Online Measurement for Parameter Discovery in Fused Filament Fabrication

Area of Science:

• Additive Manufacturing
• Materials Science
• Mechanical Engineering

Background:

• Fused filament fabrication (FFF) relies on precisely tuned process parameters.
• Manual tuning of parameters is time-consuming and material-specific.
• Existing methods lack adaptability across diverse FFF machines and materials.

Purpose of the Study:

• To develop an automated method for generating fused filament fabrication (FFF) process parameters.
• To enable parameter generation that is adaptable across various FFF machines and materials.
• To reduce the time and expertise required for optimizing FFF printing settings.

Main Methods:

• Utilized an instrumented extruder to empirically fit a function relating nozzle pressure to flow rate and temperature for specific machine-material configurations.
• Developed a novel method to derive actual flow rate and temperature parameters from relative pressure measurements and temperature offsets.
• Validated the parameter generation method across multiple FFF machines and diverse materials, including previously untested ones.

Main Results:

• Successfully generated a single set of input parameters that yielded optimal printing results across all tested machine and material combinations.
• Demonstrated the method's efficacy even with materials not previously used in the study.
• Showcased the ability to automatically select parameters by leveraging machine-generated data capturing FFF phenomenology.

Conclusions:

• The developed method automates the generation of fused filament fabrication (FFF) process parameters, offering a significant improvement over manual tuning.
• This approach enhances adaptability and efficiency in FFF by enabling parameter selection across diverse hardware and material substrates.
• Machine-generated data, reflecting the fundamental physics of FFF, can be effectively utilized for automatic parameter optimization.