Multi-objective optimization and prediction of surface roughness and printing time in FFF printed ABS polymer

Area of Science:

• Additive Manufacturing
• Materials Science
• Optimization Techniques

Background:

• Fused Filament Fabrication (FFF) is a widely used additive manufacturing technology.
• Optimizing printing parameters is crucial for enhancing the quality and efficiency of FFF processes.
• Acrylonitrile Butadiene Styrene (ABS) is a common thermoplastic used in FFF, but its surface quality and printing time require optimization.

Purpose of the Study:

• To optimize FFF printing parameters for ABS to improve surface quality and reduce printing time.
• To compare the effectiveness of Particle Swarm Optimization (PSO) and Response Surface Methodology (RSM) for multi-objective optimization.
• To develop and validate mathematical models for predicting printing time and surface roughness.

Main Methods:

• Analysis of Variance (ANOVA) was used for statistical analysis.
• A central composite design within Response Surface Methodology (RSM) was employed.
• Particle Swarm Optimization (PSO) and RSM were coupled with mathematical models for parameter optimization.
• Weighted Aggregated Sum Product Assessment (WASPAS) was used to rank optimization techniques.

Main Results:

• PSO identified optimal printing parameters: 125.6 mm/sec printing speed, 221°C nozzle temperature, and 0.29 mm layer thickness.
• The optimized parameters resulted in a minimum printing time of 24 minutes.
• Surface roughness was improved, with values of approximately 3.92 µm for flat surfaces and 1.78 µm for other surfaces.

Conclusions:

• Particle Swarm Optimization (PSO) is a more effective technique than Response Surface Methodology (RSM) for optimizing FFF printing parameters of ABS.
• The study successfully demonstrated a method for achieving high surface quality and reduced printing time in ABS FFF.
• The developed mathematical models accurately predict the relationship between printing parameters and performance metrics.