Solid particle erosion of particulate-reinforced epoxy composites
Bibliographic record
Abstract
<p>Polymeric composites are exposed to erosion by solid particles in many different applications in various industries. The objective of this dissertation is to understand and model the material removal mechanisms occurring when particle-reinforced epoxy composites are subject to solid particle erosion.</p> <p>Experiments on alumina particulate-reinforced epoxy composites using angular silicon carbide abrasive particles revealed that the dominant erosion mechanisms depended, to a large extent, on the magnitude of three dimensionless parameters. These parameters depended on process variables such as abrasive particle size, kinetic energy, reinforcement size, content and material properties. It was also observed that the use of coupling agent was effective in strengthening the composites interfacial strength leading to improving their erosion resistance.</p> <p>It was found that with alumina reinforcements, reinforcement erosion and fracture resulted in little to no improvement in erosion resistance compared to the neat epoxy. However, when soft rubber or zirconia reinforcements were used, significant gains in erosion resistance were observed. For the rubber reinforcements, a novel rule of mixtures based on the erosion rates and areal coverages of the two phases is proposed. Using a model that was developed to predict the areal coverages given the reinforcement size and volume fraction, the mixture rule was shown to accurately predict the measured erosion rates.</p> <p>To better understand the erosion mechanisms of the zirconia-reinforced system, numerical models of the erosion of the neat epoxy and composite were developed. Using smoothed particle hydrodynamics (SPH) coupled with finite elements (FE), the developed model correctly predicted the measured neat epoxy steady-state erosion rate, incubation period length, and dominant erosion mechanisms. The SPH/FE model of the zirconia-reinforced composites assumed that, because of their relatively high fracture toughness, the reinforcements did not fracture. The model accurately predicted the significant improvement of the erosion resistance and the erosion mechanisms associated with using these tougher ceramic particles, at both oblique and perpendicular incidences.</p> <p>The methodologies presented in this thesis aid in determining the process and material parameters that most affect the erosion of particulate composites and, therefore, can be used in the material design process to improve erosion resistance. Although only particle-reinforced epoxy matrix-systems were studied, it is likely that many of the same phenomena will be present in other particulate polymer-matrix composites.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".