Multi-objective optimization of mechanical properties in PLA/SCG/silane composites using synthetic data and XGBoost
Atthaphon Ariyarit, Attasit Wiangkham, Phatthawit Siripaiboonsub, Jittiwat Nithikarnjanatharn, Wannisa Nutkhum, Prasert Aengchuan
TL;DR
This study uses data-driven methods to optimize the mechanical properties of eco-friendly composites made from polylactic acid, spent coffee grounds, and silane.
Contribution
A novel XGBoost-NSGA-II framework is introduced for multi-objective optimization of composite materials using synthetic data.
Findings
A data-driven model achieved high accuracy in predicting tensile strength and hardness of composites.
Optimal formulations were identified using NSGA-II, favoring higher PLA and moderate SCG and silane contents.
The best formulation achieved 53.33 MPa tensile strength and 80.06 Shore D hardness.
Abstract
Polylactic acid (PLA) composites reinforced with spent coffee grounds (SCG) and modified with a silane coupling agent (VTMS) offer a sustainable alternative for applications requiring biodegradability and enhanced mechanical performance. This study employed a data-driven approach to optimize tensile strength and Shore D hardness by varying the contents of PLA, SCG, and silane. Seventy-five composite samples were fabricated and tested, exhibiting tensile strengths of 26.5–57.9 MPa and hardness values of 77.5–80.8 Shore D. A multi-output XGBoost regression model, trained on 60% of the data and validated on the remaining 40%, achieved strong predictive accuracy (R2 = 0.884, MSE = 12.64 for tensile strength; R2 = 0.908, MSE = 0.071 for hardness) after augmentation with 159 synthetic samples generated via jittering, Gaussian noise, and kernel density estimation. Multi-objective optimization…
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Taxonomy
TopicsNatural Fiber Reinforced Composites · biodegradable polymer synthesis and properties · Additive Manufacturing and 3D Printing Technologies