Experimental analysis and numerical modeling of particle embedment and fracture in the solid particle erosion of ductile materials
Bibliographic record
Abstract
Embedment and fracture of abrasives are two often neglected important phenomena that can affect material removal occurring in industrial processes that involve high speed impact of particles on relatively ductile targets. This thesis proposes new methodologies to predict the likelihood of particle embedment and fracture for a typical solid particle erosion application. Double-pulsed laser shadowgraphy was used to measure the instantaneous orientation of angular 89-363 μm SiC particles within a micro-abrasive jet, in order to assess whether their orientation affected the propensity for particle embedment. A tendency for particles to orient with the jet axis was measured and successfully modelled (<9% error), with larger abrasives more likely to orient. The measured instantaneous orientation of particles was used to generate a three-dimensional coupled finite element and smoothed particle hydrodynamics model capable of simulating the particle embedment. Use of various combinations of process parameters yielded embedment predictions that agreed with measured ones with, at most, a 16% error. Increases in particle size, orientation angle, and velocity were found to enhance the propensity for embedment. Double-pulsed laser shadowgraphy was used to record the impact and fracture of abrasives upon impact. A numerical model that utilized an Element Free Galerkin (EFG) technique with a novel scheme for generating realistic three-dimensional particle geometries was used to simulate the particle fracture. For a wide variety of process parameters, the numerical predictions of particle average size, roundness and rebound velocity agreed with the corresponding measurements to within 10%, at most. The propensity for particle fracture was found to depend on the magnitude of particle kinetic energy perpendicular to the target. It was confirmed that at the same incident velocity, larger particles were more likely to fracture. However, for the same kinetic energy, smaller particles were more likely to fracture. To the best knowledge of the author, this thesis is the first to report measurements of particle orientation and particle fracture in abrasive jets, and the first to develop numerical modeling of particle fracture and embedment. The results have important implications for erosion testing and abrasive jet machining operations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".