A recent study published in Polymers explores the use of machine learning (ML) to assess the tribological properties of sericite/epoxy composite coatings (SEC).
Researchers tested six different ML algorithms to analyze how filler content and mesh size influence friction and wear under varying loads. They found that gradient boosting regression (GBR) provided the most accurate and stable predictions, with 93.7 % accuracy for friction coefficient and 85.7 % for wear rate. Their findings offer valuable insights for optimizing material performance in engineering applications. ici pilling tester
Friction and wear are inevitable in industries like machinery, energy, and transportation, leading to reduced efficiency, increased energy use, and higher maintenance costs. To address these challenges, researchers have developed coatings with enhanced tribological properties. Traditional methods for evaluating these coatings, such as wear and tensile tests, can be time-consuming, destructive, and resource-intensive. Machine learning presents a promising alternative by enabling efficient, non-destructive assessments.
Previous studies have applied ML to predict tribological performance based on factors like load, temperature, and sliding velocity. While these efforts have advanced understanding, they have largely overlooked the role of inherent filler properties, an aspect this study aims to address. This study has attempted to fill that gap by evaluating six ML algorithms, including GBR and random forest (RF), to determine the best predictive model and identify key factors influencing performance.
The researchers developed and tested SEC formulations to enhance tribological performance. Epoxy resin was combined with sericite powder of varying mesh sizes (325, 500, 800) and weight percentages. After thorough mixing and vacuum treatment to remove air bubbles, the mixture was sprayed onto steel templates and cured at room temperature for 48 hours. The final coating thickness measured approximately 35 micrometers (µm).
To assess tribological properties, they conducted tests using a high-speed reciprocating friction machine, with a GCr15 steel ball as the friction counterpart. This setup was chosen due to its ability to simulate real-world contact conditions, providing reliable and reproducible measurements of friction and wear behaviors. Tests were performed under loads of 5, 10, and 15 Newtons (N).
The researchers then analyzed surface morphology and wear characteristics using scanning electron microscopy (SEM) and white light interferometry, while elemental distribution was examined through energy-dispersive spectroscopy (EDS).
For ML-based predictions, six models—RF, GBR, Gaussian process regression (GPR), artificial neural networks (ANN), support vector regression (SVR), and extreme gradient boosting (XGB)—were trained on data from 45 friction tests. The dataset was normalized and split into 80 % for training and 20 % for testing. Researchers optimized model hyperparameters using grid search and cross-validation, evaluating performance based on R-squared values, mean squared error, and mean absolute error.
The study examined how sericite content, load, and mesh size affected the coefficient of friction (COF) and wear rate. EDS and SEM analyses confirmed a uniform distribution of sericite powder in the coatings. Results showed that at 20 weight percentage (wt%) sericite, the COF and wear rate peaked due to adhesive wear mechanisms. However, at 30 wt%, wear debris accumulation led to lower COF and wear rates. Feature importance analysis identified sericite content as the primary factor affecting friction and wear, with load and mesh size playing secondary roles.
Among the ML models tested, GBR provided the highest predictive accuracy, achieving R-squared values of 0.937 for COF and 0.857 for wear rate, with minimal errors. Its ability to iteratively optimize residuals and adjust parameters ensured a strong balance between model fit and complexity.
Building on these results, the findings suggest that GBR is a powerful tool for predicting tribological behavior, helping reduce the time and cost associated with traditional testing methods. By providing reliable insights into how filler content and testing conditions influence SEC performance, this model supports the development of advanced tribological coatings for engineering applications.
This study demonstrated the effectiveness of ML in predicting the tribological properties of SEC. Among six tested models, GBR emerged as the most accurate, achieving 93.7 % accuracy for friction coefficient and 85.7 % for wear rate. The results highlighted sericite content as the most influential factor in tribological performance. By offering a cost-effective and nondestructive alternative to traditional testing, GBR can help optimize SEC formulations, ultimately improving the durability and efficiency of engineering coatings.
Yan et al., 2025. Machine Learning-Based Prediction of Tribological Properties of Epoxy Composite Coating. Polymers, 17(3), 282. DOI:10.3390/polym17030282 https://www.mdpi.com/2073-4360/17/3/28
Disclaimer: The views expressed here are those of the author expressed in their private capacity and do not necessarily represent the views of AZoM.com Limited T/A AZoNetwork the owner and operator of this website. This disclaimer forms part of the Terms and conditions of use of this website.
Please use one of the following formats to cite this article in your essay, paper or report:
Nandi, Soham. (2025, January 30). Machine Learning Enhances Tribological Coating Performance with High Accuracy Predictions. AZoRobotics. Retrieved on February 05, 2025 from https://www.azorobotics.com/News.aspx?newsID=15680.
Nandi, Soham. "Machine Learning Enhances Tribological Coating Performance with High Accuracy Predictions". AZoRobotics. 05 February 2025. <https://www.azorobotics.com/News.aspx?newsID=15680>.
Nandi, Soham. "Machine Learning Enhances Tribological Coating Performance with High Accuracy Predictions". AZoRobotics. https://www.azorobotics.com/News.aspx?newsID=15680. (accessed February 05, 2025).
Nandi, Soham. 2025. Machine Learning Enhances Tribological Coating Performance with High Accuracy Predictions. AZoRobotics, viewed 05 February 2025, https://www.azorobotics.com/News.aspx?newsID=15680.
Do you have a review, update or anything you would like to add to this news story?
As part of an Editorial short series, AZoRobotics takes a look at how the renewable energy sector is harnessing the power of robotic technologies. Here, we take a look at robots in hydropower stations.
Amid the rapid global expansion of the wind energy sector, the integration of robotics is becoming pivotal for wind farm operators.
Like any renewable energy infrastructure, solar plants must be protected and secured. It is here where robots and autonomous systems can come into play.
Discover Cavitar’s welding cameras that can be used in a variety of situations to offer high-quality visualization of the welding processes.
MTI’s 1510A portable signal generator and calibrator is ideal for testing the integrity of sensor signal conditioning electronics.
Using the advantages of the phased array technology, Olympus has designed a powerful inspection system for seamless pipe inspections well-adapted to the stringent requirements of the oil and gas markets. This phased array system is flexible and can be used to match inspection performances and the product requirements of customers.
Your AI Powered Scientific Assistant
Hi, I'm Azthena, you can trust me to find commercial scientific answers from AZoNetwork.com.
To start a conversation, please log into your AZoProfile account first, or create a new account.
Registered members can chat with Azthena, request quotations, download pdf's, brochures and subscribe to our related newsletter content.
A few things you need to know before we start. Please read and accept to continue.
Please check the box above to proceed.
Azthena may occasionally provide inaccurate responses. Read the full terms.
While we only use edited and approved content for Azthena answers, it may on occasions provide incorrect responses. Please confirm any data provided with the related suppliers or authors. We do not provide medical advice, if you search for medical information you must always consult a medical professional before acting on any information provided.
Your questions, but not your email details will be shared with OpenAI and retained for 30 days in accordance with their privacy principles.
Please do not ask questions that use sensitive or confidential information.
Read the full Terms & Conditions.
AZoRobotics.com - An AZoNetwork Site
air permeability astm d737 Owned and operated by AZoNetwork, © 2000-2025