...Chitti Babu Golla; R. Narasimha Rao; Syed Ismail This study highlights the importance of Al–Fe–Si alloys in modern engineering for their enhanced hardness, strength, and wear resistance, improving fuel efficiency in the aerospace and automotive sectors. Data-driven analysis and machine learning...

... of diverse machine learning approaches. Email: 411918003@nitt.edu Email: sharath@kluniversity.in 1 Corresponding author. Email: harish@nitt.edu Contributed by the Tribology Division of ASME for publication in the J ournal of T ribology . 10 05 2024 22 08 2024 23 08...

... was predicted based on six machine learning algorithms. The results indicated that in fluid lubrication, graphene promoted “liquid–liquid” interlayer sliding, whereas fullerene facilitated “solid–liquid” interface sliding, resulting in a decrease or increase in friction force. Under boundary lubrication...

... recommend a machine learning study with a larger number of subjects who can better capture the nuances of varying types of human crepitus. Human crepitus signals are quite complex. Consequently, our first task was to establish a strict definition of a crepitus signal. This was a novel aspect...

... efficiency, this probabilistic study is conducted in conjunction with the machine learning (ML) model based on the support vector machine (SVM) algorithm. The uncertainty in the bearing responses is presented in the form of the probability density function (PDF), considering both the independent and combined...

... procedure and contact conditions. Machine learning offers a facile path to predict mechanical properties if sufficient datasets are available, without which it is very challenging to attain a high prediction accuracy. In this work, high-accuracy wear prediction of 316L stainless steel parts fabricated using...

.... , and Tremmel , S. , 2021 , “ Current Trends and Applications of Machine Learning in Tribology—A Review ,” Lubricants , 9 ( 9 ), p. 86 . 10.3390/lubricants9090086 [16] Chambers , J. M. , and Hastie , T. J. , 2017 , “ Statistical Models ,” Statistical Models in S , Routledge , UK , pp...

... machine learning (ML) algorithms. The prediction accuracy of the data-driven models derived from ML algorithms exceeds that of the multivariate regression benchmark because the latter does not always capture the complex relationship between the as-built surface topography parameters and the corresponding...

...Nathan Hess; Lizhi Shang This paper presents a machine learning neural network capable of approximating pressure as the distributive result of elastohydrodynamic (EHD) effects for a journal bearing at steady state. Design of efficient, reliable fluid power pumps and motors requires accurate models...

...Md Syam Hasan; Amir Kordijazi; Pradeep K. Rohatgi; Michael Nosonovsky Data-driven analysis and machine learning (ML) algorithms can offer novel insights into tribological phenomena by establishing correlations between material and tribological properties. We developed ML algorithms using...