Prof. Dr. Max Marian
Pontificia Universidad Católica de Chile, Santiago, Región Metropolitana, Chile
'%3e%3cpath%20d='M22.1642%2019.0924C21.7317%2018.068%2021.1041%2017.1375%2020.3163%2016.3527C19.5309%2015.5656%2018.6006%2014.9381%2017.5766%2014.5048C17.5674%2014.5002%2017.5582%2014.4979%2017.5491%2014.4933C18.9774%2013.4616%2019.9059%2011.7811%2019.9059%209.88504C19.9059%206.74408%2017.3611%204.19922%2014.2201%204.19922C11.0792%204.19922%208.53429%206.74408%208.53429%209.88504C8.53429%2011.7811%209.46282%2013.4616%2010.8912%2014.4956C10.882%2014.5002%2010.8728%2014.5025%2010.8636%2014.5071C9.83652%2014.9404%208.91487%2015.5617%208.1239%2016.355C7.33685%2017.1404%206.70933%2018.0707%206.276%2019.0947C5.85031%2020.0972%205.62072%2021.172%205.59967%2022.2609C5.59905%2022.2853%205.60335%2022.3097%205.61229%2022.3325C5.62123%2022.3553%205.63465%2022.376%205.65174%2022.3935C5.66883%2022.4111%205.68926%2022.425%205.71182%2022.4345C5.73437%2022.444%205.7586%2022.4489%205.78308%2022.4489H7.15868C7.25956%2022.4489%207.3398%2022.3686%207.3421%2022.2701C7.38795%2020.5001%208.09868%2018.8425%209.35506%2017.5861C10.655%2016.2862%2012.3814%2015.5709%2014.2201%2015.5709C16.0588%2015.5709%2017.7852%2016.2862%2019.0852%2017.5861C20.3415%2018.8425%2021.0523%2020.5001%2021.0981%2022.2701C21.1004%2022.3709%2021.1807%2022.4489%2021.2815%2022.4489H22.6571C22.6816%2022.4489%2022.7058%2022.444%2022.7284%2022.4345C22.751%2022.425%2022.7714%2022.4111%2022.7885%2022.3935C22.8056%2022.376%2022.819%2022.3553%2022.8279%2022.3325C22.8369%2022.3097%2022.8412%2022.2853%2022.8406%2022.2609C22.8176%2021.165%2022.5907%2020.0989%2022.1642%2019.0924ZM14.2201%2013.8284C13.1678%2013.8284%2012.1773%2013.418%2011.4322%2012.6729C10.6871%2011.9278%2010.2767%2010.9374%2010.2767%209.88504C10.2767%208.83271%2010.6871%207.84227%2011.4322%207.09715C12.1773%206.35204%2013.1678%205.94165%2014.2201%205.94165C15.2724%205.94165%2016.2629%206.35204%2017.008%207.09715C17.7531%207.84227%2018.1635%208.83271%2018.1635%209.88504C18.1635%2010.9374%2017.7531%2011.9278%2017.008%2012.6729C16.2629%2013.418%2015.2724%2013.8284%2014.2201%2013.8284Z'%20fill='%238590A2'/%3e%3c/g%3e%3c/svg%3e)
Co-authors
Following
Followers
Research Focus & Metrics
Explore full metrics on Scilit
Academic Career Length
10Years (2017-Present)
(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
View All Publications
Journal Article
Max Marian
Stephan TremmelMachine learning (ML) and artificial intelligence (AI) are rising stars in many scientific disciplines and industries, and high hopes are being pinned upon them. Likewise, ML and AI approaches have also found their way into tribology, where they can support sorting through the complexity of patterns and identifying trends within the multiple interacting features and processes. Published research extends across many fields of tribology from composite materials and drive technology to manufacturing, surface engineering, and lubricants. Accordingly, the intended usages and numerical algorithms are manifold, ranging from artificial neural networks (ANN), decision trees over random forest and rule-based learners to support vector machines. Therefore, this review is aimed to introduce and discuss the current trends and applications of ML and AI in tribology. Thus, researchers and R&D engineers shall be inspired and supported in the identification and selection of suitable and promising ML approaches and strategies.
13 December 2021 in Advanced Materials Interfaces
Journal Article
Max Marian
Diana BermanARAlberto Rota
Alberto Rota
Robert L Jackson
Andreas RosenkranzJournal Article
Andreas RosenkranzMax MarianFPFrancisco J. Profito
Francisco J. ProfitoNANathan Aragon
Nathan Aragon
Raj ShahArtificial intelligence and, in particular, machine learning methods have gained notable attention in the tribological community due to their ability to predict tribologically relevant parameters such as, for instance, the coefficient of friction or the oil film thickness. This perspective aims at highlighting some of the recent advances achieved by implementing artificial intelligence, specifically artificial neutral networks, towards tribological research. The presentation and discussion of successful case studies using these approaches in a tribological context clearly demonstrates their ability to accurately and efficiently predict these tribological characteristics. Regarding future research directions and trends, we emphasis on the extended use of artificial intelligence and machine learning concepts in the field of tribology including the characterization of the resulting surface topography and the design of lubricated systems.
19 April 2022 in Friction
Journal Article
Max MarianAAAndreas Almqvist
Andreas Almqvist
Andreas RosenkranzMFMichel Fillon
Michel Fillon
Despite numerous experimental and theoretical studies reported in the literature, surface micro-texturing to control friction and wear in lubricated tribo-contacts is still in the trial-and-error phase. The tribological behaviour and advantageous micro-texture geometries and arrangements largely depend on the contact type and the operating conditions. Industrial scale implementation is hampered by the complexity of numerical approaches. This substantiates the urgent need to numerically design and optimize micro-textures for specific conditions. Since these aspects have not been covered by other review articles yet, we aim at summarizing the existing state-of-the art regarding optimization strategies for micro-textures applied in hydrodynamically and elastohydrodynamically lubricated contacts. Our analysis demonstrates the great potential of optimization strategies to further tailor micro-textures with the overall aim to reduce friction and wear, thus contributing toward an improved energy efficiency and sustainability.
1 November 2020 in Applied Surface Science
Journal Article
Max MarianGSGui Cheng Song
Gui Cheng Song
BWBo Wang
Bo Wang
VFVictor M. Fuenzalida
Victor M. Fuenzalida
SKSebastian Krauß
Sebastian Krauß
Andreas Rosenkranz +4
1 October 2021 in Colloids and Surfaces B: Biointerfaces
Journal Article
Max MarianRSRaj Shah
Raj Shah
BGBlerim Gashi
Blerim GashiSZStanley Zhang
Stanley ZhangKBKiran Bhavnani
Kiran Bhavnani
Andreas Rosenkranz +1
About Me
Max Marian is a Professor and Deputy Head at the Insitute of Machine Design and Tribology (IMKT) of Leibniz University Hannover, Germany, and an Assistant Professor for Multiscale Engineering Mechanics at the Department of Mechanical and Metallurgical Engineering of Pontificia Universidad Católica de Chile. His research focuses on energy efficiency and sustainability through tribology, with an emphasis on the modification of surfaces through micro-texturing and coatings (diamond-like carbon and 2D materials). Besides machine elements and engine components, he expanded his research to biotribology and artificial joints, as well as triboelectric nanogenerators. His research is particularly related to the development of numerical multiscale tribo-simulation and machine learning approaches. He has published more than 50 peer-reviewed publications in reputed journals, has given numerous conference and invited talks and has been awarded various individual distinctions, as well as best paper and presentation awards. Furthermore, he was listed among the Emerging Leaders 2023 of Surface Topography: Metrology and Properties. Moreover, he is on the Editorial Boards of Frontiers in Chemistry Nanoscience, Industrial Lubrication and Tribology, Lubricants as well as Tribology—Materials, Surfaces & Interfaces, and is a member of the Society of Tribologists and Lubrication Engineers (STLE) and the German Society for Tribology (GfT).