B.Senthilkumar1 and Dr. T.Kannan2 Prediction of bead geometry responses in super duplex stainless steel cladding deposited by FCAW process using neural network B.Senthilkumar1 and Dr. T.Kannan2 1Assistant Professor, Department of Mechanical Engineering, Kumaraguru College of Technology, Coimbatore – 641 049. 2 Principal, SVS College of Engineering, Arasampalayam, Coimbatore – 641 102.

Problem definition Corrosion – durability of functional components – process industries Stainless steels – alloy composition – microstructure – mechanical properties – heat sensitivity Cladding – surfacing – dilution – thermal cycles – properties Process selection – GMAW, FCAW, PAW and SAW etc.,

Material selection – Electrode and Base metal Elements (% wt.) C Mn Si S P Cr Ni Mo N Fe Base metal (IS: 2062. GR. B, ASTM A36) 0.196 1.12 0.293 0.011 0.0044 0.128 0.336 0.275 -- Balance Electrode (AWS A5.22 E2594 T0-4) 0.027 0.64 0.47 0.005 0.018 25.8 8.62 4.36 0.28

Process parameters and responses Welding voltage (X1) Wire feed rate (X2) Welding speed (X3) Nozzle to plate distance (X4) Welding gun angle (X5)

Experimental setup

Process parameter levels and coding Working range - trial runs – maximum and minimum limits of process parameters Coding

Process parameter levels and coding Parameters Units Symbol Factor levels -2 -1 +1 +2 Welding voltage V X1 22 24 26 28 30 Wire feed rate m/min X2 5.08 5.715 6.35 6.985 7.62 Welding speed X3 0.12 0.14 0.16 0.18 0.20 Nozzle to plate distance m X4 0.015 0.017 0.019 0.021 0.023 Welding gun angle  X5 70 80 90 100 110

Experimental design Central composite rotatable design Five factors each with five levels 16 factorial combinations, 10 star points and 6 center points Single bead on plate welds Experimental parameter combinations were selected at random Parameters were intentionally disturbed before conducting each experiment Specimen extraction

Bead geometry

Responses Penetration area (PA) Percentage dilution (D) Reinforcement height (H) Bead width (W) Reinforcement form factor (RFF) = Reinforcement height / bead width Penetration form factor (PFF) = Penetration depth / bead width Contact angle (CA)

Neural network model development Resembles the functioning neurons – inspired from biological systems (Nature) Capable of detecting complex inter-relationship between the process parameters and responses Normalized data (0 to 1) enhances the learning capability of artificial neural network models

Neural network configuration

ANN Training

Results of the neural network training Data Samples MSE R2 value Training 22 2.062E-03 0.985 Validation 5 4.855E-02 0.816 Testing 7.935E-02 0.735

Simulink model

Conclusions The five welding variables each with five levels were used to formulate the experimental combinations for collecting the necessary data to construct the neural network models. Three layered feed forward back propagation neural network The input data for these models can be directly interfaced with data acquisition system to enable the online monitoring of the weld cladding process. These models can also be used for welding with the objective maximising penetration and percentage dilution. This model can be used in the welding based additive manufacturing.

Acknowledgement M/S Metrode welding consumables for providing flux cored super duplex stainless steel welding wire for this work. Management of Kumaraguru College of Technology and SVS College of Engineering

Discussion

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Contd., [5] T. Kannan and N. Murugan, “Effect of flux cored arc welding process parameters on duplex stainless steel clad quality”, J. Mater. Process. Technol. 176 (2006) 230– 239. [6] I.S. Kim, J.S. Son, C.E. Park, I.J. Kim and H.H. Kim, “An investigation into an intelligent system for predicting bead geometry in GMA welding process”, J. Mater. Process. Technol. 159 (2005) 113–118. [7] J.H.F. Gomes, S.C. Costa, P. Paiva and P.P. Balestrassi, “Mathematical Modeling of Weld Bead Geometry, Quality, and Productivity for Stainless Steel Claddings Deposited by FCAW”, J. Mater. Eng. Perform. 21 (2012) 1862–1872. [8] B.Y.S. Subramaniam and J.W. Lyons, “Experimental Approach to Selection of Pulsing Parameters in Pulsed GMAW”, Weld. J. (1999) 166–172.

Contd., [9] K. Andersen, G.E. Cook, G. Karsai and K. Ramaswamy, “Artificial neural networks applied to arc welding process modeling and control”, IEEE Trans. Ind. Appl. 26 (1990) 824–830. [10] T. Kannan and J. Yoganandh, “Effect of process parameters on clad bead geometry and its shape relationships of stainless steel claddings deposited by GMAW”, Int. J. Adv. Manuf. Technol. 47 (2009) 1083–1095. [11] K. Marimuthu and N. Murugan, “Prediction and Optimisation of Weld Bead Geometry of Plasma Transferred Arc Hardfaced Valve Seat Rings”, Surf. Eng. 19 (2003) 143–149. [12] S.G. Rabinovich, “Measurement errors and uncertainties: theory and practice”, Springer Science & Business Media, 2006.

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