LEADER 04174nam 2200973z- 450 001 9910576887003321 005 20231214133157.0 035 $a(CKB)5720000000008304 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/84499 035 $a(EXLCZ)995720000000008304 100 $a20202206d2022 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMachine Learning in Tribology 210 $aBasel$cMDPI - Multidisciplinary Digital Publishing Institute$d2022 215 $a1 electronic resource (208 p.) 311 $a3-0365-3981-6 311 $a3-0365-3982-4 330 $aTribology 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. 606 $aTechnology: general issues$2bicssc 606 $aHistory of engineering & technology$2bicssc 610 $aartificial intelligence 610 $amachine learning 610 $aartificial neural networks 610 $atribology 610 $acondition monitoring 610 $asemi-supervised learning 610 $arandom forest classifier 610 $aself-lubricating journal bearings 610 $areduced order modelling 610 $adynamic friction 610 $arubber seal applications 610 $atensor decomposition 610 $alaser surface texturing 610 $atexturing during moulding 610 $adigital twin 610 $aPINN 610 $areynolds equation 610 $atriboinformatics 610 $adatabases 610 $adata mining 610 $ameta-modeling 610 $amonitoring 610 $aanalysis 610 $aprediction 610 $aoptimization 610 $afault data generation 610 $aConvolutional Neural Network (CNN) 610 $aGenerative Adversarial Network (GAN) 610 $abearing fault diagnosis 610 $aunbalanced datasets 610 $atribo-testing 610 $atribo-informatics 610 $anatural language processing 610 $atribAIn 610 $aBERT 610 $aamorphous carbon coatings 610 $aUHWMPE 610 $atotal knee replacement 610 $aGaussian processes 610 $arolling bearing dynamics 610 $acage instability 610 $aregression 610 $aneural networks 610 $arandom forest 610 $agradient boosting 610 $aevolutionary algorithms 610 $arolling bearings 610 $aremaining useful life 610 $afeature engineering 610 $astructure-borne sound 615 7$aTechnology: general issues 615 7$aHistory of engineering & technology 700 $aTremmel$b Stephan$4edt$01311976 702 $aMarian$b Max$4edt 702 $aTremmel$b Stephan$4oth 702 $aMarian$b Max$4oth 906 $aBOOK 912 $a9910576887003321 996 $aMachine Learning in Tribology$93030644 997 $aUNINA