Al-based optimization of surface roughness in wood band sawing using polynomial regression and differential evolution - Selimi,Bujar; Temelkova, Anastasija; Jevtoska, Elena (AAB College, Pristina, Kosovo, 2025)
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
Keywords: surface roughness, wood band sawing, artificial intelligence, polynomial regression, optimization