Study On The Simulation Of Multi-wire Submerged Arc Welding Heat Source Model And Appearance Of Weld

Because of the high deposition efficiency and production efficiency of multi-wirewelding, it becomes the most important welding process of the pipeline and pressure vesselwelding. However, the welding process parameters are always unavailable, and they areusually decided by experience. The most widely used heat source decision method is trial.Because of the experience limit and the time limit, it is easy to cause the artificial errer, and itis hard to ensure the accuracy of the heat source model. On the other hand, it will increase thedevelopment cost. It is emergent to develop a more reasonable heat source model and themethod to obtain its parameters according to the process parameters, which can reduce theartificial error. Inverse analysis is a from-effect-to-cause algorithm, which can determine themost accurate input parameters according to the effect parameters without the mapping ofthem. When the inverse analysis is introduced to the determination of the heat source modelparameters, it can increase the precision of the simulation, reduce the experiment and save thetrail cost. However, the reverse analysis is based on the result of the experiment, and it is notable to obtain the heat source parameters according to the unexperimented process parameters.The result is discrete. For the process parameters without experiment, the intelligentoptimization method which is based on the results of the inverse analysis can extent theparameters and make the results continuous. With the analyse of the inverse analysis and theintelligent optimization method, the heat source parameters according to the different weldingprocess parameters can be obtained directly. The method can reduce the cost of trail andincrease the precision of the simulation greatly. In this work, the error of the prediction modelwas controlled within5%.For the prediction of the heat source parameters by the reverse analysis and intelligentoptimization method, the welding experiment was applied. A new measuring method was designed to obtain the temperature-dependent thermal conductivity and the heat capacity ofthe base metal and the welding flux. The size of weld measurement was designed. Byadjusting the welding process parameters, the different sizes of weld of the correspondingprocess conditions were obtained. The size of weld is composed by the weld width, the weldwidth at2mm depth from the top surface, the penetration and the reinforcement. The weldfeed rate influenced by the welding process parameters was also measured and analyzed. Theresults showed that the weld feed rate was proportional to the welding current.Based on the summarization of the heat source models which were available nowadays,the heat source models were analyzed and improved. The parameter sensitivity of the doubleellipsoid heat source model was analyzed, and a regress function of the weld size influencedby the double ellipsoid heat source model parameters was obtained. A new surface-bodyhybrid heat source model was put forward. In the new hybrid heat source model, the surfaceheat source was based on Gaussian heat source model. Considering the character of thedouble ellipsoid model, the surface heat source model was rewritten. According to the requestof the multi-wire welding, the hybrid heat source model was rewritten to be a separated model,and it could be applied on the multi-wire welding of any distance of the wires. The deflectangle of the welding arc was also considered. It makes the heat source model can simulate thedifferent deflect angel of the welding wire.The inverse analysis was applied to research the relationship between the welding processparameters and the heat source model parameters. The parameter sensitivity of the hybrid heatsource model was analyzed. Based on the parameter sensitivity results, the hybrid heat sourcemodel was simplified. According to the character of the multi-wire submerged arc welding,the pattern search method was applied. The pattern search method was improved with theproblem of the magnitude of the different heat source parameters. The relationship betweenthe different welding process parameters and the heat source model parameters was obtained.The different groove, thickness of the base metal, welding speed, heat dissipation and basemetal shape were obtained. The sizes of weld were also analyzed.The inverse analysis can only obtain the heat source corresponding to the experimentedprocess parameters. In order to extend the results of the heat source model, the least squaremethod regression, the artificial neural network and the support vector machine was applied on the inverse analysis results. It showed that the least square method regression result of thesingle wire welding was accurate, and that of the tandem wire welding was in a high error.The artificial neural network results showed good agreement with the verification results. Thesupport vector machine results had a high error with the verification results since it had manyunknown parameters. The regression results were verified by the triple wire welding. The heatsource parameters predicted by the regression model was applied on the triple wire weldingmodel, and the size of weld was simulated. It showed that the regression result could predictthe heat source model parameters very well.In order to analyze the influence of the flux, the appearance of the weld and the flow in theweld pool, a hydrodynamic model was designed. The model was controlled by the basefunction of the hydrodynamics. The model considered the gravity model, the electromagneticforce model, the heat transfer model, the solidification model and the surface tension model.The appearance of the weld and the flow in the weld pool of triple wire welding weresimulated by the hydrodynamic model. The simulation results showed that the weld pool sizeof triple wire welding was in little difference with the experiment result, and the error was2.76%.