Characterization of resistance-welded thermoplastic composite double-lap joints
Bibliographic record
Abstract
An investigation of resistance welding of thermoplastic composite double-lap shear joints is presented.Double-lap shear specimens consisting of carbon fibre/polyetherether-ketone (PEEK/CF), carbon fibre/poly-etherketone-ketone (PEKK/CF), carbon fibre/poly-ether-imide (PEI/CF) and glassfibre/poly-ether-imide (PEI/GF) were resistance-welded using a stainless steel mesh heating element.The objective of this work was to study the mechanical performances of the double lap shear resistance-welded joints and to compare them with the single lap shear resistancewelded joints.The welded specimens were analyzed using static and dynamic lap shear tests and optical and scanning electron microscopy.Lap shear strengths of 53 MPa, 49 MPa, 45 MPa and an extrapolated value of 34 MPa were obtained for PEEK/CF, PEKK/CF, PEI/CF and PEI/GF double-lap joints, respectively.Infinite fatigue lives were obtained at 30% for PEEK/CF and PEKK/CF, 25% for PEI/CF and 20% for PEI/GF.Resistance-welded double-lap joints were found to have equivalent static and fatigue mechanical properties compared with single-lap joints, for all materials tested.I would like to acknowledge with gratitude Dr Ali Yousefpour for allowing me to work with the Aerospace Manufacturing and Technology Center, and for his priceless advice and assistance.I would like to thank him for taking the time to help me, despite
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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 | 0.000 |
| 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) | 0.001 | 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".