Aug 2026· Computational nanotechnology· Vol 13, pp. 25-33· 0 citations· 4 references
TL;DR
The obtained results confirm the effectiveness of the proposed approach for formulation optimization and identification of hidden dependencies in the “composition-property” system.
Abstract
The paper presents an approach to constructing predictive models for the physical and mechanical properties of elastomeric composites using machine learning methods. The relevance of the study is driven by the need to accelerate the development of new materials and reduce the labor intensity of full-scale experiments. An automated machine learning algorithm is proposed, encompassing stages of input data unification, feature space formation, and comparative analysis of regression models (Random Forest, Gradient Boosted Decision Trees, Gradient Tree-Boosting Tweedie, Poisson Regression, Light Gradient Boosting Machine и Stochastic Dual Coordinate Ascent). During experimental validation on datasets containing formulation data with varying content of sulfur, natural rubber (NR), and zinc oxide, predictions were made for theoretical density, Karrer plasticity, brittleness temperature, and curing temperature. It was established that ensemble methods demonstrate the highest predictive capability; however, model accuracy significantly depends on sample representativeness. Intervals of the studied parameters (particularly the 140–150 °C range for curing temperature) characterized by increased prediction uncertainty were identified, requiring additional algorithm calibration. The obtained results confirm the effectiveness of the proposed approach for formulation optimization and identification of hidden dependencies in the “composition-property” system.
This study examines the application of ML techniques to composite materials, particularly for predicting fracture toughness, characterizing damage, and optimizing mechanical properties and reveals significant relationships between fracture toughness and important input parameters.
Periyasamy Chitra· Building Materials and Engin...· 0 citations
Fused deposition modeling is an important additive manufacturing technology, and accurate prediction of the mechanical properties of three-dimensional-printed parts is essential for engineering applications. However, existing studies are often limited by small sample sizes, insufficient comparison of predictive methods and a lack of external data validation. This study therefore aims to construct a cross-literature data set and compare different predictive approaches for the ultimate tensile strength (UTS) of polylactic acid printed parts.
A total of 235 experimental data points from ten published studies were integrated to establish a data set for UTS prediction. Five input variables were considered: infill density, nozzle temperature, nozzle diameter, layer height and printing speed. Response surface methodology, multiple machine learning regression algorithms and an artificial neural network (ANN) were systematically compared under unified preprocessing conditions. In addition, ensemble models were constructed using histogram-based gradient boosting regressor (HGBR), gradient boosting machine (GBM), random forest and kernel ridge regression (KRR). External validation was performed using 36 independent data points.
Among the single models, HGBR achieved the best performance with a test-set R² of 0.8952. The GBM-KRR-HGBR ensemble further improved accuracy, reaching a test-set R² of 0.9290. For this ensemble model, external validation showed that 61.11% of samples had prediction errors below 10%. Permutation importance analysis indicated that infill density was the most influential variable.
The originality of this study lies in a reliability-oriented literature-data curation strategy, a unified comparison of statistical, machine learning, ANN and ensemble models under the same data framework, and a source-wise external error analysis for evaluating model applicability under heterogeneous literature-derived data conditions.
Accurate prediction of concrete compressive strength is essential for effective mix design, quality control, and structural performance assessment. Conventional empirical models often exhibit limited accuracy due to the complex and nonlinear interactions among concrete constituents.
This study investigates the applicability of several machine learning models for predicting the compressive strength of concrete using a publicly available experimental dataset comprising 1030 concrete mixtures. Linear regression was adopted as a baseline model and compared with support vector regression, random forest regression, and artificial neural networks.
The performance of machine learning models was meticulously assessed using the coefficient of determination, root mean square error, and mean absolute error. Additionally, the models underwent five-fold cross-validation to evaluate their robustness and generalization capabilities. The results unambiguously demonstrate that machine learning models significantly outperform linear regression models.
Cross-validation results confirm the stability and reliability of the developed models. Feature importance analysis reveals that curing age and cement content are the most influential parameters affecting compressive strength, followed by water content, which is consistent with established concrete material behavior. The findings demonstrate that machine learning models, particularly random forest regression, can serve as effective supporting tools for preliminary concrete mix design and performance evaluation.
S. Rouabah· ITEGAM- Journal of Engineeri...· 0 citations
This study presents a machine learning framework for predicting the Young's Modulus (YM) of biomedical titanium alloys to address stress shielding in implant applications. A Deep Neural Network (DNN) was developed using nineteen compositional features and physically meaningful descriptors. Prior to model training, the dataset was preprocessed using Box-Cox and Yeo-Johnson transformations to improve data distribution while preserving all samples. The model architecture incorporates multiple hidden layers with L1/L2 regularization and dropout to enhance generalization. Training was conducted using early stopping, terminating at 585 epochs to prevent overfitting. The optimized model achieved a testing Mean Squared Error (MSE) of 0.294 Gpa and r2-score of 0.82, demonstrating predictive performance. Comparative analysis with XGBoost, Random Forest, Gradient Boosting, and Support Vector Machine confirmed the capability of the proposed DNN model for capturing complex behavior non-linear relationships in Ti-Alloys data.
Muhammad Shahmir Saif, Muhammad Ali Siddiqui, Fahim Raees· Scientific Reports· 0 citations
Results suggest that, within the present five-fold cross-validation setting and limited-sample dataset, RBF kernel ridge regression captures the nonlinear relationships more effectively than conventional linear models; however, broader generalization should be verified using larger datasets and additional validation.
Yuchen Lin· International Conference on...· 0 citations
Among all ML models, eXtreme gradient boosting demonstrated superior predictive performance, effectively capturing the nonlinear and multivariate interactions governing confinement effectiveness.
Javad Shayanfar, J. Barros· Journal of Composites Scienc...· 0 citations
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