Browsing by Author "Marian, Max"

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    Additively manufactured 316L steel reinforced by multi-layer Ti3C2Tx for enhanced mechanical and bio-tribological performance
    (2025) Ramteke, R Sangharatna Munneshwar; Ramos Grez, Jorge; Rosenkranz, Andreas ; Marian, Max
    Implant materials often suffer from wear, surface degradation, and poor biocompatibility, leading to reduced durability and compromised patient outcomes. Addressing these challenges requires the development of advanced biomaterials with enhanced mechanical strength and bio-tribological performance. In this context, we explore the incorporation of multi-layer Ti3C2Tx into a 316L metal matrix to enhance mechanical and biotribological properties for biomedical applications. Metal matrix composites (MMCs) with 1, 2, and 3 wt.-% Ti3C2Tx were fabricated using laser powder bed fusion (LPBF). Mechanical properties, including surface roughness and hardness, and the bio-tribological behavior were evaluated under dry and synovial body fluid (SBF)-lubricated conditions at 37 ◦C. Lower Ti3C2Tx concentrations yielded smoother surfaces, while higher concentrations increased roughness due to particle agglomeration and clustering. However, the resulting hardness improved especially for an addition of 3 wt.-% Ti3C2Tx. The 1 wt.-% Ti3C2Tx MMCs reduced wear by 31 and 19 % under dry and SBF conditions, respectively, while balls wear (counter-bodies) were reduced by 51 and 13%, respectively. These results highlight the potential of multi-layer Ti3C2Tx to improve the durability and performance of medical devices, demonstrating their promise as advanced biomaterials.
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    Ensemble Deep Learning for Wear Particle Image Analysis
    (Multidisciplinary Digital Publishing Institute (MDPI), 2023) Shah R.; Sridharan N.V.; Mahanta T.K.; Muniyappa A.; Vaithiyanathan S.; Ramteke, Sangharatna M.; Marian, Max
    This technical note focuses on the application of deep learning techniques in the area of lubrication technology and tribology. This paper introduces a novel approach by employing deep learning methodologies to extract features from scanning electron microscopy (SEM) images, which depict wear particles obtained through the extraction and filtration of lubricating oil from a 4-stroke petrol internal combustion engine following varied travel distances. Specifically, this work postulates that the amalgamation of ensemble deep learning, involving the combination of multiple deep learning models, leads to greater accuracy compared to individually trained techniques. To substantiate this hypothesis, a fusion of deep learning methods is implemented, featuring deep convolutional neural network (CNN) architectures including Xception, Inception V3, and MobileNet V2. Through individualized training of each model, accuracies reached 85.93% for MobileNet V2 and 93.75% for Inception V3 and Xception. The major finding of this study is the hybrid ensemble deep learning model, which displayed a superior accuracy of 98.75%. This outcome not only surpasses the performance of the singularly trained models, but also substantiates the viability of the proposed hypothesis. This technical note highlights the effectiveness of utilizing ensemble deep learning methods for extracting wear particle features from SEM images. The demonstrated achievements of the hybrid model strongly support its adoption to improve predictive analytics and gain insights into intricate wear mechanisms across various engineering applications.
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    Optimisation of radiographic visibility and wear detection of total knee arthroplasty inlays using radiopaque markers
    (2025) Emonde, Crystal Kayaro ; Eggers, Max-Enno ; Heide, Klaas Maximilian; Pape, Florian ; Marian, Max; Hurschler, Christof ; Ettinger, Max ; Denkena, Berend
    Wear of the inlay in total knee arthroplasty (TKA) contributes to implant failure and the need for revision surgery. In vivo wear assessment is challenging owing to the radiolucency of the inlay in standard radiographs. This study aimed to investigate the basic feasibility of integrating radiopaque X-ray markers on standard inlays to enhance their radiographic visibility and enable wear evaluation.Preliminary experiments identified optimal process parameters for micro-milling cavities on ultra-high molecular weight polyethylene (UHMWPE). A total of 450 parameter combinations were evaluated, with burr formation serving as the quality criterion. A process chain, comprising pre-contouring, micro-milling, filling cavities with radiopaque composite, and final contouring, was developed for inlay production. Eleven inlays with varying marker alignments, orientations, and geometries were manufactured, featuring grooves (≤0.8 mm wide) and holes (diameter = 1.6 mm), both 1 mm deep. Three HDPE + BaSO₄ composites (10, 20, and 30 wt.% BaSO₄) were formulated and assessed for radiopacity per ASTM F640-20. Final marker cavities were filled with HDPE + 20 wt.% BaSO₄ via pellet extrusion. The inlays were positioned in a phantom knee setup and radiographed in the anteroposterior view. Projected markers were evaluated based on edge visibility, measurability, homogeneity, and obscuration by the implant.None of the parameter combinations resulted in burr-free cavities, indicating an unstable five-axis process. X-ray images revealed that grooves aligned in the X-ray direction and drilled holes exhibited the best visibility for wear markers. Pin-on-plate tribological experiments revealed that BaSO₄ addition to pure HDPE reduced its CoF from 0.25 to 0.1, reaching a value comparable to UHMWPE (0.15), while also enhancing wear resistance.This study demonstrated the feasibility of integrating wear markers on standard TKA inlays by micro-milling cavities at different positions and orientations on the inlay surface and filling them with a radiopaque composite. Further research is required to optimise process parameters and investigate marker wear.
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    Physics-Informed Machine Learning—An Emerging Trend in Tribology
    (Multidisciplinary Digital Publishing Institute (MDPI), 2023) Marian, Max; Tremmel, Stephan
    © 2023 by the authors.Physics-informed machine learning (PIML) has gained significant attention in various scientific fields and is now emerging in the area of tribology. By integrating physics-based knowledge into machine learning models, PIML offers a powerful tool for understanding and optimizing phenomena related to friction, wear, and lubrication. Traditional machine learning approaches often rely solely on data-driven techniques, lacking the incorporation of fundamental physics. However, PIML approaches, for example, Physics-Informed Neural Networks (PINNs), leverage the known physical laws and equations to guide the learning process, leading to more accurate, interpretable and transferable models. PIML can be applied to various tribological tasks, such as the prediction of lubrication conditions in hydrodynamic contacts or the prediction of wear or damages in tribo-technical systems. This review primarily aims to introduce and highlight some of the recent advances of employing PIML in tribological research, thus providing a foundation and inspiration for researchers and R&D engineers in the search of artificial intelligence (AI) and machine learning (ML) approaches and strategies for their respective problems and challenges. Furthermore, we consider this review to be of interest for data scientists and AI/ML experts seeking potential areas of applications for their novel and cutting-edge approaches and methods.