Triboinformatics Approach for Friction and Wear Prediction of Al-Graphite Composites Using Machine Learning Methods

2021 ◽  
Vol 144 (1) ◽  
Author(s):  
Md Syam Hasan ◽  
Amir Kordijazi ◽  
Pradeep K. Rohatgi ◽  
Michael Nosonovsky
Keyword(s):  
Machine Learning ◽  
Tribological Properties ◽  
Wear Behavior ◽  
Normal Load ◽  
Gradient Boosting ◽  
Support Vector ◽  
Sliding Speed ◽  
Graphite Content ◽  
Wear Rates ◽  
Two Parameter

Abstract 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 tribological data available in the literature for predicting the coefficient of friction (COF) and wear-rate of self-lubricating aluminum graphite (Al/Gr) composites. We collected data on effects of material variables (graphite content, hardness, ductility, yield strength, silicon carbide content, and tensile strength), processing procedure, heat treatment and tribological test variables (normal load, sliding speed, and sliding distance) on tribological properties and established two-parameter relationships. These data are analyzed using several ML algorithms: artificial neural network (ANN), K nearest neighbor (KNN), support vector machine (SVM), gradient boosting machine (GBM), and random forest (RF). The trained ML models can predict the tribological behavior from material variables and test conditions, beyond what is possible from two-parameter correlations. GBM outperformed other ML algorithms in predicting friction behavior, while RF had the best prediction of the wear behavior. ML analysis identified graphite content and hardness and as the most significant variables in predicting the COF, while graphite content and sliding speed were the most dominant variables for wear-rates.

scholarly journals Evaluation of Friction and Wear Behavior of Date Palm Fruit Syrup as an Environmentally Friendly Lubricant

Materials ◽  
2019 ◽  
Vol 12 (10) ◽  
pp. 1589 ◽  
Author(s):  
Mazin Tahir ◽  
Abdul Samad Mohammed ◽  
Umar Azam Muhammad
Keyword(s):  
Tribological Properties ◽  
Friction And Wear ◽  
Date Palm ◽  
Wear Behavior ◽  
Normal Load ◽  
Environmentally Friendly ◽  
Potential Candidate ◽  
Palm Fruit ◽  
Wear Rates ◽  
The Coefficient Of Friction

The effect of various operational factors, such as sliding speed, normal load and temperature on the tribological properties of Date palm fruit syrup (DPFS) as an environmentally friendly lubricant, is investigated. Ball-on-disc wear tests are conducted on mild steel samples in the presence of DPFS as a lubricant under different conditions and the coefficient of friction and wear rate are measured. Scanning electron microscopy, stylus profilometry, and Fourier transform infrared spectroscopy are used to evaluate the wear tracks to determine the underlying wear mechanisms. Results showed that DPFS has excellent tribological properties in terms of low friction and low wear rates making it a potential candidate to be used as a lubricant in tribological applications.

The Mechanical Properties and Friction and Wear Behavior of C/C-SIC

2010 ◽  
Author(s):  
Gao Wen ◽  
Chongsheng Long ◽  
Tang Rui ◽  
Jiping Wang
Keyword(s):  
Friction Coefficient ◽  
Wear Rate ◽  
Friction And Wear ◽  
Wear Behavior ◽  
Normal Load ◽  
Carbon Matrix ◽  
Specific Wear Rate ◽  
Sliding Speed ◽  
Wear Rates ◽  
Sic Composites

Carbon fiber reinforced carbon-silicon carbide composites (C/C-SiC) were prepared by chemical volume infiltration (CVI) method and reaction melt infiltration (RMI) technique of silicon liquid to carbon reinforce carbon matrix composites. The friction and wear behaviors of C/C-SiC composites at various loads and sliding speeds were investigated by MRH-3 block-on-ring tribometer at room temperature under water lubricating conditions. Furthermore, the morphologies, phase of the worn surface and the debris were observed, examined and analyzed by scanning electron microscopy (SEM) and energy-dispersive X-ray microanalysis (EDAX) respectively. Experimental results showed that the C/C-SiC composites had a better wear resistence, and the friction coefficient under water lubricated conditions is about 0.02–0.06. The influence of sliding speed on the friction coefficients and the specific wear rate of C/C-SiC is more obvious than that of normal load when the load is less than 200N (inclueded200N). The friction coefficient and the specific wear rate of C/C-SiC decreased as the sliding velocity increased. At the sliding speed higher than 2m/s, the friction coefficient is less than 0.02. The specific wear rates is at a low level about (2×10−7mm3/Nm–5×10−8mm3/Nm).

Study on Abrasive Wear Behavior and Mechanisms of 7Cr7Mo2V2Si Steel as Drilling Tool Materials

2011 ◽  
Vol 239-242 ◽  
pp. 2986-2992
Author(s):  
Ye Fa Tan ◽  
Bin Cai ◽  
Xiao Long Wang ◽  
Guo Liang Jiang ◽  
Chun Hua Zhou
Keyword(s):  
Abrasive Wear ◽  
Wear Behavior ◽  
Normal Load ◽  
Fine Powder ◽  
Corrosive Wear ◽  
Wear Loss ◽  
Drilling Tool ◽  
Sliding Speed ◽  
Wear Rates ◽  
Abrasive Wear Behavior

In order to search for new wear resistant materials used as drilling tools and improve the service life and drilling efficiency, the 7Cr7Mo2V2Si steel was prepared and its abrasive wear behavior and mechanisms were studied under both dry and water wear conditions. The research results show that the wear losses of the 7Cr7Mo2V2Si steel increase with the increase of normal load and sliding speed at both of dry and water wear conditions. The wear losses become greatly increase at high sliding speed and heavy normal load wear conditions. The wear rates of the 7Cr7Mo2V2Si steel at water wear conditions are bigger than those at dry wear conditions. The existence of water will aggravate the wear loss of the steel because water can clean the tribo-interface by taking away the fine powder or debris, which may keep the corundum abrasives protruding and remaining sharp edge state to produce more serious two-body abrasive wear to the steel, and meanwhile the collaborative action of the friction stress and the corrosion may result in stress corrosive wear of the steel. The main wear mechanisms of the 7Cr7Mo2V2Si steel are micro-cutting wear, multi-plastic deformation wear at dry wear conditions and accompanied with stress corrosive wear at water wear conditions.

In silico Prediction of Inhibitory Constant of Thrombin Inhibitors Using Machine Learning

2019 ◽  
Vol 21 (9) ◽  
pp. 662-669 ◽  
Author(s):  
Junnan Zhao ◽  
Lu Zhu ◽  
Weineng Zhou ◽  
Lingfeng Yin ◽  
Yuchen Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Regression Tree ◽  
Large Data ◽  
Thrombin Inhibitors ◽  
Coagulation Cascade ◽  
Gradient Boosting ◽  
Support Vector ◽  
Data Set ◽  
Descriptor Selection

Background: Thrombin is the central protease of the vertebrate blood coagulation cascade, which is closely related to cardiovascular diseases. The inhibitory constant Ki is the most significant property of thrombin inhibitors. Method: This study was carried out to predict Ki values of thrombin inhibitors based on a large data set by using machine learning methods. Taking advantage of finding non-intuitive regularities on high-dimensional datasets, machine learning can be used to build effective predictive models. A total of 6554 descriptors for each compound were collected and an efficient descriptor selection method was chosen to find the appropriate descriptors. Four different methods including multiple linear regression (MLR), K Nearest Neighbors (KNN), Gradient Boosting Regression Tree (GBRT) and Support Vector Machine (SVM) were implemented to build prediction models with these selected descriptors. Results: The SVM model was the best one among these methods with R2=0.84, MSE=0.55 for the training set and R2=0.83, MSE=0.56 for the test set. Several validation methods such as yrandomization test and applicability domain evaluation, were adopted to assess the robustness and generalization ability of the model. The final model shows excellent stability and predictive ability and can be employed for rapid estimation of the inhibitory constant, which is full of help for designing novel thrombin inhibitors.

scholarly journals Machine learning models to identify low adherence to influenza vaccination among Korean adults with cardiovascular disease

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Moojung Kim ◽  
Young Jae Kim ◽  
Sung Jin Park ◽  
Kwang Gi Kim ◽  
Pyung Chun Oh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cardiovascular Disease ◽  
Influenza Vaccination ◽  
Machine Learning Techniques ◽  
Gradient Boosting ◽  
Support Vector ◽  
Age Group ◽  
Learning Models ◽  
Extreme Gradient Boosting ◽  
Machine Learning Models

Abstract Background Annual influenza vaccination is an important public health measure to prevent influenza infections and is strongly recommended for cardiovascular disease (CVD) patients, especially in the current coronavirus disease 2019 (COVID-19) pandemic. The aim of this study is to develop a machine learning model to identify Korean adult CVD patients with low adherence to influenza vaccination Methods Adults with CVD (n = 815) from a nationally representative dataset of the Fifth Korea National Health and Nutrition Examination Survey (KNHANES V) were analyzed. Among these adults, 500 (61.4%) had answered "yes" to whether they had received seasonal influenza vaccinations in the past 12 months. The classification process was performed using the logistic regression (LR), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGB) machine learning techniques. Because the Ministry of Health and Welfare in Korea offers free influenza immunization for the elderly, separate models were developed for the < 65 and ≥ 65 age groups. Results The accuracy of machine learning models using 16 variables as predictors of low influenza vaccination adherence was compared; for the ≥ 65 age group, XGB (84.7%) and RF (84.7%) have the best accuracies, followed by LR (82.7%) and SVM (77.6%). For the < 65 age group, SVM has the best accuracy (68.4%), followed by RF (64.9%), LR (63.2%), and XGB (61.4%). Conclusions The machine leaning models show comparable performance in classifying adult CVD patients with low adherence to influenza vaccination.

scholarly journals Prediction of Healing Performance of Autogenous Healing Concrete Using Machine Learning

Materials ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4068
Author(s):  
Xu Huang ◽  
Mirna Wasouf ◽  
Jessada Sresakoolchai ◽  
Sakdirat Kaewunruen
Keyword(s):  
Machine Learning ◽  
Search Algorithm ◽  
Weather Conditions ◽  
Prediction Performance ◽  
Machine Learning Algorithms ◽  
Coefficient Of Determination ◽  
Gradient Boosting ◽  
Support Vector ◽  
Self Healing ◽  
Artificial Neural Network Ann

Cracks typically develop in concrete due to shrinkage, loading actions, and weather conditions; and may occur anytime in its life span. Autogenous healing concrete is a type of self-healing concrete that can automatically heal cracks based on physical or chemical reactions in concrete matrix. It is imperative to investigate the healing performance that autogenous healing concrete possesses, to assess the extent of the cracking and to predict the extent of healing. In the research of self-healing concrete, testing the healing performance of concrete in a laboratory is costly, and a mass of instances may be needed to explore reliable concrete design. This study is thus the world’s first to establish six types of machine learning algorithms, which are capable of predicting the healing performance (HP) of self-healing concrete. These algorithms involve an artificial neural network (ANN), a k-nearest neighbours (kNN), a gradient boosting regression (GBR), a decision tree regression (DTR), a support vector regression (SVR) and a random forest (RF). Parameters of these algorithms are tuned utilising grid search algorithm (GSA) and genetic algorithm (GA). The prediction performance indicated by coefficient of determination (R2) and root mean square error (RMSE) measures of these algorithms are evaluated on the basis of 1417 data sets from the open literature. The results show that GSA-GBR performs higher prediction performance (R2GSA-GBR = 0.958) and stronger robustness (RMSEGSA-GBR = 0.202) than the other five types of algorithms employed to predict the healing performance of autogenous healing concrete. Therefore, reliable prediction accuracy of the healing performance and efficient assistance on the design of autogenous healing concrete can be achieved.

scholarly journals Proposing a machine-learning based method to predict stillbirth before and during delivery and ranking the features: nationwide retrospective cross-sectional study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Toktam Khatibi ◽  
Elham Hanifi ◽  
Mohammad Mehdi Sepehri ◽  
Leila Allahqoli
Keyword(s):  
Machine Learning ◽  
External Validation ◽  
Fetal Loss ◽  
Null Distribution ◽  
Training Dataset ◽  
Gradient Boosting ◽  
Support Vector ◽  
Cross Sectional ◽  
Boosting Method ◽  
Demographic Features

Abstract Background Stillbirth is defined as fetal loss in pregnancy beyond 28 weeks by WHO. In this study, a machine-learning based method is proposed to predict stillbirth from livebirth and discriminate stillbirth before and during delivery and rank the features. Method A two-step stack ensemble classifier is proposed for classifying the instances into stillbirth and livebirth at the first step and then, classifying stillbirth before delivery from stillbirth during the labor at the second step. The proposed SE has two consecutive layers including the same classifiers. The base classifiers in each layer are decision tree, Gradient boosting classifier, logistics regression, random forest and support vector machines which are trained independently and aggregated based on Vote boosting method. Moreover, a new feature ranking method is proposed in this study based on mean decrease accuracy, Gini Index and model coefficients to find high-ranked features. Results IMAN registry dataset is used in this study considering all births at or beyond 28th gestational week from 2016/04/01 to 2017/01/01 including 1,415,623 live birth and 5502 stillbirth cases. A combination of maternal demographic features, clinical history, fetal properties, delivery descriptors, environmental features, healthcare service provider descriptors and socio-demographic features are considered. The experimental results show that our proposed SE outperforms the compared classifiers with the average accuracy of 90%, sensitivity of 91%, specificity of 88%. The discrimination of the proposed SE is assessed and the average AUC of ±95%, CI of 90.51% ±1.08 and 90% ±1.12 is obtained on training dataset for model development and test dataset for external validation, respectively. The proposed SE is calibrated using isotopic nonparametric calibration method with the score of 0.07. The process is repeated 10,000 times and AUC of SE classifiers using random different training datasets as null distribution. The obtained p-value to assess the specificity of the proposed SE is 0.0126 which shows the significance of the proposed SE. Conclusions Gestational age and fetal height are two most important features for discriminating livebirth from stillbirth. Moreover, hospital, province, delivery main cause, perinatal abnormality, miscarriage number and maternal age are the most important features for classifying stillbirth before and during delivery.

scholarly journals Comparison of Ensemble Machine Learning Methods for Soil Erosion Pin Measurements

2021 ◽  
Vol 10 (1) ◽  
pp. 42
Author(s):  
Kieu Anh Nguyen ◽  
Walter Chen ◽  
Bor-Shiun Lin ◽  
Uma Seeboonruang
Keyword(s):  
Machine Learning ◽  
Soil Erosion ◽  
Ensemble Methods ◽  
Machine Learning Algorithms ◽  
Multivariate Adaptive Regression Splines ◽  
Gradient Boosting ◽  
Support Vector ◽  
Ensemble Machine Learning ◽  
Boosting Method ◽  
Bagging Method

Although machine learning has been extensively used in various fields, it has only recently been applied to soil erosion pin modeling. To improve upon previous methods of quantifying soil erosion based on erosion pin measurements, this study explored the possible application of ensemble machine learning algorithms to the Shihmen Reservoir watershed in northern Taiwan. Three categories of ensemble methods were considered in this study: (a) Bagging, (b) boosting, and (c) stacking. The bagging method in this study refers to bagged multivariate adaptive regression splines (bagged MARS) and random forest (RF), and the boosting method includes Cubist and gradient boosting machine (GBM). Finally, the stacking method is an ensemble method that uses a meta-model to combine the predictions of base models. This study used RF and GBM as the meta-models, decision tree, linear regression, artificial neural network, and support vector machine as the base models. The dataset used in this study was sampled using stratified random sampling to achieve a 70/30 split for the training and test data, and the process was repeated three times. The performance of six ensemble methods in three categories was analyzed based on the average of three attempts. It was found that GBM performed the best among the ensemble models with the lowest root-mean-square error (RMSE = 1.72 mm/year), the highest Nash-Sutcliffe efficiency (NSE = 0.54), and the highest index of agreement (d = 0.81). This result was confirmed by the spatial comparison of the absolute differences (errors) between model predictions and observations using GBM and RF in the study area. In summary, the results show that as a group, the bagging method and the boosting method performed equally well, and the stacking method was third for the erosion pin dataset considered in this study.

scholarly journals Explainable machine learning can outperform Cox regression predictions and provide insights in breast cancer survival

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Arturo Moncada-Torres ◽  
Marissa C. van Maaren ◽  
Mathijs P. Hendriks ◽  
Sabine Siesling ◽  
Gijs Geleijnse
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Explicit Knowledge ◽  
Cox Regression ◽  
Metastatic Breast ◽  
Gradient Boosting ◽  
Support Vector ◽  
Netherlands Cancer Registry ◽  
Extreme Gradient Boosting ◽  
The Impact

AbstractCox Proportional Hazards (CPH) analysis is the standard for survival analysis in oncology. Recently, several machine learning (ML) techniques have been adapted for this task. Although they have shown to yield results at least as good as classical methods, they are often disregarded because of their lack of transparency and little to no explainability, which are key for their adoption in clinical settings. In this paper, we used data from the Netherlands Cancer Registry of 36,658 non-metastatic breast cancer patients to compare the performance of CPH with ML techniques (Random Survival Forests, Survival Support Vector Machines, and Extreme Gradient Boosting [XGB]) in predicting survival using the $$c$$ c -index. We demonstrated that in our dataset, ML-based models can perform at least as good as the classical CPH regression ($$c$$ c -index $$\sim \,0.63$$ ∼ 0.63 ), and in the case of XGB even better ($$c$$ c -index $$\sim 0.73$$ ∼ 0.73 ). Furthermore, we used Shapley Additive Explanation (SHAP) values to explain the models’ predictions. We concluded that the difference in performance can be attributed to XGB’s ability to model nonlinearities and complex interactions. We also investigated the impact of specific features on the models’ predictions as well as their corresponding insights. Lastly, we showed that explainable ML can generate explicit knowledge of how models make their predictions, which is crucial in increasing the trust and adoption of innovative ML techniques in oncology and healthcare overall.

Investigation of friction and wear behavior of hydroxyl-functionalized graphene nanoplatelets filled elastomer nanocomposites

2021 ◽  
pp. 146442072110616
Author(s):  
Hasan Kasim ◽  
Adem Onat ◽  
Barış Engin ◽  
İsmail Saraç
Keyword(s):  
Wear Rate ◽  
Tribological Properties ◽  
Wear Behavior ◽  
Normal Load ◽  
Graphene Nanoplatelets ◽  
Rubber Matrix ◽  
High Wear Resistance ◽  
Homogeneous Distribution ◽  
Functionalized Graphene ◽  
Sealing Elements

The use of unfilled pure elastomer parts is limited in friction wheels, roller tires, sealing elements, and dynamic friction air suspension applications requiring high wear resistance. This study investigates the mechanical and tribological properties of new nanocomposites obtained by adding hydroxyl-functionalized graphene nanoplatelets at 1, 4, and 8 phr (parts per hundred rubber) ratios to the carbon black filled main rubber compound of sealing elements designed for axle hubs. The synergistic effect of nanofiller materials on the wear behavior of nanocomposites was tested with a block-on-ring wear tester under dry sliding conditions at 1000 rpm and 15 N normal load conditions. The worn surfaces were examined with scanning electron microscopy and circularly polarized light–differential interference contrast topology microscopy to reveal the wear mechanism. The addition of functionalized graphene nanoplatelets to the nanocomposite compound caused significant changes in tensile strength and elongation values by changing the cross-link density. The wear rate of nanocomposites prepared with graphene nanoplatelets at 1, 4, and 8 phr ratios was 11.15%, 25.24%, and 36.54% lower than the main rubber mixture used, respectively. While the hysteresis loss decreased by 14.83% at 1 phr, this value increased in other filler ratios. Significant differences in temperature change occurred as the amount of filler increased. After the test, the temperature values of nanocomposites with 1 and 4 phr filler ratios were between about 85–89°C, while it was measured as 99°C in nanocomposites with 8 phr filler ratios. It has been observed that the homogeneous distribution of two-dimensional carbon allotropes such as graphene nanoplatelet added to the rubber matrix at the optimum rate will improve tribological properties such as better surface lubrication, low wear rate, and low friction coefficient.

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