Finite Element Analysis and Experimental Characterisation of SMC Composite Car Hood Specimens under Complex Loadings
Bibliographic record
Abstract
Composite materials have recently been of particular interest to the automotive industry due to their high strength-to-weight ratio and versatility. Among the different composite materials used in mass-produced vehicles are sheet moulded compound (SMC) composites, which consist of random fibres, making them inexpensive candidates for non-structural applications in future vehicles. In this work, SMC composite materials were prepared with varying fibre orientations and volume fractions (25% and 45%) and subjected to a series of uniaxial tensile and flexural bending tests at a strain rate of 3 × 10−3 s−1. Tensile strength as well as failure strain increased with the increasing fibre volume fraction for the uniaxial tests. Flexural strength was found to also increase with increasing fibre percentage; however, failure displacement was found to decrease. The two material directions studied—longitudinal and transverse—showed superior strength and failure strain/displacement in the transverse direction. The experimental results were then used to create a finite element model to describe the deformation behaviour of SMC composites. Tensile results were first used to create and calibrate the model; then, the model was validated with flexural experimental results. The finite element model closely predicted both SMC volume fraction samples, predicting the failure force and displacement with less than 3.5% error in the lower volume fraction tests, and 6.6% error in the higher volume fraction tests.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".