Leveraging physical intelligence for the self-design of high performance engineering structures.

Area of Science:

• Engineering
• Computational Intelligence
• Manufacturing

Background:

• Complex engineering design relies on predictive models, facing challenges with uncertainties.
• Existing methods involve a performance-robustness trade-off due to explicit uncertainty handling.
• Difficult-to-model physics and manufacturing variability pose significant design hurdles.

Purpose of the Study:

• To introduce a novel self-design paradigm integrating physical intelligence into the design loop.
• To leverage real-time experimental observations for implicit handling of uncertainties.
• To circumvent the performance-robustness trade-off and achieve enhanced performance in engineered structures.

Main Methods:

• A self-design paradigm was proposed, closing the loop between design and manufacturing.
• Physical intelligence from real-time experimental observations was leveraged.
• The approach was applied to design a simply-supported plate with a vibration-reducing beam absorber using a 3D printer and online vibration testing.

Main Results:

• The self-design paradigm implicitly accounted for manufacturing variability and complex physics.
• Enhanced performance was achieved compared to standardized designs, circumventing the performance-robustness trade-off.
• Tailored design realizations demonstrated the paradigm's potential for high-performance applications.

Conclusions:

• The proposed self-design paradigm offers a significant advancement over traditional computational intelligence approaches.
• Implicit handling of uncertainties via in situ measurements leads to superior performance and robustness.
• This approach enables tailored engineering solutions with broad applicability in high-performance systems.