Minimization of Residual Stresses in Submerged Arc Welding Process of Oil and Gas Steel Pipes by Committee Machine

Abstract

Submerged arc welding (SAW) is a well-known method to weld seam in manufacturing of large diameters steel pipes in oil and gas industry. The main subject of SAW design is selection of the optimum combination of input variables for achieving the desired output variables of weld. Input variables include voltage, amperage and speed of welding and output variables include residual stresses due to welding. On the other hand, main target in multi response optimization (MRO) problem is to find input variables values to achieve to desired output variables. Current study is a combination and modification of some works of authors in MRO and SAW subjects. This study utilizes an experiment design according to Taguchi arrays. Also a committee machine (CM) modeling the problem by CM using two approaches. The first CM consists eight experts with traditional approach in computation and second CM includes elite experts. Genetic algorithm was applied to find CM weights and desired responses. Results show that proposed approach in CM has a smaller root mean squire error (RMSE) than traditional approach. The validation of CM model is done by comparison of results with simulation of SAW process and residual stresses in a finite element environment. Finally, the results show few differences between the real case responses and the proposed algorithm responses.