Browsing by Author "Tremmel, Stephan"
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- ItemDesigning amorphous carbon coatings using numerical and experimental methods within a multi-scale approach(2020) Tremmel, Stephan; Marian, Max; Rothammer, Benedict; Weikert, Tim; Wartzack, SandroAmorphous carbon coatings have the potential to effectively reduce friction and wear in tribotechnical systems. The appropriate application of amorphous carbon layers requires both, a very good understanding of the tribological system and knowledge of the relationships between the fabrication of the coatings and their properties. In technical practice, however, the coatings’ development and their selection on the one hand and the design of the tribological system and its environment on the other hand are usually very strongly separated. The present work therefore aims to motivate the integrated development of tribotechnical systems with early consideration of the potential of amorphous carbon coatings. An efficient integrated development process is presented, which makes it possible to determine the boundary conditions and the load collective of the tribological system based upon an overall system and to derive the requirements for a tailored coating. In line with the nature of tribology, this approach must cover several scales. In this respect, the development process follows a V-model. The left branch of the V-model is mainly based upon a simulation chain including multibody and contact simulations. The right branch defines an experimental test chain comprising coating characterization to refine the contact simulation iteratively and tribological testing on different levels to validate the function fulfillment. Within this contribution, the outlined approach is illustrated by two use cases, namely the cam/tappet-pairing and the total knee replacement.
- ItemEvaluation of DLC, MoS2, and Ti3C2T thin films for triboelectric nanogenerators(2022) Tremmel, Stephan; Luo, Xiongxin; Rothammer, Benedict; Seynstahl, Armin; Wang, Bo; Rosenkranz, Andreas; Marian, Max|Zhu, LaipanDue to their cost-effective fabrication, easy integration, and low frequency working range, triboelectric nanogenerators (TENGs) demonstrate tremendous potential in green energy harvesting to power smart devices and the internet of things (IoT). However, there is an urgent need to synergistically maximize their output and improve their durability to ensure a long-lasting high performance. This study aims at elucidating the performance of protective thin films deposited on the wear-prone PTFE surface of TENGs including doped and undoped, single- and multi-layer hydrogenated DLC films, MoS2 coatings fabricated by physical vapor deposition and multi-layer Ti3C2Tx (MXene) films. The deposited coatings are characterized by electron microscopy, and Raman spectroscopy. Their triboelectric performance is analyzed for TENGs operating in contact separation and freestanding sliding modes. We verified that MXenes outperformed the other films in contact separation mode due to the good electron gain ability of functional oxygen and fluorine groups. In sliding mode, the undoped a-C:H coating performed on a comparable level to the uncoated reference and superior to the tungsten-doped DLC and MoS2 films. The film withstood long-term tests without notable signs of wear; merely the output slowly decreased with time due to graphitization and thus potential material transfer to the mating body.
- ItemEvaluation of the surface fatigue behavior of amorphous carbon coatings through cyclic nanoindentation(2021) Weikert, Tim; Wartzack, Sandro; Baloglu, Maximiliano V.; Willner, Kai; Gabel, Stefan; Merle, Benoit; Pineda, Fabiola; Walczak, Magdalena; Marian, Max; Rosenkranz, Andreas; Tremmel, StephanDiamond-like carbon (DLC) coatings, frequently used to reduce wear and friction in machine components as well as on forming tools, are often subjected to cyclic loading. Doping of DLC coatings with metals or metal carbides as well as the usage of multilayer architectures represent promising approaches to enhance toughness, which is beneficial for the coatings' behavior under cyclic loading. In this study, we utilized cyclic nanoindentation to characterize the tribologically induced surface fatigue behavior of single-layer tungsten-doped (a-C:H:W) and multilayer silicon oxide containing (a-C:H:Si:O/a-C:H)25 amorphous carbon coatings under cyclic loading. Columnar growth was observed for both coatings by focused ion beam microscopy and scanning electron microscopy, while the multilayer architecture of the (a-C:H:Si:O/a-C:H)25 coating was verified by the silicon content using glow-discharge optical emission spectroscopy. In cyclic nanoindentation of the (a-C:H:Si:O/a-C:H)25 multilayer coating, stepwise small changes in indentation depth were observed over several indentation cycles. The surface fatigue process of the single-layer a-C:H:W covered a smaller number of indentation cycles and was characterized by an early steep increase of the static displacement signal. Microscopical analyses hint at grain deformation, sliding at columnar boundaries, and grain detachment as underlying fatigue mechanisms of the a-C:H:W coating, while the (a-C:H:Si:O/a-C:H)25 multilayer coating showed transgranular crack propagation and gradual fracturing. In case of the (a-C:H:Si:O/a-C:H)25 multilayer coating, superior indentation hardness (HIT) and indentation modulus (EIT) as well as a higher HIT3/EIT2 ratio suggest a higher resistance to plastic deformation. A high HIT3/EIT2 ratio, being an indicator for hindered crack initiation, combined with the capability of stress relaxation in soft layers contributed to the favorable surface fatigue behavior of the (a-C:H:Si:O/a-C:H)25 multilayer coating observed in this cyclic nanoindentation studies
- ItemMachine Learning in Tribology-More than Buzzwords?(2022) Tremmel, Stephan; Marian, Max
- ItemPhysics-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.
- ItemRecent Advances in Machine Learning in Tribology(2024) Marian, Max; Tremmel, Stephan