# Data-Driven AI Approach for Optimizing Processes and Predicting Mechanical Properties of Boron Nitride Nanoplatelet-Reinforced PLA Nanocomposites

## TL;DR

This study uses machine learning to optimize the mechanical properties of boron nitride nanoplatelet-reinforced PLA composites, showing significant improvements in strength and hardness.

## Contribution

The novel use of XGBoost machine learning models to predict and optimize mechanical properties of BNNP-PLA composites with high accuracy.

## Key findings

- 0.04 wt.% BNNP loading increased tensile strength, Young’s modulus, and hardness by 18.6%, 32.7%, and 20.5%, respectively.
- XGBoost achieved R2 values above 98% for tensile strength and 96% for hardness predictions.
- Injection temperature was the dominant factor for tensile strength and Young’s modulus, while BNNP composition mainly influenced hardness.

## Abstract

This research explores the optimization of mechanical properties and predictive modeling of polylactic acid (PLA) reinforced with boron nitride nanoplatelets (BNNPs) using data-driven machine learning (ML) models. PLA-BNNP composites were fabricated through injection molding, with a focus on how key processing parameters influence their mechanical performance. A Taguchi L27 orthogonal array was applied to assess the effects of BNNP composition (0.02 wt.% and 0.04 wt.%), injection temperature (135–155 °C), injection speed (50–70 mm/s), and pressure (30–50 bar) on properties such as tensile strength, Young’s modulus, and hardness. The results indicated that a 0.04 wt.% BNNP loading improved tensile strength, Young’s modulus, and hardness by 18.6%, 32.7%, and 20.5%, respectively, compared to pure PLA. Taguchi analysis highlighted that higher BNNP concentrations, along with optimal injection temperatures, improved all mechanical properties, although excessive temperatures compromised tensile strength and modulus, while enhancing hardness. Analysis of variance (ANOVA) revealed that injection temperature was the dominant factor for tensile strength (68.88%) and Young’s modulus (86.39%), while BNNP composition played a more significant role in influencing hardness (78.83%). Predictive models were built using machine learning (ML) models such as Random Forest Regression (RFR), Gradient Boosting Regression (GBR), and Extreme Gradient Boosting (XGBoost). Among the ML models, XGBoost demonstrated the highest predictive accuracy, achieving R2 values above 98% for tensile strength, 92–93% for Young’s modulus, and 96% for hardness, with low error metrics i.e., Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE). These findings underscore the potential of using BNNP reinforcement and machine learning-driven property prediction to enhance PLA nanocomposites’ mechanical performance, making them viable for applications in lightweight packaging, biomedical implants, consumer electronics, and automotive components, offering sustainable alternatives to petroleum-based plastics.