@article{ErtugrulEmdadiHaertel2025, author = {Ertugrul, G{\"o}khan and Emdadi, Aliakbar and H{\"a}rtel, Sebastian}, title = {Powder production and additive manufacturing of iron aluminide alloys using plasma ultrasonic atomization and laser-directed energy deposition}, series = {Additive Manufacturing Letters}, volume = {14}, journal = {Additive Manufacturing Letters}, publisher = {Elsevier}, address = {Amsterdam}, issn = {2772-3690}, doi = {10.1016/j.addlet.2025.100313}, year = {2025}, abstract = {With a combination of desirable properties such as low density, high specific yield strength, low material cost, and excellent oxidation and corrosion resistance, iron aluminide (Fe-Al) has shown considerable potential to be an alternative to high-alloy chromium steels, and in some cases even nickel-based superalloys, in hightemperature applications. Due to these features, it is especially suitable for the aerospace and automotive industries. Recent advancements indicate an increasing interest in Fe-Al within the additive manufacturing industry, particularly in directed energy deposition (DED) processes. Despite this progress, processing of Fe-Al materials using the laser directed energy deposition (L-DED) has not been sufficiently investigated. In this study, Fe-Al powder material was produced from a commercial Al rod encased in a commercial low alloy-steel tube by a plasma-based ultrasonic atomization eliminating the need to cast an alloy ingot in advance. Subsequently, the produced powder was used in a L-DED process to fabricate an additively manufactured sample. The sample was investigated in terms of mechanical property, microstructure, chemical composition, and phase structure by scanning electron microscope (SEM) / energy dispersive X-ray spectroscopy (EDX), X-ray diffraction (XRD), electron backscatter diffraction (EBSD) and microhardness analyses.}, subject = {Iron aluminides (Fe-Al); Laser directed energy deposition (L-DED); Plasma ultrasonic atomization; Powder; Intermetallic phase}, language = {en} } @article{ErtugrulEmdadiJedynaketal.2025, author = {Ertugrul, G{\"o}khan and Emdadi, Aliakbar and Jedynak, Angelika and Weiß, Sabine and H{\"a}rtel, Sebastian}, title = {Hot forming behavior of tungsten carbide reinforced Ni-based superalloy 625 additively manufactured by laser directed energy deposition}, series = {Additive Manufacturing Letters}, volume = {13}, journal = {Additive Manufacturing Letters}, publisher = {Elsevier}, address = {Amsterdam}, issn = {2772-3690}, doi = {10.1016/j.addlet.2025.100267}, year = {2025}, abstract = {The demands of high-performance industries such as aerospace, automotive, tool manufacturing, oil, and gas industries are driving the innovation in high-performance materials and their production methods. This study explores the impact of hybrid manufacturing, specifically the effect of the addition of tungsten carbide (WC/W2C) via Laser-Directed Energy Deposition (L-DED), on the hot workability, hardness, and microstructure of nickel-based superalloy Inconel 625 (IN625). IN625 is known for its high temperature and high corrosion resistance, and tungsten carbide for its high wear resistance and grain refinement effect. The integration of WC/W2C particles into the IN625 matrix, in addition to the use of the hybrid approach of additive manufacturing followed by a hot-forming process, significantly influences the microstructure and mechanical behavior of the material. Thus, while incorporation of the WC/W2C can strengthen the material and extend the mechanical limitations, its full impact, including any potential usages, should be thoroughly evaluated for the intended application of the materials. To understand the effect of WC/W2C, additive manufacturing of IN625 both with and without WC/W2C and isothermal hot compression was carried out. The objective is to analyze the differences in microstructure and properties between L-DED manufactured IN625, and WC-reinforced IN625, and their hot-forming behavior, focusing on the effects of WC addition and post-deformation on microstructure and mechanical properties. This work represents the first investigation into the effect of WC/W2C hard particles on the hot-forming process of additively manufactured Ni-based metal matrix composites.}, subject = {Hybrid manufacturing; Laser-directed energy deposition (L-DED); Hot-forming; Inconel 625 (In625); Microstructure manipulation}, language = {en} } @phdthesis{Babel2025, author = {Babel, Christoph Josef}, title = {Virtual evaluation of car body panel surfaces}, doi = {10.26127/BTUOpen-7196}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:co1-opus4-71960}, school = {BTU Cottbus - Senftenberg}, year = {2025}, abstract = {The progression of the automotive industry has introduced a slew of complex designs to meet the personalized style expressions of consumers. However, these intricate designs in premium automobiles present significant manufacturing challenges, especially in detecting and prioritizing the varying severities of cosmetic surface defects. Traditionally, defect identification is performed at the end of the development phase, which is a costly and time-consuming process. This thesis seeks to enhance early defect detection efficiency in the concept phase using artificial intelligence (AI). Through examination of unpainted parts, auditors' perceptual practices, and deep draw simulations, the research aims to establish a virtual prediction method to accurately classify surface defects. The findings of this thesis prove that the geometry of unpainted parts sufficiently predicts potential defects post-painting, refuting the need for physical painted part analysis. The identification process adopted by auditors, grounded in analyzing light distortions on surfaces, is replicated within a neural network. Furthermore, by incorporating physiological optical aspects, the thesis improves the virtual representation of defects, creating a visible and analyzable dataset for AI algorithms. The accuracy of the neural networks trained on such datasets is substantiated by the successful translation of auditors' classification methods into a machine learning environment. In particular, a machine learning approach using Random Forest (RF) algorithms excelled in prefiltering areas of interest based on curvature and strain values. A convolutional recurrent neural network (CRNN) is developed to classify the severity of defects, with the introduction of an annotation application to label data by experts. The CRNN demonstrated a remarkable accuracy of 86 \% in classifying defect severity based on simulation data. Moreover, the study assessed the frame-by-frame localization of defects, where the PatchCore anomaly detection algorithm proves optimal, achieving an F1 Score of 0.86. Overall, the research successfully shows the transferability of expert auditors' perception into a neural network architecture, highlighting the feasibility of implementing AI in early phases of automotive design to predict and classify defects, which has the potential to significantly reduce development costs and improve manufacturing efficiency.}, subject = {Computer vision; Neural network; Deep draw simulation; Rendering; K{\"u}nstliche Intelligenz; Maschinelles Lernen; Umformsimulation; Bildgenerierung; Kraftfahrzeugindustrie; Produktentwicklung; Tiefziehen; Fehlererkennung; Simulation; K{\"u}nstliche Intelligenz; Maschinelles Lernen}, language = {en} }