Comparison between Finite Elements simulation of residual stress and Computer Vision measurements in a welding TIG process
More details
Hide details
University of Perugia
University Guglielmo Marconi (Rome)
Submission date: 2020-11-19
Final revision date: 2021-03-05
Acceptance date: 2021-04-12
Online publication date: 2021-04-13
Publication date: 2021-04-13
Corresponding author
Luca Petrucci   

University of Perugia
Diagnostyka 2021;22(2):29-37
In this work, residual stresses arising after an industrial TIG welding process on an aerospace grade part are investigated. The customer demand for high product resistance and high dimensional accuracy calls for the control of the welding process and the minimisation of the residual stresses. Dimensional check of manufactured parts was traditionally performed in a quality room by means of coordinate measuring machines (CMM). For parts larger than 1 meter, this operation shows several issues, as the handling and the need for large and expensive measuring devices. These needs can be fulfilled by an innovative method that, through continuous dimensional check, allows to optimise the welding process parameters. This method is built on a post-process measurement of part shrinkage based on a Computer Vision technique, the outcome being a 3D reconstruction of the actual part. Moreover, the whole procedure is low-cost and time saving, as it can be performed with a conventional camera mounted on a tripod. A Finite Element Model (FEM) of the TIG process on the selected sample was developed. The result of the numerical model was compared with the Computer Vision-based post-process measurement. The simulation scenario predicted by Finite Element Analysis agrees with measurements.
The authors want to thank NCM Spa enterprise (Via A. Vici, 34 – Z.I. La Paciana, 06034 Foligno PG, Italy) for the support given in terms of professional knowledge and for having made available its areas and instrumentation.
This paper is the result of the project implementation: “NCM-PRO: Innovative integrated control in Numerical Control Manufacturing PROcesses”, funded by the POR-FESR 2014-2020 – Regional Operative Programme – European Fund for Regional Development of Umbria Region (Italy).
Chang PH, Teng TL. Numerical and experimental investigations on the residual stresses of the butt-welded joints. Computational Materials Science. 2004;29(4):511–522. ;.
Haelsig A, Kusch M, Mayr P. New findings on the efficiency of gas shielded ARC welding. 2009.
Deng D. FEM prediction of welding residual stress and distortion in carbon steel considering phase transformation effects. Materials and Design. 2009;30(2):359–366.
Mackerle J, Pietraszkiewicz W, Konopiska V. Modelling and simulation in materials science and engineering related content finite element analysis and simulation of welding: a bibliography (1976 - 1996) Finite element analysis and simulation of welding.
Jain R, Pal SK, Singh SB. Finite element simulation of pin shape influence on material flow, forces in friction stir welding. International Journal of Advanced Manufacturing Technology. 2018;94(5–8):781–1797.
Ogawa K, Deng D, Kiyoshima S, Yanagida N, Saito K. Investigations on welding residual stresses in penetration nozzles by means of 3D thermal elastic plastic FEM and experiment. Computational Materials Science. 2009 45(4):1031–1042.
Hwang JD, Lin HJ, Hwang JD, Hu CT. Numerical Simulation of Metal Flow and Heat Transfer during Twin Roll Strip Casting. ISIJ International. 1995; 35(2):170–177.
Dean D, Hidekazu M. Prediction of welding residual stress in multi-pass butt-welded modified 9Cr – 1Mo steel pipe considering phase transformation effects. Computational Materials Science. 2006;37: 209–219.
Nodeh IR, Serajzadeh S, Kokabi AH. Simulation of welding residual stresses in resistance spot welding, FE modeling and X-ray verification. Journal of Materials Processing Technology. 2007;5:60–69.
Agin GJ. Computer Vision Systems for Industrial Inspection and Assembly. Computer. 1980;13(5):11-20.
Bacioiu D, Melton G, Papaelias M, Shaw R. Automated defect classification of SS304 TIG welding process using visible spectrum camera and machine learning. NDT & E International. 2019;107
Zhu H, Ge W, Liu Z. Deep Learning-based classification of weld surface defects. Applied Sciences. 2019;9(16):3312.;.
Sizyakin R, Voronin V, Gapon N, Zelensky A. Pižurica A. Automatic detection of welding defects using the convolutional neural network. Proc. SPIE 11061, Automated Visual Inspection and Machine Vision III, 2019;110610E.
Sang Y, Zhao J, Duan F. Ji X. In-plane deformation monitoring of a cylindriscal specimen using a simple approximate method combined with two-dimensional digital image correlation. Rev. Sci. Instrum. 2019;90: 1115107.;.
Ali M, Mailah M, Kazi S. Tang H. Defects detection of cylindrical object's surface using vision system. Recent Researches in Computational Intelligence and Information Security, 2011:222-227.
Xiao G, Li Y, Xia Q, Cheng, Chen W. Research on the on-line dimensional accuracy measurement method of conical spun workpieces based on machine vision technology. Measurement. 2019:148.
Ayub MA, Mohamed AB. Esa AH. In-line inspection of roundness using machine vision. Procedia Technology. 2014;105:807-816.
Haynes International. 2020. Website visited 11-19-2020.
Romero J, Saha B, Toledo G. Beltran-Bqz D. Welding Sequence Optimization Using Artificial Intelligence Techniques, an Overview. SSRG International Journal of Computer Science and Engineering (SSRG-IJCSE). 2016;3:91-95.
Yin S, Ding SX, Xie X, Lu H. A Review on Basic Data-Driven Approaches for Industrial Process Monitoring. IEEE Transactions on Industrial Electronics. 2014;61(11):6418-6428.
Dutta P, Pratihar DK. Modeling of TIG welding process using conventional regression analysis and neural network-based approaches. Journal of Materials Processing Technology. 2007;184(1-3): 56–68.
Gao X, You D, Katayama S. Seam tracking monitoring based on adaptive Kalman filter embedded elman neural network during high-power fiber laser welding. IEEE Transactions on Industrial Electronics. 2012;59(11):4315–4325.
Pal S, Pal SK, Samantaray AK. Artificial neural network modeling of weld joint strength prediction of a pulsed metal inert gas welding process using arc signals. Journal of Materials Processing Technology. 2008;202(1–3):464–474.
Journals System - logo
Scroll to top