Mechanical Strength Enhancement of 3D Printed Acrylonitrile Butadiene Styrene Polymer Components Using Neural Network...

Area of Science:

• Materials Science
• Manufacturing Engineering
• Additive Manufacturing

Background:

• Fused filament fabrication (FFF) is a versatile 3D printing technology with widespread applications.
• Acrylonitrile butadiene styrene (ABS) is a common FFF material known for toughness, but its parts often have limitations in mechanical strength and surface finish.
• Optimizing FFF process parameters is crucial for enhancing the performance of ABS components.

Purpose of the Study:

• To investigate a novel optimization tool for FFF process parameters.
• To determine the tensile, flexural, and impact strength of ABS parts using neural network algorithm (NNA) based optimization.
• To compare the effectiveness of NNA against conventional optimization methods.

Main Methods:

• Utilized neural network algorithm (NNA) for optimizing fused filament fabrication (FFF) process parameters.
• Conducted an optimization study focused on acrylonitrile butadiene styrene (ABS) materials.
• Evaluated and compared the performance of NNA with traditional optimization techniques.

Main Results:

• The neural network algorithm (NNA) successfully optimized the process parameters for FFF.
• The study predicted maximum mechanical properties (tensile, flexural, and impact strength) at the suggested parameter settings for ABS parts.
• NNA demonstrated superior efficacy compared to conventional optimization tools.

Conclusions:

• Neural network algorithm (NNA) based optimization is an effective tool for enhancing the mechanical properties of FFF printed ABS parts.
• Optimized process parameters lead to significant improvements in tensile, flexural, and impact strength.
• This study highlights the potential of advanced algorithms in advancing additive manufacturing capabilities.