Expert-guided optimization for 3D printing of soft and liquid materials

Area of Science:

• Materials Science
• Engineering
• Manufacturing Technology

Background:

• Additive manufacturing (AM) offers design freedom and customization but presents challenges in optimizing numerous process parameters.
• 3D printing soft, deformable materials like liquid polydimethylsiloxane (PDMS) requires specialized experimental methods.
• Optimizing AM for liquid resins is complex due to the vast parameter space.

Purpose of the Study:

• To develop and apply an expert-guided optimization (EGO) strategy for improving 3D printing of liquid PDMS elastomer resins.
• To systematically explore and refine the parameter space for high-performance AM of soft materials.
• To achieve enhanced detail and fidelity in 3D printed PDMS and epoxy objects.

Main Methods:

• An expert-guided optimization (EGO) strategy was developed, involving expert screening, a hill-climbing algorithm, and expert decision-making.
• The EGO strategy systematically searched the parameter space for optimal 3D printing settings.
• Calibration objects (hollow cylinder, five-sided hollow cube) were used to evaluate the optimization process via a multi-factor scoring system.

Main Results:

• The EGO strategy successfully identified optimal print parameters for liquid PDMS elastomer resin.
• The optimized settings enabled the printing of complex 3D objects with unprecedented detail and fidelity.
• Successfully printed objects included intricate designs like a twisted vase, water drop, toe, and ear.

Conclusions:

• The developed EGO strategy provides a structured approach to optimize complex AM processes for soft materials.
• This method significantly improves the quality and fidelity of 3D printed PDMS and epoxy components.
• Expert guidance combined with algorithmic search is effective for advancing AM of liquid resins.