Mechanical strength and shape accuracy optimization of polyamide FFF parts using grey relational analysis

Area of Science:

• Materials Science
• Manufacturing Engineering
• Mechanical Engineering

Background:

• Additive manufacturing (AM) offers design flexibility but requires precise parameter control for optimal component properties.
• PA6 is a versatile polymer for 3D printing, yet its mechanical performance is sensitive to process variations.
• Simultaneously optimizing multiple quality characteristics in AM remains a challenge.

Purpose of the Study:

• To investigate the impact of AM process parameters on PA6 component quality.
• To determine the optimal settings for enhancing cylindricity, circularity, strength, Young's modulus, and deformation.
• To apply Gray Relational Analysis (GRA) for multi-objective optimization.

Main Methods:

• Taguchi method was employed to design experimental runs for PA6 parts fabricated on a German RepRap X500® 3D printer.
• Gray Relational Analysis (GRA) was used to calculate Gray Relational Grade (GRG) values for each experiment.
• Analysis of Variance (ANOVA) and Signal-to-Noise (S/N) ratio analysis were performed on GRG data to identify optimal parameters.

Main Results:

• The 8th experimental trial yielded the highest GRG value, indicating superior overall component quality.
• Optimal process parameters were identified as: 60°C chamber temperature, 270°C printing temperature, 0.1 mm layer thickness, and 600 mm/min print speed.
• A verification test using optimal parameters resulted in a 14% improvement in GRG compared to initial experiments.

Conclusions:

• The study successfully identified optimal additive manufacturing parameters for PA6 components using a combined Taguchi-GRA approach.
• The optimized process parameters significantly enhance multiple critical component characteristics simultaneously.
• This research provides a valuable framework for multi-objective optimization in polymer additive manufacturing.