A5078823953- Microstructure and Mechanical Properties of Steels
- Manufacturing Process and Optimization
- Metal Alloys Wear and Properties
- Additive Manufacturing and 3D Printing Technologies
- Metallic Glasses and Amorphous Alloys
- Additive Manufacturing Materials and Processes
- Hydrogen embrittlement and corrosion behaviors in metals
- Digital Transformation in Industry
- Force Microscopy Techniques and Applications
- Flexible and Reconfigurable Manufacturing Systems
- Machine Learning in Materials Science
- Magnetic properties of thin films
- Metallurgy and Material Forming
- Magnetic Properties and Applications
- Advanced machining processes and optimization
- Microstructure and mechanical properties
- Aluminum Alloy Microstructure Properties
- Integrated Circuits and Semiconductor Failure Analysis
- Metallurgical Processes and Thermodynamics
- Advanced Electron Microscopy Techniques and Applications
- Solidification and crystal growth phenomena
- Injection Molding Process and Properties
- Cultural Heritage Materials Analysis
- Advanced Surface Polishing Techniques
- Magnetic Properties of Alloys
RWTH Aachen University
2020-2025
Salzgitter Group (Germany)
2025
Mannesmann (Germany)
2025
FH Aachen
2022
Tempus Labs (United States)
2021
Saarland University
1982-1986
Developing novel alloys for 3D printing of metals is a time- and resource-intensive challenge. High-throughput material characterization protocols are used in this work to rapidly screen wide range chemical compositions processing conditions. In situ, alloying high-strength steel with pure Al the targeted 0-10 wt.% flexible adjustment volumetric energy input performed derive 20 individual alloy combinations. These conditions characterized using large-area crystallographic analysis combined...
The transformation behavior of medium manganese steels after forging has been characterized using dilatometry in order to investigate the influence alloying elements. alloys contain 4 wt.–% manganese, 0.5 silicon, 0.035 niobium and varying amounts carbon (0.15–0.19 wt.–%), aluminum (0.025–0.5 molybdenum (0.02–0.2 titanium (0–0.02 boron (0–0.06 wt.–%). continuous cooling (CCT) diagrams a multiple linear regression (MLR) formula for calculating martensite start (Ms) temperature is...
This study investigates the high‐cycle‐fatigue (HCF) behavior of carbide‐bearing bainite (CBB) and carbide‐free (CFB) fabricated at different transformation temperatures. The fatigue limit each material is determined via staircase method using a 1 kHz resonant testing machine. A new load increase test proposed as an efficient alternative to estimate in HCF regimes. assessment accompanied by data‐driven microstructural analyses state‐of‐the‐art computer vision tools. reveal that finer carbide...
Microstructures in steel can be understood as hierarchical structures holding information on various length scales (e.g. from nano to macro scale). In electron microscopy images, the microscope resolution allows extraction of meso-scale grain interiors and boundaries encrypted morphological features. So far, experts design experiments extract select important microstructural features based their experience evidence literature. These often serve indicator for interpretation corresponding...
Abstract Data streams in science and economy are becoming increasingly automatized. This has various advantages compared to previous, user-dependent analyses, which the same results analyzed differently by different persons. Even though these differences only of a certain degree, they can lead false estimations underlying material process parameters as well missing comparability. In order automatize previously processes analysis tests, modular database management system, called idCarl, been...
Abstract Studying steel microstructures yields important insights regarding its mechanical characteristics. Within steel, transform based on a multitude of factors including chemical composition, transformation temperatures, and cooling rates. Martensite-austenite (MA) islands in bainitic appear as blocky structures with abstract shapes that are difficult to identify differentiate from other types microstructures. In this regard, material science may benefit machine learning models able...
The high-temperature austenite phase is the initial state of practically all technologically relevant hot forming and heat treatment operations in steel processing. phenomena occurring austenite, such as recrystallization or grain growth, can have a decisive influence on subsequent properties material. After process, however, transforms into other microstructural constituents information prior morphology are no longer directly accessible. There established methods available for...
Hot‐rolled wire is often further processed into complex components and therefore has to meet high demands on mechanical properties. Above all, during production, close control of the cooling parameters after hot rolling required if strength toughness must be set within narrow limits. Parameter studies are conducted in laboratory investigations adjust bainitic microstructures, that consist ferrite as primary phase, whereas retained austenite films martensite–austenite (M–A) constituents...
The microstructural description in bainitic steel is commonly ambiguous, and the interpretations of results that originate from applied methods are usually user dependent. In consequence, a manifold bainite makes it difficult to reveal structure–property relationships. This why novel classification quantification routine for microstructures wire rod presented. based on electron probe microanalysis (EPMA), backscatter diffraction (EBSD), nanohardness same local area. Microstructural...
Bainitic steels are extensively utilized across various sectors, such as the automotive and railway industries, owing to their impressive mechanical properties, including strength, hardness, fatigue resistance. However, pursuit of achieving desired optimal properties presents considerable challenges due intricate bainitic microstructures consisting multiple phases. To tackle these challenges, an automated workflow is used for extracting 2D 3D microstructural features. The proposed method...
The use of data-driven methods for metal additive manufacturing (AM) is currently gaining importance as indicated by the increasing number scientific literature in this field. Incorporation has potential to eliminate current bottlenecks microstructure design given diverse and complex nature microstructures additively manufactured metals. So far, coupling existing simulation methods, e.g. physics-based process models, simulate AM with desired morphological characteristics requires extensive...
e13588 Background: Pre-screening for clinical trials is becoming more challenging as inclusion/exclusion criteria becomes increasingly complex. Oncology precision medicine provides an exciting opportunity to simplify this process and quickly match patients with by leveraging machine learning technology. The Tempus TIME Trial site network matches relevant, open, recruiting trials, personalized each patient’s molecular biology. Methods: screens at sites within the Network find high-fidelity...
Microstructures in steel can be understood as hierarchical structures holding information on various length scales (e.g. from nano to macro scale). In electron microscopy images, the microscope resolution allows extraction of meso-scale grain interiors and boundaries encrypted morphological features. So far, experts design experiments extract select important microstructural features based their experience evidence literature. These often serve indicator for interpretation corresponding...