Al-based optimization of surface roughness in wood band sawing using polynomial regression and differential evolution

Abstract

This study presents an artificial intelligence–assisted optimization framework for minimizing surface roughness (Rₘₐₓ) in wood band sawing. A third-order Polynomial Regression Model (PRM-3) was developed and trained on experimentally collected data obtained under varied cutting conditions, including angle, height, and feed rate. To ensure robust generalization, configurations with a 60° cutting angle were deliberately excluded from training and used solely for out-of-sample validation. PRM-3 was integrated with the Differential Evolution (DE) algorithm to identify optimal process configurations. For comparative purposes, a Gaussian Process Regression (GPR) model was also implemented to evaluate the relative generalization capability. Results confirmed the superior performance of PRM-3 in terms of accuracy, stability, and generalization, compared to GPR, demonstrating high potential for deployment in intelligent wood machining applications. The proposed framework represents a valuable integration of interpretable modeling and automated optimization for surface quality control in industrial settings