Weld Quality Prediction of Mild Steel Pipe Joint during Shielded Metal Arc Welding through fuzzy Logic

The present paper describes fuzzy logic simulation of shielded metal arc welding (SMAW) process to predict the Quality of weld joint. It describe the quality of weld joint & effect of welding current, voltage and welding speed on tensile strength of shielded metal arc pipe welded mild steel joints. An ERW Mild steel pipe I S 1239 of 6 mm thickness and 150 mm diameter were used as the base material for preparing root & final pass butt welded joints. Speed of weld was provided by welder in 6G position. Tensile strength of the joint fabricated by A 5.1 E-6013 electrodes as filler metals was evaluated and the results were reported. Report is used in fuzzy logic simulation to predict the tensile strength of weld for the given welding parameters. A series of 19 experiments were carried out for collecting the data. Out of which 9 experimental data were then used for building a fuzzy logic model to predict the effects of control factors on the responses. The model was also tested from 10 set of data to establish it's adequacy. The Fuzzy logic predictions shows to be an excellent agreement with experimental results and the prediction error find are minimal. KeywordsShielded Metal Arc Welding, Tensile Strength, Fuzzy logic All plant now a day’s uses Pipelines for transport of water, and petroleum products these pipelines play an important role for sustaining vital functions such as power generation, heating supply and transportation If the pipeline carrying these chemicals burst/leaks it results huge loss of money & time. Failure of these pipelines is due lack of strength, lack of penetration, gas porosities, and cracks etc. Pipeline welding under field conditions has always faced severe demands with regard to quality and cost. Manual metal arc welding also called shielded metal arc welding (SMAW), is the most extensively used manual welding process, which is done with stick (coated) electrodes. Though in USA, its use is decreasing in comparison to the other arc welding processes. This process is highly versatile and can be used extensively for both simple as well as sophisticated jobs. Further, the equipment is less expensive than those used in most of the other arc welding processes. Welds by this process can be made in any position [1]. Quality is a very important factor in the field of welding. The quality of a weld mainly depends on mechanical properties of the weld metal which in turn depends on metallurgical characteristics and chemical composition of the weld. The mechanical feature of weld depends directly on welding process parameters [2].SMAW input process parameters like welding current, welding speed; open circuit voltage and external magnetic field are highly influencing the quality of weld joints. [3].Selection of process parameters like welding current, welding speed and voltage has great influence on the quality of a welded connection. The selection of the process variables and control of weld bead shape has become important because mechanical strength of weld is depend on it. The high weld quality can be achieved by meeting quality requirements such as bead geometry which is highly influenced by various process parameters involved in the process. Poor weld bead dimensions will contribute to failure of the welded structure [4].Good weld design and selection of appropriate and optimum combinations of welding parameters are imperative for producing high quality weld joints with the desired tensile strength. Understanding the correlation between the process parameters and mechanical properties is a precondition for obtaining high productivity and reliability of the welded joints[5] Also SMAW is slower than other methods of welding and is more depend on the operator skill for high weld quality[6]. We are using ANN in this paper as it can easily represent non-linear relationships between input data and output data. Even if the data is incomplete, neural networks are able to correctly classify the different data classes captured from the network or other sources [8]. ANN modeling has been chosen by its capability to solve complex and difficult problems. Kim et al. used multiple regression analysis and back propagation neural network in modeling bead height in metal arc welding [9]. The Prediction of bead geometry in pulsed GMA welding is done by using back propagation neural network, with the use of ANN. The back propagation neural network model is developed 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 [10]. The Prediction of gas metal arc welding parameters based on artificial neural is done. A novel technique based on artificial neural networks (ANNs) for prediction of gas metal arc welding parameters. Input parameters of the model consist of gas mixtures, whereas, outputs of the ANN model include mechanical properties such as tensile strength, impact strength, elongation and weld metal hardness, respectively. ANN controller was trained with the extended delta bar delta learning algorithm. The measured and calculated data were simulated by a computer program. The results showed that the outcomes of the calculation were in