Enhancing machining accuracy of banana fiber-reinforced composites with ensemble machine learning

Abstract

Through innovative predictive modeling, this study advances Abrasive Water Jet Machining (AWJM) for banana fiber-reinforced biocomposites. Utilizing Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM) models, hyperparameters are meticulously tuned for predicting Surface Roughness (Ra), Material Removal Rate (MRR), and Kerf Angle (Ka). Optimal configurations are identified, such as 3–6-6–4-1, 3–5-5–4-1, and 3–5–4–4-1 for ANN models, and 250, 100, and 50 units for LSTM models. Beyond individual models, the study explores stacking ensemble models, merging ANN and LSTM strengths with a Linear Regression final estimator, showcasing robust performance validated with high R-squared values. Regression models from Design Expert 13 contribute to understanding process parameter-outcome relationships. Optimized input parameters offer insights into minimizing surface roughness and kerf angle while maximizing MRR. This holistic approach, integrating advanced machine learning and ensemble learning, enhances predictive accuracy for banana-reinforced biocomposites, providing a versatile framework for diverse materials processing applications.