Manufacturing Technology 2024, 24(6):960-968 | DOI: 10.21062/mft.2024.095

Statistical Analysis and Machine Learning-based Modelling of Kerf width in CO2 Laser Cutting of PMMA

Ema Vasileska ORCID..., Ognen Tuteski ORCID..., Boban Kusigerski ORCID..., Aleksandar Argilovski ORCID..., Mite Tomov ORCID..., Valentina Gecevska ORCID...
Faculty of Mechanical Engineering - Skopje, Ss. Cyril and Methodius University in Skopje, Ul. Ruger Boskovic 18, 1000 Skopje, North Macedonia

Recently, engineering polymers like PMMA have increasingly replaced traditional materials in industry where feasible, with CO2 laser cutting gaining attention for its high quality and speed in processing these materials. Achieving precise cuts is crucial for product accuracy, with kerf width serving as a key quality attribute to ensure quality and functionality of the final product. This study focuses on the im-pact of three critical process variables: stand-off distance, laser power, and cutting speed, on the kerf width in CO2 laser cutting of PMMA. Through a full-factorial experiment, the process parameters are systematically varied to understand their individual and interaction effects on the cutting process. The kerf width is measured as an indicator of precision using an optical microscope to evaluate the quality of the laser cuts. To address the non-linear relationships between these process parameters and kerf width, several machine learning models were utilized. Performance comparisons indicated that the Artificial Neural Network (ANN) model provided the highest accuracy, with R² values of 0.98 for the validation dataset and 0.95 for the testing dataset. The optimized ANN model offers a robust tool for parameter optimization, facilitating the determination of optimal settings to achieve the desired kerf width while ensuring productivity.

Keywords: CO2 laser cutting, Kerf width, Machine learning, Process modelling

Received: July 15, 2024; Revised: November 12, 2024; Accepted: November 26, 2024; Prepublished online: December 16, 2024; Published: December 21, 2024  Show citation

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Vasileska E, Tuteski O, Kusigerski B, Argilovski A, Tomov M, Gecevska V. Statistical Analysis and Machine Learning-based Modelling of Kerf width in CO2 Laser Cutting of PMMA. Manufacturing Technology. 2024;24(6):960-968. doi: 10.21062/mft.2024.095.
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