Prediction of TIG Process Parameters Needed to Eliminate Post Weld Crack Formation and Stabilize Heat Input in Mild Steel Weldment using Artificial Neural Network (ANN) Volume 4 - Issue 1

Pondi P, Achebo J and Ozigagun A*

• Department of Production Engineering, Faculty of Engineering, University of Benin, Nigeria

Received: March 25, 2021;   Published: April 07, 2021

*Corresponding author: Ozigagun A, Department of Production Engineering, Faculty of Engineering, University of Benin, P.M.B 1154, Benin City, Edo State, Nigeria

DOI: 10.32474/MAMS.2021.04.000179

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Abstract

One of the limitations associated with response surface methodology (RSM) is that an understanding of the trend and pattern of the input variable is required for every model. It means therefore that the performance of RSM is dependent on the beauty of experimental design. Therefore, to predict the response variables beyond the scope of experimentation, predictive model such as artificial neural network (ANN) is required. The focus of the study is to apply artificial neural network for the prediction of tig process parameter such as Brinell hardness number (BHN), heat input (HI) and cooling rate (CR) which is required for eliminating post weld crack formation, and stabilizing heat input in mild steel weldment. The key input parameters considered in this work are welding current, welding voltage and welding speed while the response or measured parameters are Brinell hardness number (BHN), heat input (HI) and cooling rate (CR). Using the range and levels of the independent variables, statistical design of experiment (DOE) using central composite design (CCD) method was done. Hundred (100) pieces of mild steel coupons measuring 60 x 40 x10 were used for the experiments. The experiment was performed 20 times, using 5 specimens for each run. The plate samples were 60 mm long with a wall thickness of 10mm. The samples were cut longitudinally with a single-V joint preparation. The tungsten inert gas welding equipment was used to weld the plates after the edges have been bevelled and machined. The welding process uses a shielding gas to protect the weld specimen from atmospheric interaction. For the analysis of the measured variables artificial neural network was employed. To implement the neural network, a learning rate of 0.01, momentum coefficient of 0.1, target error of 0.01, analysis update interval of 500 and a maximum training cycle of 1000 epochs were used. The network generation process divides the input data into training data sets, validation and testing. For this study, 60% of the data was employed to perform the network training, 25% for validating the network while the remaining 15% was used to test the performance of the network.
From the result obtained, it was observed that the network performance was very good with performance errors of 3.4393e-05, 6.3500e-09 and 0.00034858 representing Brinell hardness number, heat input and cooling rate, respectively.

Keywords: Tig process parameters; Brinell hardness number (BHN); Heat input (HI); Cooling rate (CR); Artificial neural network (ANN)

Abstract| Introduction| Research Methodology| Results and Discussion| Conclusion References|