Journal of Applied and Computational Mechanics

Modeling of Weld Bead Geometry Using Adaptive Neuro-Fuzzy Inference System (ANFIS) in Additive Manufacturing

Document Type : Research Paper

Authors

1 Department of Mechanical Engineering, Birjand Ubiversity of Technology, Birjand, Iran

2 Department of Mechanical Engineering, Birjand University of Technology, Birjand, Iran

3 Department of Computer engineering and Information Technology, Birjand University of Technology, Birjand, Ira

4 Department of Mechanical Engineering, Tarbiat Modares University, Tehran, Iran

Abstract

Additive Manufacturing describes the technologies that can produce a physical model out of a computer model with a layer-by-layer production process. Additive Manufacturing technologies, as compared to traditional manufacturing methods, have the high capability of manufacturing the complex components using minimum energy and minimum consumption. These technologies have brought about the possibility to make small pieces of raw materials in the shortest possible time without the need for a mold or tool. One of the technologies used to make pieces of the layer-by-layer process is the Gas Metal Arc Welding (GMAW). One of the basic steps in this method of making parts is the prediction of bead geometry in each pass of welding. In this study, taking into account the effective parameters on the geometry of weld bead, an empirical study has been done in this field. For this purpose, three parameters of voltage, welding speed and wire feeding rate are considered as effective parameters on the welding geometry of the process. Width and height of the bead are also determined by the parameters of the geometry of the weld according to the type and application of the research as output parameters are considered. In this paper, an adaptive neuro-fuzzy inference system (ANFIS) is used to create an adaptive model between input process data and parameters of weld bead geometry. The least squares mean error is used to evaluate the model. The predicted results by the model have a good correlation with the experimental data.

Keywords

Main Subjects

[1] Zhang, Y., Chen, Y., Li, P., Male, A. T., Weld deposition-based rapid prototyping: a preliminary study, Journal of Materials Processing Technology, 135 (2), 2003, 347-357.
[2] Suryakumar, S., Karunakaran ,K., Bernard, A., Chandrasekhar, U., Raghavender, N., Sharma, D., Weld bead modeling and process optimization in hybrid layered manufacturing, Computer-Aided Design, 43 (4), 2011, 331-344.
[3] Wanjara, P., Brochu, M., Jahazi, M., Electron beam freeforming of stainless steel using solid wire feed, Materials & design, 28 (8), 2007, 2278-2286.
[4] Karunakaran, K., Suryakumar, S., Pushpa, V., Akula, S., Retrofitment of a CNC machine for hybrid layered manufacturing, The International Journal of Advanced Manufacturing Technology, 45 (7), 2009, 690-703.
[5] Xiong, J., Zhang, G., Hu, J., Wu, L., Bead geometry prediction for robotic GMAW-based rapid manufacturing through a neural network and a second-order regression analysis, Journal of Intelligent Manufacturing, 25 (1), 2014,157-163.
[6] Merz, R., Prinz, F., Ramaswami, K., Terk, M., Weiss, L., Shape deposition manufacturing Engineering Design Research Center, Carnegie Mellon Univ, 1994, 1-8.
[7] Song, Y-A., Park, S., Experimental investigations into rapid prototyping of composites by novel hybrid deposition process, Journal of Materials Processing Technology, 171 (1), 1006, 35-40.
[8] Kovacevic, R., Rapid prototyping technique based on 3D welding, In NSF design & manufacturing grantees conference, 1999, 12-16.
[9] Cortes, C., Vapnik, V., Support-vector networks, Machine learning, 20 (3), 1995, 273-297.
[10] Weiss, L., Prinz, F., Adams, D., Siewiorek, D., Thermal spray shape deposition. Journal of Thermal Spray Technology, 1 (3), 1992, 231-237
[11] Kim, I-S., Son, J-S., Lee, S-H., Yarlagadda, P. K., Optimal design of neural networks for control in robotic arc welding, Robotics and computer-integrated manufacturing, 20 (1), 2004, 57-63.
[12] Ramos-Jaime, D., López-Juárez, I., Perez, P., Effect of process parameters on robotic GMAW bead area estimation, Procedia Technology, 7, 2013, 398-405.
[13] Lee, W-c., Wei, C-C., Chung, S-C., Development of a hybrid rapid prototyping system using low-cost fused deposition modeling and five-axis machining, Journal of Materials Processing Technology, 214 (11), 2014, 2366-2374.
[14] Dewan, M. W., Huggett, D. J., Liao, T. W., Wahab, M. A., Okeil, A. M., Prediction of tensile strength of friction stir weld joints with adaptive neuro-fuzzy inference system (ANFIS) and neural network, Materials & Design, 92, 2016, 288-299.
[15] Kovacevic, R., Zhang, Y. M., Neurofuzzy model-based weld fusion state estimation. IEEE Control Systems Magazine, 17(2),1997, 30-42.
[16] Zhang, Y. M., Kovacevic, R., Neurofuzzy model-based predictive control of weld fusion zone geometry. IEEE Transactions on Fuzzy Systems, 6(3), 1998, 389-401.
[17] Karuthapandi, S., Ramu, M., Thyla, P., Effects of the use of a flat wire electrode in gas metal arc welding and fuzzy logic model for the prediction of weldment shape profile, Journal of Mechanical Science and Technology, 31 (5), 2017, 2477-2486.
[18] Chandrasekhar, N., Vasudevan, M., Bhaduri, A., Jayakumar, T., Intelligent modeling for estimating weld bead width and depth of penetration from infra-red thermal images of the weld pool, Journal of Intelligent Manufacturing, 26 (1), 2015, 59-71.
[19] Vishnuvaradhan, S., Chandrasekhar, N., Vasudevan, M., Jayakumar, T., Intelligent modeling using adaptive neuro fuzzy inference system (ANFIS) for predicting weld bead shape parameters during A-TIG welding of reduced activation ferritic-martensitic (RAFM) steel, Transactions of the Indian Institute of Metals, 66 (1), 2013, 57-63.
[20] Liu, Y., Zhang, W., Zhang, Y., Dynamic neuro-fuzzy-based human intelligence modeling and control in GTAW. IEEE Transactions on Automation Science and Engineering, 12(1), 2013, 324-335.
[21] Liu, Y., Zhang, Y., Iterative local ANFIS-based human welder intelligence modeling and control in pipe GTAW process: A data-driven approach. IEEE/ASME Transactions on Mechatronics, 20(3), 2014, 1079-1088.
[22] Liu, Y. K., Zhang, W. J., Zhang, Y. M., Nonlinear modeling for 3D weld pool characteristic parameters in GTAW. Weld J, 94(7), 2015, 231-240.
[23] Ozcelik, S., Moore, K., Modeling, sensing and control of gas metal arc welding, Elsevier, 2003.
[24] Olabi, A., Alsinani, F., Alabdulkarim, A., Ruggiero, A., Tricarico, L., Benyounis, K., Optimizing the CO 2 laser welding process for dissimilar materials, Optics and Lasers in Engineering, 51 (7), 2013, 832-839.
[25] Murugan, V. V., Gunaraj, V., Effects of process parameters on angular distortion of gas metal arc welded structural steel plates, Welding journal, 11, 2005, 165-171.
[26] Li, K., Zhang, Y., Consumable double-electrode GMAW-Part 1: The process, WELDING JOURNAL-NEW YORK, 87 (1), 2008, 11,
[27] Wang, L-X., A course in fuzzy systems, Prentice-Hall press, USA, 1999.
[28] Jang, J-S. R., Sun, C-T., Mizutani, E., Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence, Proceedings of the IEEE, 86(3), 1998, 600-603.
[29] Ghomsheh, V. S,, Shoorehdeli, M. A., Teshnehlab, M., Training ANFIS structure with modified PSO algorithm. In 2007 Mediterranean Conference on Control & Automation , IEEE, 2007, 1-6.
[30] Zangeneh, A. Z., Mansouri, M., Teshnehlab, M., Sedigh, A. K., Training ANFIS system with DE algorithm. The Fourth International Workshop on Advanced Computational Intelligence. IEEE, 2011.
[31] Liu, P., Leng, W., Fang, W., Training anfis model with an improved quantum-behaved particle swarm optimization algorithm, Mathematical Problems in Engineering, 2013.
Journal of Applied and Computational Mechanics
Volume 6, Issue 1
January 2020
Pages 160-170