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Residual Stress Prediction of Welded Joints Using Gradient Boosting Regression

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Intelligent Technologies and Applications (INTAP 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1616))

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Abstract

Welding residual stress (WRS) estimation is highly nonlinear process due to its association with high thermal gradients generated during welding. Accurate and fast estimation of welding induced residual stresses in critical weld geometries of offshore structures, piping components etc., becomes important from structural integrity perspective. Fitness for services (FFS) codes like API 579, BS7910 recommend residual stress profiles are mainly based on three approaches, out of which nonlinear finite element modelling (FEM) results coupled with residual stress experimental measurement, have been found to be most conservative and realistic. The residual stress estimation from thermo mechanical FEM models is computationally expensive as it involves a large degree of interactions between thermal, mechanical, metallurgical and phase transformations etc. The destructive and non-destructive measurement techniques also carry a large amount of uncertainly due to lack of standardization and interpretation variability of measurement results. To mitigate the aforementioned challenges, response surface models (RSMs) have been proposed in this study, for the estimation of WRS at a significant confidence. This paper examines the applicability of 12 different Response Surface Models (RSMs) for estimating WRS. The training and testing data is generated using FEM, Abaqus - 2D weld interface (AWI) plug-in. To compare the accuracy of the RSMs, three metrics, namely, Root Mean Square Error (RMSE), Maximum Absolute Error (AAE), and Explained Variance Score (EVS) are used. An illustrative case study to demonstrate the applicability of the response surface model to predict WRS is also presented.

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References

  1. Bhardwaj, S., Ratnayake, R.M.C.: Challenges due to welds fabricated at a close proximity on offshore structures, pipelines, and piping: state of the art. In: ASME 2020 39th International Conference on Ocean, Offshore and Arctic Engineering (2020)

    Google Scholar 

  2. Bhardwaj, S., et al.: Machine learning approach for estimating residual stresses in girth welds of topside piping. In: ASME 2020 39th International Conference on Ocean, Offshore and Arctic Engineering (2020)

    Google Scholar 

  3. Bhatti, A.A., Barsoum, Z., Khurshid, M.: Development of a finite element simulation framework for the prediction of residual stresses in large welded structures. Comput. Struct. 133, 1–11 (2014)

    Article  Google Scholar 

  4. Dong, P., et al.: On residual stress prescriptions for fitness for service assessment of pipe girth welds. Int. J. Press. Vessels Pip. 123–124, 19–29 (2014)

    Article  Google Scholar 

  5. Dong, P.: Residual stresses and distortions in welded structures: a perspective for engineering applications. Sci. Technol. Weld. Joining 10(4), 389–398 (2005)

    Article  Google Scholar 

  6. Ficquet, X., et al.: Measurement and prediction of residual stress in a bead-on-plate weld benchmark specimen. Int. J. Press. Vessels Pip. 86(1), 20–30 (2009)

    Article  Google Scholar 

  7. Francis, J.A., Bhadeshia, H.K.D.H., Withers, P.J.: Welding residual stresses in ferritic power plant steels. Mater. Sci. Technol. 23(9), 1009–1020 (2007)

    Article  Google Scholar 

  8. Institute, A.P., API RP 579-1/ASME FFS-1. Houston, TX: American Petroleum Institute; USA, August 2007 (2007)

    Google Scholar 

  9. Brownlee, J.: A gentle introduction to the gradient boosting algorithm for machine learning (2020). http://machinelearningmastery.com/gentle-introduction-gradient-boosting-algorithm-machine-learning/. Accessed 30 Aug 2021

  10. Keprate, A., Ratnayake, R.M.: Using gradient boosting regressor to predict stress intensity factor of a crack propagating in small bore piping (2017)

    Google Scholar 

  11. Keprate, A., Ratnayake, R.M.C., Sankararaman, S.: Comparing different metamodelling approaches to predict stress intensity factor of a semi-elliptic crack. In: ASME 2017 36th International Conference on Ocean, Offshore and Arctic Engineering (2017)

    Google Scholar 

  12. Mirzaee-Sisan, A., Wu, G.: Residual stress in pipeline girth welds- a review of recent data and modelling. Int. J. Press. Vessels Pip. 169, 142–152 (2019)

    Article  Google Scholar 

  13. Smith, M.C., et al.: A review of the NeT Task Group 1 residual stress measurement and analysis round robin on a single weld bead-on-plate specimen. Int. J. Press. Vessels Pip. 120–121, 93–140 (2014)

    Article  Google Scholar 

  14. Song, S., Pei, X., Dong, P.: An analytical interpretation of welding linear heat input for 2D residual stress models. In: ASME 2015 Pressure Vessels and Piping Conference (2015)

    Google Scholar 

  15. Standard, B.: BS 7910 Guide to methods for assessing the acceptability of flaws in metallic structures, UK (2019)

    Google Scholar 

  16. Ueda, Y., Murakawa, H., Ma, N.: Introduction to measurement and prediction of residual stresses with the help of inherent strains. In: Ueda, Y., Murakawa, H., Ma, N. (eds.) Welding Deformation and Residual Stress Prevention, pp. 35–53. Butterworth-Heinemann, Boston (2012)

    Google Scholar 

  17. Withers, P.J., Bhadeshia, H.K.D.H.: Residual stress. Part 2 – Nature and origins. Mater. Sci. Technol. 17(4), 366–375 (2001)

    Article  Google Scholar 

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Correspondence to Arvind Keprate .

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Bhardwaj, S., Keprate, A., Ratnayake, R.M.C. (2022). Residual Stress Prediction of Welded Joints Using Gradient Boosting Regression. In: Sanfilippo, F., Granmo, OC., Yayilgan, S.Y., Bajwa, I.S. (eds) Intelligent Technologies and Applications. INTAP 2021. Communications in Computer and Information Science, vol 1616. Springer, Cham. https://doi.org/10.1007/978-3-031-10525-8_4

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  • DOI: https://doi.org/10.1007/978-3-031-10525-8_4

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  • Online ISBN: 978-3-031-10525-8

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