Version 1
: Received: 20 August 2024 / Approved: 20 August 2024 / Online: 20 August 2024 (14:25:11 CEST)
How to cite:
Kim, D.-Y.; Yu, J. Statistical Analysis-Based Prediction Model for Fatigue Characteristics in Lap Joints considering Weld Geometry, including Gaps. Preprints2024, 2024081463. https://doi.org/10.20944/preprints202408.1463.v1
Kim, D.-Y.; Yu, J. Statistical Analysis-Based Prediction Model for Fatigue Characteristics in Lap Joints considering Weld Geometry, including Gaps. Preprints 2024, 2024081463. https://doi.org/10.20944/preprints202408.1463.v1
Kim, D.-Y.; Yu, J. Statistical Analysis-Based Prediction Model for Fatigue Characteristics in Lap Joints considering Weld Geometry, including Gaps. Preprints2024, 2024081463. https://doi.org/10.20944/preprints202408.1463.v1
APA Style
Kim, D. Y., & Yu, J. (2024). Statistical Analysis-Based Prediction Model for Fatigue Characteristics in Lap Joints considering Weld Geometry, including Gaps. Preprints. https://doi.org/10.20944/preprints202408.1463.v1
Chicago/Turabian Style
Kim, D. and Jiyoung Yu. 2024 "Statistical Analysis-Based Prediction Model for Fatigue Characteristics in Lap Joints considering Weld Geometry, including Gaps" Preprints. https://doi.org/10.20944/preprints202408.1463.v1
Abstract
This study proposed a regression model for predicting fatigue properties based on crucial weld geometry factors in lap-welded joints with gaps using statistical analysis. Welding conditions were varied to build various weld geometries in joints configured in a lap from with gaps of 0, 0.2, 0.5, and 1.0 mm, and 87 S-N curves for the lap-welded joints were derived. As input variables, 17 weld geometry factors (7 lengths, 7 angles, and 3 area factors) were selected. The slope of the S-N curve using Basquin model from the S-N curve and the safe fatigue strength were selected as output variables for prediction to develop the regression model. Multiple linear regression models, multiple non-linear regression models, and second-order polynomial regression models were proposed to predict fatigue properties. Backward elimination was applied to simplify the models and reduce overfitting. Among the three proposed regression models, the multiple non-linear regression model had a coefficient of determination greater than 0.86. In lap-welded joints with gaps, the weld geometry factors representing fatigue properties were identified through standardized regression coefficients, and four weld geometry factors related to stress concentration were proposed.
Engineering, Industrial and Manufacturing Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
