Aspects of in-situ consolidation of thermoplastic laminates manufactured by automated tape placement: A material deformation study
Bibliographic record
Abstract
Understanding and predicting the deformation of the thermoplastic material during the in-situ consolidation Automated Tape Placement (ATP) process is essential for manufacturing composite laminates exempt of laps and gaps. This thesis describes the experimental and theoretical analyses conducted to characterise the in-situ deformation behaviour of a pre-impregnated carbon/PEEK composite system: APC-2/IM7. A "static" or "steady state" experimental approach was adopted in order to simplify the analysis of the dynamic ATP process. The principal processing parameters were decoupled and studied independently using a specifically designed thermoplastic compaction fixture. Taguchi Design of Experiment (DOE) and Analysis of Variance (ANOVA) were used to determine the influence of each parameter. The results showed that both the compaction pressure and the fibre orientation difference were affecting the material deformations. The processing temperature, in the range under study, was shown to have a negligible impact on the steady state strain. Squeeze flow models were developed to describe the behaviour of the material during the compaction experiments. A model formulation with a slip boundary condition was implemented in order to capture the fibre orientation difference dependency. An inverse method, based on half-interval, was used with the compaction fixture's experimental results in order to determine the material viscosity, and to identify the friction factor. An empirical relation based on a sinusoidal function was proposed to describe this friction factor. The model with slip was then adapted to the ATP manufacturing problem, and predictions were made for in-situ material deformations. Finally, the model was used to construct strain prediction surfaces that could provide guidelines for improving the design of deposition paths, and reduce the amount of manufacturing defects.
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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 | 0.000 | 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".