Formation and occurrence of subsurface damage mechanism with the analysis of optimal machining strategy when milling different fibre matrix combination of unidirectional FRP’s
Bibliographic record
Abstract
To utilise LFT (Long fibre thermoplastic) materials for structural components, they are reinforced with UD-tapes to achieve high stiffness and strength. Machining of unidirectional fibre reinforced plastics can cause various types of damage such as delamination, burrs or subsurface damage due to the anisotropy of the material and the resulting cutting conditions. A reduction of surface quality or component rejection can be the result. In addition to the usual process parameters such as feed rate the fibre type and the matrix material have a decisive effect on the separation mechanisms. In this paper different materials are analysed to provide a deeper understanding of the subsurface damage formation during the milling cut. The focus is on thermoplastic composites T700/PA6 and T700/PEEK as well as GF/PP and flax/PP. The composites with epoxy resin are reinforced with different carbon fibre types such as high tenancy (AS4), high modular (HM) and ultra-high modular (UHM) fibres. A spatially defined fibre cutting angle is used to analyse the subsurface damage area which varies due to uncut chip thickness, fibre type and matrix material. The damage range and depth is strongly influenced by the fibre type, as well as the fracture morphology due to bending induced fractures. Even for the flax/PP a subsurface damage area can be observed, although their properties differ greatly. For T700/PA6 and T700/PEEK plastic deformation can be observed due to fibre-matrix fragments adhere back to the surface after cutting. The findings about the subsurface damage area are applied to the milling of an external radius. For UHM/epoxy there was no subsurface damage in milling an external radius. By using an optimised milling strategy the maximum damage depth can be reduced in T700/PA6 up to 26%, in HM/epoxy up to 52% and in AS4/epoxy up to 73%.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".