ArticleScientific reports2026
Machine learning and response surface methodology for optimization and prediction of tribological performance of PLA/rice husk biochar composites.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Response Surface Methodology for Biochar Performance Optimization: A Comprehensive Bibliometric Review.Molecules (Basel, Switzerland) · 2026Review
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Authors and funding
7 authors.
Funding
Abstract
The present study investigates the tribological behaviour of polylactic acid (PLA) composites reinforced with rice husk biochar (RHBC) through an integrated approach combining experimentation, statistical optimization, and machine learning. PLA/RHBC composites with filler contents ranging from 10 to 20 wt% were fabricated using fused deposition modeling (FDM). A Box-Behnken design (BBD) within the framework of Response Surface Methodology (RSM) was employed to systematically examine the effects of key process parameters-printing angle, infill type, nozzle temperature, and filler content at three levels and across 24 experimental runs. Tribological performance was evaluated in terms of wear rate and coefficient of friction (COF) using a pin-on-disc setup under applied loads of 10, 20, and 30 N. Analysis of variance (ANOVA) revealed that the applied load is the most significant factor influencing wear rate, whereas COF is predominantly governed by the combined effects of the printing parameters. Optical microscopy further indicated that incorporating RHBC reduces ploughing and improves interfacial stability. To enhance predictive capability, a machine learning framework incorporating Multiple Linear Regression (MLR), Extreme Gradient Boosting (XGBoost), and Artificial Neural Networks (ANN) was implemented. Among these, the ANN model demonstrated superior performance, achieving the highest prediction accuracy for wear rate with R² values of 0.9852, 0.9845, and 0.9891 at 10, 20, and 30 N, respectively. A similar trend was observed for COF, where ANN again outperformed the other models, yielding R² values of 0.9892, 0.9880, and 0.9910 at the corresponding loads. The integration of RSM with machine learning enables efficient optimization of FDM parameters and accurate prediction of tribological performance. This highlights the suitability of RHBC/PLA composites for lightweight, wear-resistant applications such as bearings, bushings, and sliding components.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.