Multi-Output Prediction and Optimization of CO2 Laser Cutting Quality in FFF-Printed ASA Thermoplastics Using Machine Learning Approaches.

Abstract

This research article examines the CO₂ laser cutting performance of Fused Filament Fabricated Acrylonitrile Styrene Acrylate (ASA) thermoplastics by analyzing the influence of plate thickness, laser power, and cutting speed on four quality characteristics: surface roughness (Ra), top kerf width (Top KW), bottom kerf width (Bottom KW), and bottom heat-affected zone (Bottom HAZ). Forty-five experiments were conducted using five thickness levels, three power levels, and three cutting speeds. To model and predict these outputs, seven machine learning approaches were employed: Autoencoder, Autoencoder-Gated Recurrent Unit, Autoencoder-Long Short-Term Memory, Random Forest, Extreme Gradient Boosting (XGBoost), Support Vector Regression, and Linear Regression. Among them, XGBoost yielded the highest accuracy across all performance metrics. Analysis of Variance results revealed that Ra is mainly affected by plate thickness, Bottom KW by cutting speed, and Bottom HAZ by power, while Top KW is influenced by all three parameters. The study proposes an effective prediction framework using multi-output modeling and hybrid deep learning, offering a data-driven foundation for process optimization. The findings are expected to support intelligent manufacturing systems for real-time quality prediction and adaptive laser post-processing of engineering-grade thermoplastics such as ASA. This integrative approach also enables a deeper understanding of nonlinear dependencies in laser-material interactions.