Multi-objective optimization of process parameters for laser cutting polyethylene using fuzzy AHP-based MCDM methods

This experimental study examines how cutting parameters influence surface roughness (Ra), kerf width (KW), heat-affected zone (HAZ), and material removal rate (MRR) during CO 2 laser cutting of polyethylene (PE) material. The aim is to determine the optimal cutting conditions by integrating fuzzy analytic hierarchy process (AHP) and multi-criteria decision-making (MCDM) methods. Control factors (i.e. focal length, power, and cutting speed) were varied in a full factorial experimental design. Criterion weights for the responses were calculated with the help of the AHP method in fuzzy environment, resulting in weights of 0.442 for Ra, 0.102 for KW, 0.287 for HAZ, and 0.169 for MRR. These weights were integrated into MCDM methods (i.e. combined compromise solution [CoCoSo], technique for order preference by similarity to ideal solution [TOPSIS], and Vlsekriterijumska Optimizacija I Kompromisno Resenje [VIKOR]) to rank the experiments and identify the optimal parameters. The optimal cutting condition was found to be a focal length of 8 mm, power of 90 W, and cutting speed of 12 mm/s. Spearman's rank correlation coefficients were applied for analysis of the consistency among the hybrid approaches, with results indicating a very strong positive relationship between them. This comprehensive approach ensures precise and efficient laser cutting of PE, contributing valuable insights to the field of manufacturing and materials processing.

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Publication Details

Journal
Proceedings of the Institution of Mechanical Engineers Part E Journal of Process Mechanical Engineering
Published
2025-02-26
DOI
https://doi.org/10.1177/09544089251319202
Citations
27
Primary Topic
Quality Function Deployment in Product Design
Type
article
Field-Weighted Citation Impact
74.53
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article

Multi-objective optimization of process parameters for laser cutting polyethylene using fuzzy AHP-based MCDM methods

Oğuzhan Der, Gökhan Başar
27 citations
Proceedings of the Institution of Mechanical Engineers Part E Journal of Process Mechanical Engineering
Quality Function Deployment in Product Design
74.53
article

Multi-objective optimization of process parameters for laser cutting polyethylene using fuzzy AHP-based MCDM methods

Oğuzhan Der, Gökhan Başar
article en
27 citations

Abstract

This experimental study examines how cutting parameters influence surface roughness (Ra), kerf width (KW), heat-affected zone (HAZ), and material removal rate (MRR) during CO 2 laser cutting of polyethylene (PE) material. The aim is to determine the optimal cutting conditions by integrating fuzzy analytic hierarchy process (AHP) and multi-criteria decision-making (MCDM) methods. Control factors (i.e. focal length, power, and cutting speed) were varied in a full factorial experimental design. Criterion weights for the responses were calculated with the help of the AHP method in fuzzy environment, resulting in weights of 0.442 for Ra, 0.102 for KW, 0.287 for HAZ, and 0.169 for MRR. These weights were integrated into MCDM methods (i.e. combined compromise solution [CoCoSo], technique for order preference by similarity to ideal solution [TOPSIS], and Vlsekriterijumska Optimizacija I Kompromisno Resenje [VIKOR]) to rank the experiments and identify the optimal parameters. The optimal cutting condition was found to be a focal length of 8 mm, power of 90 W, and cutting speed of 12 mm/s. Spearman's rank correlation coefficients were applied for analysis of the consistency among the hybrid approaches, with results indicating a very strong positive relationship between them. This comprehensive approach ensures precise and efficient laser cutting of PE, contributing valuable insights to the field of manufacturing and materials processing.

Proceedings of the Institution of Mechanical Engineers Part E Journal of Process Mechanical EngineeringVol. 239(4)
Osmaniye Korkut Ata University (TR), California Maritime Academy (US), Bandırma Onyedi Eylül University (TR)
Openalex Percentile: Top 0%
Quality Function Deployment in Product Design
74.53
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