Lightweight Design of Variable-Stiffness Cylinders with Reduced Imperfection Sensitivity Enabled by Continuous Tow...

Area of Science:

• Mechanical Engineering
• Materials Science
• Computational Mechanics

Background:

• Lightweight cylindrical shells are prone to buckling and imperfection sensitivity.
• Optimizing designs for reduced sensitivity and improved stiffness is crucial for structural integrity.
• Existing methods often involve complex reinforcements or lack manufacturability.

Purpose of the Study:

• To apply continuous tow shearing (CTS) in a manufacturable design parameterization for lightweight cylindrical shells.
• To investigate machine learning methods for optimizing these designs.
• To achieve a compromise between mass reduction and enhanced post-buckled stiffness.

Main Methods:

• Utilized Koiter's asymptotic nonlinear method to predict post-buckled stiffness.
• Employed machine learning algorithms (Support Vector Machine, Kriging, Random Forest) for design optimization.
• Developed an optimization formulation balancing mass and stiffness, constrained by positive post-buckled stiffness index and minimum design load.

Main Results:

• The machine learning-based framework successfully solved the inverse problem, yielding lightweight designs with reduced imperfection sensitivity.
• Compared the performance of different machine learning strategies in driving the optimization process.
• Identified optimal designs on the compromise frontier between mass and stiffness.

Conclusions:

• Continuous tow shearing (CTS) offers a viable approach for creating manufacturable, imperfection-insensitive lightweight cylindrical shells.
• Machine learning provides an effective tool for optimizing complex structural designs with competing objectives.
• The developed methodology aligns with and complements existing literature on enhancing shell stability.