Conductive Additive Manufactured Acrylonitrile Butadiene Styrene Filaments: Statistical Approach to Mechanical and...

Area of Science:

• Materials Science
• Additive Manufacturing
• Electrical Engineering

Background:

• Additive manufacturing (AM) utilizes digital 3D data to build components layer by layer.
• Conductive composite materials, particularly carbon nanostructure-infused polymers, are increasingly used in AM.
• Acrylonitrile butadiene styrene (ABS) filled with carbon black offers high strength and electrical conductivity, making it suitable for advanced AM applications.

Purpose of the Study:

• To investigate the mechanical and electrical properties of acrylonitrile butadiene styrene (ABS) processed via fused deposition modeling (FDM).
• To determine the influence of fabrication parameters on the tensile strength and electrical resistance of 3D-printed ABS.
• To develop machine learning models for predicting the real-time tensile strength and resistance of conductive ABS parts.

Main Methods:

• Samples of carbon black-filled ABS were fabricated using a fused deposition modeling (FDM) printer.
• Mechanical tests were performed to measure maximum tensile strength, with infill volume, layer height, infill type, and printing direction as variables.
• Electrical tests analyzed sample resistance, with length, nozzle temperature, and measurement temperature as variables.
• Statistical analysis and machine learning algorithms (Gaussian Process Regression, Support Vector Machine) were employed for data evaluation and prediction model creation.

Main Results:

• Confirmed the linear relationship between electrical resistance and the length of 3D-printed conductive ABS samples.
• Demonstrated that fabrication settings significantly influence the mechanical strength of the printed parts.
• Successfully created prediction models for real-time tensile strength and electrical resistance using machine learning.

Conclusions:

• Fabrication parameters critically affect both mechanical and electrical properties of conductive ABS in AM.
• The study provides insights into optimizing 3D printing processes for conductive materials.
• Machine learning offers a viable approach for predicting and controlling the performance of 3D-printed components.