Optimization of FFF Process Parameters by Naked Mole-Rat Algorithms with Enhanced Exploration and Exploitation...

Area of Science:

• Manufacturing Engineering
• Materials Science
• Computational Intelligence

Background:

• Fused filament fabrication (FFF) is a widely used additive manufacturing process.
• Mechanical properties of FFF parts are significantly influenced by numerous process parameters.
• Optimization of these parameters is crucial for enhancing part performance and reliability.

Purpose of the Study:

• To investigate the effectiveness of the naked mole-rat algorithm (NMRA) for optimizing FFF process parameters.
• To identify optimal parameter levels for maximizing impact strength, flexural strength, and tensile strength in FFF parts.
• To explore the application of NMRA in manufacturing process optimization.

Main Methods:

• Utilized variants of the naked mole-rat algorithm (NMRA) for optimization.
• Focused on five key FFF process parameters.
• Evaluated the algorithm's performance against conventional and other advanced optimization methods.

Main Results:

• The NMRA successfully identified optimal parameter settings for FFF.
• Achieved significant improvements in impact strength, flexural strength, and tensile strength compared to existing studies.
• Demonstrated the algorithm's capability to yield maximum responses for mechanical properties.

Conclusions:

• The naked mole-rat algorithm (NMRA) is a promising tool for optimizing fused filament fabrication (FFF) processes.
• Findings provide critical insights into parameter settings for enhanced mechanical strength in 3D-printed parts.
• The study supports the application of NMRA in achieving Industry 4.0 objectives for customized product manufacturing.