Joining of copolyamide thermoplastic-coated galvanized DP600 steel sheets using ultrasonic spot welding
Bibliographic record
Abstract
The application of bio-coated metals in automotive structures plays an important role for lightweighting and damping purposes. However, joining organic-coated metals presents significant challenges due to the huge difference in the physical properties between metals and polymers. In this study, copolyamide thermoplastic-coated and metal zinc-coated (galvanized) dual-phase (DP600) automotive steel sheets were joined using ultrasonic spot welding to study the influence of faying surface characteristics and welding energy on the mechanical performance. For the joining of organic-coated galvanized DP600 to organic-coated galvanized DP600, the softening of copolyamide thermoplastic at a higher welding energy of 1000 J facilitated intimate contact, void reduction, and interdiffusion of polymer chains. This resulted in a combination of cohesive and adhesive failure, achieving a high tensile lap shear load of ~1736 N. Conversely, insufficient softening at a lower welding energy of 500 J hindered intimate contact and the spreading of surface asperities, resulting in a lower joint strength. Additionally, for the galvanized DP600 to organic-coated galvanized DP600 joints, minimal bonding between the galvanized Zn layer on DP600 steel and the copolyamide thermoplastic led to adhesive failure under tensile lap shear loading. The findings reveal that effective joining of bio-coated steels can be achieved while preserving the integrity of metal-polymer interfaces.
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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".