TOPSIS-based multi-response optimization for improving CO2 laser cutting quality of 3D printed PLA

Abstract

In the present study, CO2 laser cutting was performed on PLA plates produced using fused filament fabrication (FFF), one of the most common additive manufacturing methods. Main process factors included the thickness of the plates (2.5 and 3.5 mm), laser power (90, 95, and 100 W), and cutting speed (3, 6, and 9 mm/s). The cutting quality was evaluated based on the following four key performance responses: surface roughness (Ra), top kerf width (Top KW), bottom kerf width (Bottom KW), and the bottom heat-affected zone (Bottom HAZ). Main effect plots illustrated the impact of cutting parameters on each response. The analysis of variance (ANOVA) proved that cutting speed had the most significant effect on surface roughness and kerf widths as it was almost 93.51% and 94.17%, respectively, of the total variation. All responses were modeled via regression, and high R² values ranging from 88.01% to 98.97% showed the excellent model fitness in predicting experimental results. In view of simultaneous treatments of joint quality characteristics in an optimal cutting condition, a well-known multicriteria decision-making method, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), was employed. It shows improvement in surface roughness, width of the kerfs, and values of HAZ under conditions. Based on the results of TOPSIS, the best cutting condition was found to be a plate thickness of 2.5 mm, with a laser power of 90 W and a cutting speed of 9 mm/s. Such conditions secured a 51.75% improvement in surface roughness, with a significant reduction in both kerf width and HAZ, thus presenting a positive guideline toward improving post-processing quality from laser cutting on FFF-printed PLA parts.

Keywords

• CO2 laser cutting
• 3D printed PLA
• Multi-response optimization
• TOPSIS method
• Surface quality

Project Number

This work was supported by Scientific Research Projects Coordination Unit of Bandirma Onyedi Eylul University under Project Number: BAP 24-1003-003.

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