Comprehensive Evaluation of Double-Wire Narrow Gap GMAW Process and Dissimilar Joint for Chute Structure
Bibliographic record
Abstract
Abstract Chute structures are critical components for transporting mined coal in coal machinery, and their manufacturing involves welding dissimilar thick-plate materials. To achieve efficient and high-quality fabrication of chute structures, double-wire narrow gap gas metal arc welding (GMAW) was utilized to join 40-mm-thick wear-resistant steel NM450 and cast steel ZG30SiMn. A comprehensive evaluation was conducted on the welding process, microstructure, and mechanical properties of the welded joint. Additionally, temperature field simulations were performed to investigate the impact of multi-layer welding thermal cycles on microstructural evolution. The double-wire narrow gap GMAW process demonstrated high stability, yielding a defect-free welded joint. The weld metal (WM) microstructure comprised proeutectoid ferrite, polygonal ferrite, and acicular ferrite, which endowed the WM with better ductility and toughness. Due to the welding-induced multiple thermal cycles, a white band formed between filling layers. In the heat-affected zone (HAZ), the microstructure consisted of martensite and bainite, achieving higher strength and 86-110% toughness values of the base metals. This study successfully implemented double-wire narrow gap GMAW for chute fabrication, enabling high-quality welding of thick-plate dissimilar joint while enhancing welding efficiency without compromising performance. The findings provide actionable insights for optimizing chute manufacturing in heavy machinery industries.
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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".