Estimation of Weld Bead Geometry of Gas Metal Arc Welding Process Using Artificial Neural Netw
Keywords:
Artificial neural network, multiple regression, metal arc welding, weld bead geometryAbstract
A single weld bead geometry has significant effects on the mechanical properties of the bead, layer thickness, quality of surface bead and dimensional accuracy of the metallic parts of the welding. This research presents the application of an artificial intelligence approach using artificial neural network (ANN) and conventional multiple regression analysis for predicting the weld bead geometry in gas metal arc welding (GMAW) in which galvanized steel was the material used for the experiment. The developed models for the study were based on the experimental data. The welding voltage, welding current, welding speed and wire feed rate have been considered as the input parameters and the bead width (W) and height (H) are the output parameters in developing the models. In order to demonstrate which method performs better in terms of higher accuracy and prediction, three performance measures related to the coefficient of determination (R2), root mean square error (RMSE) and mean absolute percentage error (MAPE) were applied to the models and later compared. The results from the analysis show that the ANN models are more accurate compared to multiple regression approach in predicting the weld bead geometry due to its great capacity in approximating the non-linear process of the system.References
Tay K.M. and Butler C., 1997. Modelling and Optimizing of A MIG Welding Process – A Case Study Using Experimental Designs and Neural Networks, Quality and Reliability Engineering International, 13(2): 61-70.
Kanti K.M. and Rao S., 2008. Prediction of Bead Geometry in Pulsed GMA Welding Using Back Propagation Neural Network, Journal of Materials Processing Technology, 200(1-3): 300-305.
Xiong J., Zhang G., Hu J. and Wu L., 2014. Bead Geometry Prediction for Robotic GMAW-based Rapid Manufacturing through A Neural Network and A Second-order Regression Analysis, Journal Intelligent Manufacturing, 25(1): 157-163.
Nagesh D.S. and Datta G.L., 2002. Prediction of Weld Bead Geometry and Penetration in Shielded Metal Arc Welding Using Artificial Neural Network, Journal of Materials Processing Technology, 123(2): 303-312.
Liu Y.K. and Zhang Y.M., 2014. Model-based Predictive Control of Weld Penetration in Gas Tungsten Arc Welding, IEEE T Contr. Syst., 22(3): 955-966.
Zhao D., Wang Y., Wang X., Chen F. and Liang D., 2014. Process Analysis and Optimization for Failure Energy of Spot Welded Titanium Alloy, Journal of Materials Design, 60: 479-489.
Bahia I.S.H., 2013. A Data Mining Model by Using ANN for Predicting Real Estate Market: Comparative Study, International Journal of Intelligence Science, 3(4): 162-169.
Huang W. and Kovacevic R., 2011. A Neural Network and Multiple Regression Method for The Characterization of The Depth of Weld Penetration in Laser Welding Based on Acoustic Signatures, Journal of Intelligent Manufacturing, 22(2): 131-143.
Zaharuddin M.F.A., Kim D. and Rhee S., 2017. An ANFIS Based Approach for Predicting The Weld Strength of Resistance Spot Welding in Artificial Intelligence Development, Journal of Mechanical Science and Technology, 31(11): 5467-5476.
Zaharuddin M.F.A., 2018. Predictive Modelling in Welding Using Adaptive Neuro-fuzzy Inference System (ANFIS), PhD Dissertation, Hanyang University, Seoul, South Korea.
Lee J.I. and Rhee S., 2000. Prediction of Process Parameters for Gas Metal Arc Welding by Multiple Regression Analysis, Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture, 214(6):443-449.
Downloads
Published
How to Cite
Issue
Section
License
Copyright of articles that appear in Jurnal Mekanikal belongs exclusively to Penerbit Universiti Teknologi Malaysia (Penerbit UTM Press). This copyright covers the rights to reproduce the article, including reprints, electronic reproductions or any other reproductions of similar nature.