Prediction of bead geometry in pulsed GMA welding using back propagation neural network

Abstract

This paper presents the development of a back propagation neural network model for the prediction of weld bead geometry in pulsed gas metal arc welding process. The model is based on experimental data. The thickness of the plate, pulse frequency, wire feed rate, wire feed rate/travel speed ratio, and peak current have been considered as the input parameters and the bead penetration depth and the convexity index of the bead as output parameters to develop the model. The developed model is then compared with experimental results and it is found that the results obtained from neural network model are accurate in predicting the weld bead geometry.

Introduction

The GMAW-P process has recently gained wide attention in the welding industry, owing to its comparatively low heat input and precise control over the thermal cycle. This is because, with the pulsed current gas metal arc welding process, spray transfer or more precisely controlled droplet transfer is obtained at a low average current. This provides a smaller and well-controlled weld pool, which allows cladding of thin materials in all positions.

The superiority of the GMAW-P process is mainly related to the nature of the metal deposition, which is governed by the pulse parameters, namely peak current, pulse frequency, wire feed rate, wire feed rate/travel speed ratio, etc. Bead geometry in the arc welding process is an important factor in determining the mechanical characteristics of the weld (Srinivasa Rao, 2004, Srinivasa Rao et al., 2005, Rajasekaran, 1999, Senthil Kumar and Parmer, 1986). Bead geometry variables, such as bead width, bead height, and penetration depth, are greatly influenced by welding process parameters such as wire feed rate, welding current, welding speed, plate thickness etc. Weld shape and size are represented by bead width (W), bead height (H) and depth of penetration (P) as shown in Fig. 1. Convexity index (CI) is defined as the ratio of bead reinforcement height to the bead width. Selection of the most suitable combination of pulse parameters is very difficult owing to the complex interdependence of the above parameters on the pulsed current (Srinivasa Rao, 2004, Srinivasa Rao et al., 2005).

However, costly and time-consuming experiments are required in order to determine the optimum welding process parameters due to the complex and nonlinear nature of the welding process. Therefore, a more efficient method is needed to determine the optimum welding process parameters.

Technique of neural networks offers potential as an alternative to standard computer techniques in control technology and has attracted a widening interest in their development and application (Rajasekaran, 1999). The advantages of the neural networks is that the network can be updated continuously with new data to optimize its performance at any instance, the networks ability to handle a large number of input variables rapidly, and the networks ability to filter noisy data and interpolate incomplete data.

The back propagation network (BPN) system is one of the family of artificial neural network techniques used to determine welding parameters for various arc welding processes The network is a multi layer network that contains at least one hidden layer in addition to input and output layers. Number of input layers and number of neurons in each hidden layer is to be fixed, based on the application, the complexity of the problem, and the number of inputs and outputs. Use of nonlinear log-sigmoid or tan-sigmoid transfer function enables the network to simulate non-linearity in practical systems. Due to its numerous advantages, back propagation network is chosen for present work (Kim et al., 2001, Kim et al., 2002, Kim et al., 2005, Chan et al., 1999, Lee and Um, 2000, Krose and Van der Smagt, 1996).