... (MMT) nanoclay reinforcement on the machining performance of GFRP composites and employs advanced machine learning (ML) models to optimize key machining parameters. Experimental results indicate that incorporating 3 wt% MMT nanoclay enhances the mechanical integrity of GFRP composites by improving...

...Sunil Kumar Prajapati Machine learning is a cutting-edge technology that stands out among the various artificial intelligence offerings with its exceptional ability to comprehend intricate processes in computational tools. The optimization of input parameters for polyetheretherketone (PEEK...

..., machine learning models with excellent performance were selected as the base learners. Second, the Lévy flight strategy and golden sine algorithm were introduced to improve the whale optimization algorithm (LGWOA). Finally, based on LGWOA and base learner performance, the model adjustment coefficient...

... to the challenges in capturing subtle asperity interactions and small contact areas under low-load conditions. Future research should address these limitations by incorporating finer-resolution datasets and extending the model to better account for such scenarios as well as applying different machine-learning...

... in the J ournal of T ribology . 27 08 2024 18 01 2025 21 01 2025 17 03 2025 fault identification rolling bearing cycle characteristics 1D-LBP cross-correlation bearings machine learning rolling bearing vibration signal Aeronautical Science Foundation...

... of these surfaces. The machine learning models employed in the study were XGBoost, K-Nearest Neighbors (KNN), and Support Vector Classifier (SVC). The models were evaluated using fivefold cross-validation. The analysis revealed that the most significant parameter in DCOF predictions for the XGBoost model...

.... This study, which is based on experimental research and numerical analysis methods, investigates the effect of impact frequency on the impact-sliding fretting corrosion behavior of Inconel 690 alloy tubes. Then, machine learning is applied to predict the evolution law of the degree of damage. The results...

... fabric-reinforced epoxy composites using a computational and data-driven machine learning (ML) approach. Predictive models for the coefficient of friction (COF) were developed based on previous tribological, mechanical, and thermal data, employing three ML algorithms: artificial neural network (ANN...

... slag in repairs of automotive bodies by machine vision and machine learning. Based on the high computational abilities in object identification and segmentation, the latest version YOLOv8 was used in the present study. In metal cutting, traditional mathematical models are developed and predict...

... behavior. In contrast, in situ monitoring provides real-time insights into evolving friction dynamics. This study employs machine learning to monitor polymer wear performance through friction noise. The predictive accuracy of various machine learning methods, including Extremely Randomized Trees, Gradient...

... on wear prediction based on physical models, but due to device complexity and uncertainty, these methods often fail to provide accurate predictions and accurate wear identification. Machine learning, as a data-driven approach based on its ability to discover patterns and correlations in complex systems...

...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...