Distortions in Large Stiffened Ship Panels Caused by Welding: An Experimental Study
Bibliographic record
Abstract
Severe distortions are observed in fabrication of large stiffened ship panels using lower thickness plates ranging from 3 mm to 10 mm. It requires expensive postwelding fairing operations. It is naturally preferable to control the distortion during fabrication than to apply fairing operations. Welding sequence plays an important role in controlling distortion. The aspect of weld sequencing has been studied only on a small and extremely simple form of test models. Possible welding sequences were worked out, based on principles of heat balancing or heat accumulation and subsequent behavior of such panels. Based on this, large stiffened panels were fabricated using low carbon steel of shipbuilding quality considering four different welding sequences. Initial and final distortions of these panels were measured using high precision coordinate measuring machine. In this investigation, experiments and measurements played a very important role, as numerical simulation of such large panels would have been prohibitively expensive or not feasible. This study established the pattern of welding sequence that needs to be followed for minimizing welding distortion in the fabrication of large stiffened panels. Gainful conclusions were drawn to give directions to welding designers to work out the appropriate sequence of welding to minimize the weld-induced panel distortions. 1. Introduction Ship structural components are built by assembling various stiffened steel plates. Large steel plates are stiffened by welding stiffeners using fusion welding process which is a common welding method used in the shipbuilding industry. Severe thermal gradients in the welded components occur because of intense localized heating in the heat-affected zone (HAZ), followed by uneven cooling. As a result, residual stresses and distortions occur in the welded components. These distortions are introduce in the block assembly process in shipbuilding. These unwanted distortions result in additional production cost because necessary rectification process and extra manpower are required. These also cause delays in shipbuilding.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".