Browsing by Author "Ramteke, Sangharatna M."

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    2D materials for Tribo-corrosion and -oxidation protection: a review
    (Elsevier B.V., 2024) Ramteke, Sangharatna M.; Walczak, Magdalena; De Stefano, Marco; Ruggiero, Alessandro; Rosenkranz, Andreas; Marian, Max
    The recent rise of 2D materials has extended the opportunities of tuning a variety of properties. Tribo-corrosion, the complex synergy between mechanical wear and chemical corrosion, poses significant challenges across numerous industries where materials are subjected to both tribological stressing and corrosive environments. This intricate interplay often leads to accelerated material degradation and failure. This review critically assesses the current state of utilizing 2D nanomaterials to enhance tribo-corrosion and -oxidation behavior. The paper summarizes the fundamental knowledge about tribo-corrosion and -oxidation mechanisms before assessing the key contributions of 2D materials, including graphene, transition metal chalcogenides, hexagonal boron nitride, MXenes, and black phosphorous, regarding the resulting friction and wear behavior. The protective roles of these nanomaterials against corrosion and oxidation are investigated, highlighting their potential in mitigating material degradation. Furthermore, we delve into the nuanced interplay between mechanical and corrosive factors in the specific application of 2D materials for tribo-corrosion and -oxidation protection. The synthesis of key findings underscores the advancements achieved through integrating 2D nanomaterials. An outlook for future research directions is provided, identifying unexplored avenues, and proposing strategies to propel the field forward. This analysis aims at guiding future investigations and developments at the dynamic intersection of 2D nanomaterials, tribo-corrosion, and -oxidation protection.
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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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    Influence of micro-texture radial depth variations on the tribological and vibration characteristics of rolling bearings under starved lubrication
    (2024) Long, Risheng; Sun, Yuhao; Zhang, Yimin; Shang, Qingyu; Ramteke, Sangharatna M.; Marian, Max
    Surface micro-texturing involves creating microscopic pits or textures, serving as lubricant reservoirs to enhance lubrication distribution, potentially reducing friction and wear. This study specifically delves into the influence of varied depth patterns of pits on the operational performance of textured rolling bearings under severe lubrication conditions. Five distinct patterns with a fixed pit diameter (250 mu m) and different depth variations (concave, decreasing, increasing, convex, and horizontal) were introduced on the shaft rings of cylindrical roller thrust bearings using the laser marking method. Wear tests were conducted under starved lubrication condition. Wear loss and signal analysis highlight the profound effect of depth variations, whereby bearings with shallow pits near the outer side of their working surfaces exhibit longer lubrication times, improved tribological performance, and enhanced vibration characteristics. Notably, the convex pattern stands out for providing comprehensive and favorable tribological and vibration properties. This research contributes valuable insights for the optimal design of micro-textures for rolling bearings, paving the way for enhanced efficiency and reliability in mechanical systems.
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    Prediction of air compressor faults with feature fusion and machine learning
    (2024) Nambiar, Abhay; Venkatesh, S. Naveen; Aravinth, S.; Sugumaran, V; Ramteke, Sangharatna M.; Marian, Max
    Air compressors are critical for many industries, but early detection of faults is crucial for keeping them running smoothly and minimizing maintenance costs. This contribution investigates the use of predictive machine learning models and feature fusion to diagnose faults in single-acting, single-stage reciprocating air compressors. Vibration signals acquired under healthy and different faulty conditions (inlet valve fluttering, outlet valve fluttering, inlet-outlet valve fluttering, and check valve fault) serve as the study's input data. Diverse features including statistical attributes, histogram data, and auto-regressive moving average (ARMA) coefficients are extracted from the vibration signals. To identify the most relevant features, the J48 decision tree algorithm is employed. Five lazy classifiers viz. k-nearest neighbor (kNN), K-star, local kNN, locally weighted learning (LWL), and random subspace ensemble K-nearest neighbors (RseslibKnn) are then used for fault classification, each applied to the individual feature sets. The classifiers achieve commendable accuracy, ranging from 85.33% (K-star and local kNN) to 96.00% (RseslibKnn) for individual features. However, the true innovation lies in feature fusion. Combining the three feature types, statistical, histogram, and ARMA, significantly improves accuracy. When local kNN is used with fused features, the model achieves a remarkable 100% classification accuracy, demonstrating the effectiveness of this approach for reliable fault diagnosis in air compressors.
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    Ti3C2Tx and Mo2TiC2Tx MXenes as additives in synovial fluids - towards an enhanced biotribological performance of 3D-printed implants
    (2024) Marian, Max; Esteban, Cotty D. Quiroz; Zambrano, Dario F.; Ramteke, Sangharatna M.; Grez, Jorge Ramos; Wyatt, Brian C.; Patenaude, Jacob; Wright, Bethany G.; Anasori, Babak; Rosenkranz, Andreas
    Synovial joints, critical for limb biomechanics, rely on sophisticated lubrication systems to minimize wear. Disruptions, whether from injury or disease, often necessitate joint replacements. While additive manufacturing offers personalized implants, ensuring wear resistance remains a challenge. This study delves into the potential of Ti3C2Tx 3 C 2 T x and Mo2TiC2Tx 2 TiC 2 T x nanosheets in mitigating wear of additively manufactured cobalt-chromium tungsten alloy substrates when incorporated as additives into synovial fluid. The colloidal solutions demonstrate an excellent stability, a crucial factor for reproducible assays and potential clinical applicability. Analysis of contact angles and surface tensions reveals MXene-induced alterations in substrate wettability, while maintaining their general hydrophilic character. Viscosity analysis indicates that MXene addition reduces the dynamic viscosity, particularly at higher concentrations above 5 mg/mL, thus enhancing dispersion and lubrication properties. Friction and wear tests demonstrate a dependency on the MXene concentration, while Ti3C2Tx 3 C 2 T x exhibits stable friction coefficients and up to 77 % wear reduction at 5 mg/mL, which was attributed to the formation of a wear- protecting tribo-film (amorphous carbon and MXene nano-sheets). Our findings suggest that Ti3C2Tx, 3 C 2 T x , when supplied in favorable concentrations, holds promise for reducing wear in biotribological applications, offering avenues for future research into optimizing MXene utilization in load-bearing joint replacements and other biomedical devices.