GrowCAD: bioinspired mathematical design for additive manufacturing

Area of Science:

• Engineering
• Computer Science
• Biomimetics

Background:

• Additive Manufacturing (AM) offers significant benefits but faces challenges in design implementation.
• Current AM design processes are complex, involving geometry representation, software, and human problem-solving.
• Enhancing engineer knowledge and creativity is crucial for overcoming design limitations.

Purpose of the Study:

• To develop a novel design framework for Additive Manufacturing.
• To enhance human knowledge and creativity in AM design.
• To address the challenges of geometry representation and manufacturability in AM.

Main Methods:

• A bioinspired methodology is introduced, using plant growth as an analogy for the layer-by-layer AM process.
• Development of a novel length-polar-projection coordinate system and algebraic definitions for geometric features.
• Creation of the GrowCAD™ design framework based on this mathematical representation.

Main Results:

• The GrowCAD™ framework enables manufacturability analysis, parametric editability, and CAD compatibility.
• Qualitative analysis validated shape fidelity, efficiency, detection of non-manufacturable geometry, and printability.
• The developed system successfully generated and printed complex geometries.

Conclusions:

• GrowCAD™ provides a simplified and intuitive method to enhance engineer knowledge and creativity in AM design.
• The algebraic geometry representation facilitates manufacturability analysis and design iterations.
• This bioinspired approach offers a promising solution for wider AM implementation.