This work is an investigation into the applicability of the adaptive neuro-fuzzy inference system (ANFIS), a machine learning technique, to develop a model of the relation of residual stress distribution in a single weld bead-on-plate part to weld heat input and distance from the center of the weld line. Residual stress distributions required to train the ANFIS model were obtained through thermal elastic-plastic finite element analysis. Appropriate conditions for training the ANFIS model were investigated by evaluating the prediction error of the ANFIS model developed under various conditions. Afterward, residual stress distributions obtained by the developed ANFIS model trained under the appropriate conditions were compared with those obtained through thermal elastic-plastic finite element analysis. Discrepancies between the residual stresses obtained through the ANFIS model and thermal elastic-plastic finite element analysis were smaller than ±40 MPa in all regions. The results suggest that the ANFIS modeling had the ability to learn and generalize residual weld stress distributions in single weld bead-on-plate parts.

1. [1] A. Ohata, Y. Maeda, T. Mawari, S. Nishijima, and H. Nakamura, “Fatigue strength evaluation of welded joints containing high tensile residual stresses,” Int. J. of Fatigue, Vol.8, No.3, pp. 147-150, 1986.
2. [2] F. W. Brust and P. Dong, “Welding residual stresses and effects on fracture in pressure vessel and piping components: a millennium review and beyond,” J. of Pressure Vessel Technology, Vol.122, pp. 329-338, 2000.
3. [3] G. Webster and A. Ezeilo, “Residual stress distributions and their influence on fatigue lifetimes,” Int. J. of Fatigue, Vol.23, Supplement 1, pp. 375-383, 2001.
4. [4] J. A. Beavers, J. T. Johnson, and R. L. Sutherby, “Materials factors influencing the initiation of near-neutral pH SCC on underground pipelines,” Proc. of 3th Int. Pipeline Conf., Vol.2, pp. 979-988, 2000.
5. [5] G. V. Boven, W. Chen, and R. Rogge, “The role of residual stress in neutral pH stress corrosion cracking of pipeline steels. Part I: Pitting and cracking occurrence,” Acta Materialia, Vol.55, No.1, pp. 29-42, 2007.
6. [6] M. Fitzpatrick, A. Fry, P. Holdway, F. Kandil, J. Shackleton, and L. Suominen, “Determination of Residual Stresses by X-ray Diffraction – Issue 2,” Measurement Good Practice Guide, Vol.52, pp. 5-11, 2005.
7. [7] M. T. Hutchings and A. D. Krawitz, “Measurement of Residual and Applied Stress Using Neutron Diffraction,” Springer Science & Business Media, Vol.216, 2012.
8. [8] E. Takahashi, K. Iwai, and K. Satoh, “A Method of Measuring Triaxial Residual Stress in Heavy Section Butt Weldments,” Trans. of the Japan Welding Society, Vol.10, No.1, pp. 36-45, 1979.
9. [9] H. Kitano, S. Okano, and M. Mochizuki, “Evaluation of weld residual stress field by the deep hole drilling technique based on three-dimensional elasto-plasticity theory,” Quarterly J. of the Japan Welding Society, Vol.33, No.2, pp. 15s-19s, 2015.
10. [10] M. C. Smith, A. C. Smith, R. Wimpory, and C. Ohms, “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. of Pressure Vessels and Piping, Vol.120-121, pp. 93-140, 2014.
11. [11] P. Dong and J. K. Hong, “Analysis of IIW X/XV RSDP Phase I Round-Robin Residual Stress Results,” Welding in the World, Vol.46, No.5, pp. 24-31, 2002.
12. [12] M. C. Smith, B. Nadri, A. C. Smith, D. G. Carr, P. J. Bendeich, and L. E. Edwards, “Optimisation of Mixed Hardening Material Constitutive Models for Weld Residual Stress Simulation Using the NeT Task Group 1 Single Bead on Plate Benchmark Problem,” Proc. of ASME 2009 Pressure Vessels and Piping Conf., pp. 303-318, 2009.
13. [13] Y. Mikami, T. Nakamura, and M. Mochizuki, “Numerical investigation of the influence of heat source modeling on simulated residual stress distribution in weaving welds,” Welding in the World, Vol.60, No.1, pp. 41-49, 2016.
14. [14] D. Mandal, S. K. Pal, and P. Saha, “Modeling of electrical discharge machining process using back propagation neural network and multi-objective optimization using non-dominating sorting genetic algorithm-II,” J. of Materials Processing Technology, Vol.186, No.1-3, pp. 154-162, 2007.
15. [15] M. Kato and T. Yamamoto, “Development of Neural-Net-Based Decision Support System for Mattress Patterns Using Particle Swarm Optimization,” Int. J. Automation Technol., Vol.4, No.2, pp. 178-183, 2010.
16. [16] D. M. D’Addona, A. M. M. S. Ullah, and D. Matarazzo, “Tool-wear prediction and pattern-recognition using artificial neural network and DNA-based computing,” J. of Intelligent Manufacturing, Vol.28, No.6, pp. 1285-1301, 2017.
17. [17] A. Iqbal, S. M. Khan, and H. S. Mukhtar, “ANN assisted prediction of weld bead geometry in gas tungsten arc welding of HSLA steels,” Proc. of the World Congress on Engineering, Vol.1, pp. 6-8, 2011.
18. [18] J. E. R. Dhas and S. Kumanan, “ANFIS for prediction of weld bead width in a submerged arc welding process,” J. of Scientific and Industrial Research, Vol.66, pp. 335-338, 2007.
19. [19] N. Akkas, D. Karaye, S. S. Ozkan, A. Oğur, and B. Topal, “Modeling and Analysis of the Weld Bead Geometry in Submerged Arc Welding by Using Adaptive Neurofuzzy Inference System,” Mathematical Problems in Engineering, Vol.2013, Article ID 473495, 2013.
20. [20] J. S. R. Jang, “Fuzzy modeling using generalized neural networks and Kalman filter algorithm,” AAAI’91 Proc. of the 9th National Conf. on Artificial Intelligence, Vol.2, pp.762-767, 1991.
21. [21] J. S. R. Jang, “ANFIS: Adaptive-Network-Based Fuzzy Inference System,” IEEE Trans. on Systems, Man, and Cybernetics, Vol.23, No.3, pp. 665-685, 1993.