04174nam 2200973z- 450 991057688700332120231214133157.0(CKB)5720000000008304(oapen)https://directory.doabooks.org/handle/20.500.12854/84499(EXLCZ)99572000000000830420202206d2022 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierMachine Learning in TribologyBaselMDPI - Multidisciplinary Digital Publishing Institute20221 electronic resource (208 p.)3-0365-3981-6 3-0365-3982-4 Tribology has been and continues to be one of the most relevant fields, being present in almost all aspects of our lives. The understanding of tribology provides us with solutions for future technical challenges. At the root of all advances made so far are multitudes of precise experiments and an increasing number of advanced computer simulations across different scales and multiple physical disciplines. Based upon this sound and data-rich foundation, advanced data handling, analysis and learning methods can be developed and employed to expand existing knowledge. Therefore, modern machine learning (ML) or artificial intelligence (AI) methods provide opportunities to explore the complex processes in tribological systems and to classify or quantify their behavior in an efficient or even real-time way. Thus, their potential also goes beyond purely academic aspects into actual industrial applications. To help pave the way, this article collection aimed to present the latest research on ML or AI approaches for solving tribology-related issues generating true added value beyond just buzzwords. In this sense, this Special Issue can support researchers in identifying initial selections and best practice solutions for ML in tribology.Technology: general issuesbicsscHistory of engineering & technologybicsscartificial intelligencemachine learningartificial neural networkstribologycondition monitoringsemi-supervised learningrandom forest classifierself-lubricating journal bearingsreduced order modellingdynamic frictionrubber seal applicationstensor decompositionlaser surface texturingtexturing during mouldingdigital twinPINNreynolds equationtriboinformaticsdatabasesdata miningmeta-modelingmonitoringanalysispredictionoptimizationfault data generationConvolutional Neural Network (CNN)Generative Adversarial Network (GAN)bearing fault diagnosisunbalanced datasetstribo-testingtribo-informaticsnatural language processingtribAInBERTamorphous carbon coatingsUHWMPEtotal knee replacementGaussian processesrolling bearing dynamicscage instabilityregressionneural networksrandom forestgradient boostingevolutionary algorithmsrolling bearingsremaining useful lifefeature engineeringstructure-borne soundTechnology: general issuesHistory of engineering & technologyTremmel Stephanedt1311976Marian MaxedtTremmel StephanothMarian MaxothBOOK9910576887003321Machine Learning in Tribology3030644UNINA