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Richard Wiebe

Publications and source records attributed to Richard Wiebe.

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Experimental characterization of cohesive laws for mode-II interlaminar fracture in geometrically scaled composites using through-thickness deformation analysis

This work proposes an experimental framework to characterize a cohesive law for mode-II interlaminar fracture and demonstrates its implementation. For a size effect study, geometrically scaled end-notched flexure specimens were tested using microscopic and macroscopic digital image correlation (DIC) systems. The fracture energy was characterized using a compliance calibration method and Ba\v{z}ant's type-II size effect law for comparison. In the proposed experimental framework, the DIC data were post-processed using three steps: coordinate transformation, curve fitting, and through-thickness deformation analysis. Different magnitudes of separation values were measured from different sizes at fracture loads, implying size effect and partial development of cohesive laws. Modeling and simulations were intended to validate the proposed method and demonstrate the utilization of the experimental data. Additionally, challenges related to finding a single cohesive law for geometrically scaled specimens of a single material were exposed. A single cohesive law for the scaled specimens was developed and proposed as a material property of the specimen material. The fracture energy of the single law was smaller than the energy obtained from the size effect analysis, while the sizes of fracture process zones at fracture loads were smaller than the experimental measurements. However, the global fracture behaviors of the models showed good agreement with the experimental data of the mid-size specimen while showing reasonable agreement with the other sizes. Furthermore, the single law successfully captured local fracture behaviors by showing partial cohesive zone development at the fracture loads and matching the microscopic measurement of the separation values.

physics.app-ph

Data-driven prediction and analysis of chaotic origami dynamics

Advances in machine learning have revolutionized capabilities in applications ranging from natural language processing to marketing to health care. Here, we demonstrate the efficacy of machine learning in predicting chaotic behavior in complex nonlinear mechanical systems. Specifically, we use quasi-recurrent neural networks to predict extremely chaotic time series data obtained from multistable origami systems. Additionally, while machine learning is often viewed as a "black box", in this study we conduct hidden layer analysis to understand how the neural network can process not only periodic, but also chaotic data in an accurate manner. Also, our approach shows its effectiveness in characterizing and predicting chaotic dynamics in a noisy environment of vibrations without relying on a mathematical model of origami systems. Therefore, our method is fully data-driven and has the potential to be used for complex scenarios, such as the nonlinear dynamics of thin-walled structures and biological membrane systems.

cond-mat.soft