Process and performance evaluation of ultrasonic, induction and resistance welding of advanced thermoplastic composites
Bibliographic record
Abstract
The possibility of assembling through welding is one of the major features of thermoplastic composites and it positively contributes to their cost-effectiveness in manufacturing. This article presents a comparative evaluation of ultrasonic, induction and resistance welding of individual carbon fibre-reinforced polyphenylene sulphide (PPS) thermoplastic composite samples that comprises an analysis of the static and dynamic mechanical behaviour of the joints as well as of the main process variables. The induction welding process as used in this research benefitted from the conductive nature of the reinforcing fibres. Hence, no susceptor was placed at the welding interface. Resistance welding used a fine-woven stainless-steel mesh as the heating element and low welding pressures and times were applied to prevent current leakage. Triangular energy directors moulded on a separate tape of PPS resin were used to concentrate ultrasonic heat at the welding interface. The static single-lap shear strength of the joints was found similar for induction and ultrasonic welding. A 15% drop in the static mechanical properties of the resistance welded joints was attributed to incomplete welded overlaps following current leakage prevention. However, the fatigue performance relative to the static one was similar for the three sorts of joints. A comparative analysis of process variables such as welding time, required power and energy was also carried out.
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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 | 0.000 | 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".