Understanding the UHMR effects used to generate tough ceramic-reinforced polymers

Permanent URL: http://hdl.handle.net/2047/D20383700
Title:
Understanding the UHMR effects used to generate tough ceramic-reinforced polymers
Creator:
Pan, Chunzhou (Author)
Contributor:
Erb, Randall (Advisor)
Shefelbine, Sandra (Committee member)
Karma, Alain (Committee member)
Müftü, Sinan (Committee member)
Jamali, Safa (Committee member)
Language:
English
Publisher:
Boston, Massachusetts : Northeastern University, 2020
Date Accepted:
July 2020
Date Awarded:
August 2020
Type of resource:
Text
Genre:
Dissertations
Format:
electronic
Digital origin:
born digital
Abstract/Description:
This thesis aims (i) to better understand near-field interactions in magnetic colloids, (ii) to study the particle distribution pattern effects on the magnetic response of magnetically labeled ceramic particles, and (iii) to apply these learnings to process ceramic-polymer composites with anisotropic micro-structures enabling a better understanding of fundamental structure-property relationships of fracture toughness in anisotropic composites. This work first focuses on predicting the magnetostatic interactions of super- paramagnetic particles in a transient simulation by applying machine learning to this field for the first time. Conventional predictive models for magnetostatic interactions include the widely applied and analytic dipole model, a matrix-based mutual dipole model and a numerically intensive but precise finite element model. Due to the accuracy and computational challenges of the near- field magnetic interaction with high susceptibility particles, and inspired by the success of machine learning approach in complex problems such as solid mechanics and hydrodynamics, we hypothesized that machine learning would deliver an accurate and fast predictive model that would enable large scale simulations. Simple neural network machine learning models showed limitations. To overcome this, we combined the fast computation advantage of dipole model (low fidelity data) with the enhanced accuracy of finite element modeling (high fidelity data). A multi-fidelity neural network model is built on this database and found to predict magnetostatic interactions in simple particle systems extremely well. However, this effort uncovered some limitations and concerns with the machine learning approach. The accuracy, benefits and limitations of this approach are discussed in this work. One of the motivations for developing a predictive machine learning code was to be able to model ceramic microparticles that are coated with tens of thousands of magnetic nanoparticles that have found many applications in composites, active optics, and micro- rheology. These labeled ceramics dramatically orient in applied magnetic fields with surprising speed. The discrete model developed in this thesis work is applied to model these massive multi-body systems and compared to previously proposed continuum analytic models that treat the labelled particles as magnetic ellipsoidal shells. In our discrete model, we employ the Monte Carlo method to find the average response of randomized assemblies of tens of thousands of magnetic nanoparticles adsorbed to the ceramic surface. The combined discrete model and Monte Carlo approach brings into the calculation the correct first principles of magnetostatic interactions between magnetic nanoparticles in the coating layer. This approach, therefore, provides a means for measuring the influence on the magnetic response of the platelet brought on the distribution pattern of the nanoparticles that can be experimentally tuned through controlled aggregation. These developed methodologies allow us to probe many of our hypotheses able these magnetic nanoparticle labelled ceramics including the root cause of their dramatic responses. The magnetic response of the labelled ceramic platelets can be applied to manipulate the micro-structure of ceramic-polymer composites to create fully aligned composites that exhibit toughness with sufficient enough control to establish new fundamental understandings in structure-property relationships in this field. The thesis turns attention to these systems and discusses processing, structure, and property paradigms within this class of materials. First, the process of manufacturing alumina reinforced acrylate polymer composites is discussed. Next, the experimental characterization of their mechanical properties is detailed. Finally, details about resultant fracture toughness properties are discussed including the crack kinking behavior in thin samples with anisotropic fracture energy. The effects of non-singular stress at the front of the crack tip associated with the stabilizing/destabilizing effect on the crack kinking will be discussed under the linear elastic fracture mechanics framework using plane stress assumptions. A phase-field simulation result carried out by collaborators will also be presented. Ultimately, we will show that the fracture energy anisotropy and the non-singular stress that can be modulated by the process zone size will work together to predict the existence of an anisotropic threshold for crack kinking and how this threshold is strongly dependent on load distribution.
Subjects and keywords:
mechanical engineering
DOI:
https://doi.org/10.17760/D20383700
Permanent URL:
http://hdl.handle.net/2047/D20383700
Use and reproduction:
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