Bibliographic record
Abstract
The ability to process composite structures with a high degree of dimensional control remains a barrier to the further implementation of composite materials in commercial applications. Poor control over final part shape can necessitate custom shimming of composite parts or remachining of tooling, resulting in excessive manufacturing costs. Mechanical interaction between the tool and part has been identified as a significant contributor to dimensional control problems yet this phenomena remains poorly understood. Tool-part interaction can often manifest itself in the warpage of initially flat laminates. In the present work an experimental study was performed to identify the effect that part geometry and process variables had on this warpage. Part geometry had a much greater influence on warpage than autoclave process pressure did, while tool surface condition was not observed to have any significant effect. A second experimental study was performed whereby a thin aluminum tool was instrumented with strain gages. The mechanical strain induced in the instrumented tool provided a means for estimating the magnitude and distribution of shear stress operative at the tool-part interface. Both sliding and sticking interface conditions were observed to occur at various times throughout the cure cycle. The interfacial shear stress increased with increasing part degree of cure. An analytical model to predict warpage was developed based on the conclusions of the instrumented tool investigation. This model agreed well with the trends in part warpage which were identified experimentally. Process induced warpage was also simulated using an existing numerical process model. The current method of accounting for tool-part interactions via an elastic shear layer was unable to correctly represent the interface behavior, however, reasonable agreement with experimental results was possible by using a sufficiently low modulus shear layer. The value assigned to the shear modulus of the part was also observed to have a significant effect on the success with which part warpage could be modelled.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".