The Effect of Welding Dimensional Variability on the Fatigue Life of Gas Metal Arc Welded Joints
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">Gas Metal Arc Welding (GMAW) is widely employed for joining relatively thick sheet steels in automotive body-in-white structures and frames. The GMAW process is very flexible for various joint geometries and has relatively high welding speed. However, fatigue failures can occur at welded joints subjected to various types of loads. Thus, vehicle design engineers need to understand the fatigue characteristics of welded joints produced by GMAW. Currently, automotive structures employ various advanced high strength steels (AHSS) such as dual-phase (DP) and transformation-induced plasticity (TRIP) steels to produce lighter vehicle structures with improved safety performance and fuel economy, and reduced harmful emissions. Relatively thick gages of AHSS are commonly joined to conventional high strength steels and/or mild steels using GMAW in current body-in-white structures and frames. Therefore, the Sheet Steel Fatigue Committee of Auto/Steel Partnership (A/S-P) has completed fatigue tests of GMAW joints for DP590 GA, SAE 1008, HSLA HR 420, DP600 HR, Boron, DQSK, TRIP780 GI, and DP780 GI sheet steels. Dissimilar metal welded joints were tested for DP590GA and SAE1008, DP600 and SAE1008, TRIP780 and SAE1008, DP780 and SAE1008, and Boron and HSLA. The specimen configurations included single lap-shear, double lap-shear, butt weld, start-stop, and perch mount. The fatigue test results obtained from various sheet steels and specimen types showed that the strength of the base metal was not an important parameter to determine the GMAW fatigue life. For the same specimen type and sheet thickness, the test results for the different base materials collapsed nicely into a well defined curve when plotted in log-log scales. However, some specimen types showed greater scatter at long fatigue lives than others. This study investigated the sources of the scatter in terms of the dimensional variability of the weld geometry, such as weld toe radius, weld gap, horizontal weld leg length, and vertical weld leg length.</div></div>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".