Optimization of the nosing process of metal pipe using genetic algorithm

Nosing is one of the methods of forming metal pipes, during this process, a metal shell or pipe is guided by a press into a mold that has the shape of the final piece. This method is used to make pressure tanks and CNG cylinders. In this research, the simulation of the nosing process of a steel pipe has been carried out, and then, in order to validate the results of the simulation, it has been compared with experimental tests. Then, with the help of response surface method, a series of experiments were designed and in order to investigate the effect of parameters of thickness, slope of preform pipe wall and friction coefficient between pipe and mold, as well as their interactive effects on the percentage of nosing, analysis of variance method was used. According to the obtained results, the thickness and slope of the preform pipe wall have the greatest effect on the nosing percentage compared to the friction coefficient between the pipe and the mold. A regression equation to predict the percentage of nosing is expressed based on the effective parameters, and finally, the optimal percentage of nosing for the regression equation obtained by the genetic algorithm is obtained.