Robustness optimization for rapid prototyping of functional artifacts based on visualized computing digital twins.

Area of Science:

• Engineering
• Materials Science
• Computational Science

Background:

• Rapid prototyping (RP) of functional artifacts requires robust design methods.
• Integrating multidisciplinary knowledge (thermal, structural) is crucial for complex designs.
• Visualized computing digital twins (VCDT) offer a novel approach for optimizing RP.

Purpose of the Study:

• To develop a robustness optimization method for RP using VCDT.
• To integrate thermal, structural, and multidisciplinary knowledge for visualization in RP scheme design.
• To enhance manufacturing efficacy and electrothermal regulation under hybrid uncertainties.

Main Methods:

• Developed a generalized multiobjective robustness optimization model for RP.
• Optimized fuzzy decision-making membership functions using a genetic algorithm for visualized computing.
• Conducted transient thermodynamic, structural statics, flow field analyses, and electrothermal experiments.
• Utilized infrared thermography for temperature distribution analysis.
• Performed thermal-solid coupled finite element analysis for manufacturability verification.

Main Results:

• Demonstrated the integration of thermal, structural, and multidisciplinary knowledge for VCDT.
• Validated the optimized fuzzy decision-making using a genetic algorithm.
• Presented numerical analysis of a lightweight ribbed ergonomic artifact using VCDT.
• Verified manufacturability through coupled finite element analysis.
• Confirmed the effectiveness of VCDT for robust RP with glass fiber composite materials.

Conclusions:

• The proposed VCDT provides a robust design paradigm for layered RP.
• Achieved a steady balance between electrothermal regulation and manufacturing efficacy.
• The method effectively addresses hybrid uncertainties in functional artifact prototyping.