that even comparably small neural network architectures are capable of providing state-of-the-art prediction results. In addition to this, our XAI studies demonstrate that the models are using understandable human features while ignoring present artefacts

Material aspects of sintering of EAC-1A lunar regolith simulant

Future lunar exploration will be based on in-situ resource utilization (ISRU) techniques. The most abundant raw material on the Moon is lunar regolith, which, however, is very scarce on Earth, making the study of simulants a necessity. The objective of this study is to characterize and investigate the sintering behavior of EAC-1A lunar regolith simulant. The characterization of the simulant included the determination of the phase assemblage, characteristic temperatures determination and water content analysis. The results are discussed in the context of sintering experiments of EAC-1A simulant, which showed that the material can be sintered to a relative density close to 90%, but only within a very narrow range of temperatures (20–30 °C). Sintering experiments were performed for sieved and unsieved, as well as for dried and non-dried specimens of EAC-1A. In addition, an analysis of the densification and mechanical properties of the sintered specimens was done. The sintering experiments at different temperatures showed that the finest fraction of sieved simulant can reach a higher maximum sintering temperature, and consequently a higher densification and biaxial strength. The non-dried powder exhibited higher densification and biaxial strength after sintering compared to the dried specimen. This difference was explained with a higher green density of the non-dried powder during pressing, rather than due to an actual influence on the sintering mechanism. Nevertheless, drying the powder prior to sintering is important to avoid the overestimation of the strength of specimens to be fabricated on the Moon