Predicting the combined effect of TiO 2 nano-particles and welding input parameters on the hardness of melted zone in submerged arc welding by fuzzy logic (original) (raw)
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The submerged-arc welding (SAW) process is an essential metal-joining process used in industry. SAW is applied for pressure vessels, heat exchangers, shipbuilding, line piping, and petroleum industries. The present work investigated the optimization of SAW parameters of 10-mm thick SA516 grade 70 steel to achieve the desired mechanical properties of the weld. Three SAW process parameters were investigated, each at three different levels: welding current (300, 350, and 400 A); arc voltage (32, 36, and 40 V); and welding speed (26, 28, and 30 cm/min). Sample plates measuring 500×500×10 mm were used to test the parameters. The weld quality and properties were evaluated using the following response parameters: Ultimate Tensile Strength (UTS), Ultimate Bending Force (UBF), HB Macrohardness (HB), and the Charpy Impact Test (CIT). Utility-based fuzzy logic was used to convert the complex multiple objectives into a single utility, Multi Performance Characteristic Index (MPCI). The MPCI response values were measured using the fuzzy inference system, Mamdani type. The results revealed that the optimal SAW parameters are welding current 400 A, arc voltage 40 V, and welding speed 30 cm/min. All process parameters had significant effects based on analysis of variance (ANOVA) (P <0.05 for all). Welding current had a major effect (37.03%) on the response parameters, followed by welding speed (32.9%) and arc voltage (30.07%).
Utility Fuzzy Taguchi Optimization Multiple Quality Factors of Submerged Arc Welding.pdf
Submerged Arc Welding (SAW) process is an essential metal joining processes in industry. The applications of SAW are as pressure vessels, heat exchanger, line piping, steel structural, nuclear industries and petroleum industries due to its high quality welds. The present work aims are to investigate the optimization of SAW parameters of selected material SA516 grade 70 steel of 10 mm thickness, in order to obtain desired mechanical properties of weld joint. Selection of process parameters has great influence on the weld quality, which is one of a problem faces manufacturer. SAW is multi input and multi output process, the multiple response optimization theory which is utility-based fuzzy logic coupled with Taguchi L9 orthogonal array was implemented. Three SAW process parameters were investigated such as welding current, arc voltage and welding speed with three varying levels. For welding current (300, 350 and 400 Amps.), three levels of arc voltage are (32, 36 and 40 V) and three level of welding speed (26, 28 and 30 cm/min) were used to weld sample plates measuring 500×500×10 mm. The weld quality and properties were evaluated using ultimate tensile strength, ultimate bending force, HB macrohardness and Charpy impact which are selected as performance characteristics, weld geometry measurements and non-destructive test such as visual inspection, penetrated liquid and X-ray radiography. Method of utility-based fuzzy logic converts the complex multiple objectives into a single utility which is Multi Performance Characteristics Index (MPCI). Fuzzy logic was used to reduce the degree of uncertainty in the output. MPCI values of responses measured using fuzzy inference system type Mamdani. MPCI is optimized using the S/N ratio analysis. The experimental results were analyzed by using Minitab®17 program and MATLAB software. The results show the optimal SAW parameters setting are welding current= 400 Amps, arc voltage= 40 V and welding speed= 30 cm/min. Significant contributions of parameters are estimated using analysis of variance (ANOVA). P values for welding current, arc voltage and welding speed are below interval significance (α)= 0.05 which meaning that all process parameters have significant effects. The contribution of welding current had major influence with percentage (37.03%), welding speed had (32.9%) effects and arc voltage had least (30.07%) contribution to influence on response parameters.
This study is aimed at obtaining a relationship between the values defining bead geometry and the welding parameters and also to select optimum welding parameters. For this reason, an experimental study has been realized. The welding parameters such as the arc current, arc voltage, and welding speed which have the most effect on bead geometry are considered, and the other parameters are held as constant. Four, three, and five different values for the arc current, the arc voltage, and welding speed are used, respectively. So, sixty samples made of St 52-3 material were prepared. The bead geometries of the samples are analyzed, and the thickness and penetration values of the weld bead are measured. Then, the relationship between the welding parameters is modeled by using artificial neural network (ANN) and neurofuzzy system approach. Each model is checked for its adequacy by using test data which are selected from experimental results. Then, the models developed are compared with regard to accuracy. Also, the appropriate welding parameters values can be easily selected when the models improve.
Journal of Advances in Science and Engineering, 2021
The focus of this study is to predict tungsten inert gas (TIG) welding process parameter such as heat input for stabilizing heat and removing post weld crack formation in mild steel weldment. The main input parameters examined are the welding current, voltage and speed whereas the measured (response) parameter is heat input. Statistical design of experiment was done by means of central composite design method using the range and levels of independent variables. The experiment was carried out 20 times (with 5 specimens per run) using 60 mm x 40 mm x 10 mm mild steel coupons. The plate samples were cut longitudinally with a Single-V joint preparation, with the edges beveled. The welding process utilizes 100% pure argon as a protecting gas to shield the weld specimen from external interaction. The interaction between the input and response variables was analyzed using a fuzzy logic system. The result showed that for a welding current, voltage and speed of 190 A, 21 V, and 2.0 mm/s resp...
Hardfacing operation is required in dual plate check valve manufacturing to create proper metal to metal seal. Metal Inert Gas (MIG) Welding process has been used for hardfacing. Welding input parameters plays a vital role for determining the weld quality. In hardfacing process weld bead geometry plays significant role. This paper shows the effect MIG welding process parameters on weld height. The input parameters taken under investigation are welding current, welding speed and gas flow rate. To predict Weld Height Fuzzy Logic based model is prepared using fuzzy expert rules, triangular membership function and centroid area method for defuzzyfication process using MATLAB fuzzy logic tool box. An attempt is made to relate statistical technique and computational technique to model the predicted response. The results obtained by Fuzzy, ANN and Regression are compared with experimental data to identify the best modelling technique among them.