Multi-Objective Optimization of Dimensional Accuracy in Electric Hot Incremental Sheet Forming

The forming defects of parts mainly include fracture, springback, thermal expansion, and rough surface during electric hot incremental forming, in which the springback and thermal expansion directly affects the dimensional accuracy of parts. In this paper, the combined optimization of process parameters and unsupported distances was proposed to control the dimensional accuracy of forming parts in the electric hot incremental sheet forming process. The predictive model of major errors was established through the response surface methodology, and then the multi-objective optimal model was obtained using Non-dominated Sorting Genetic Algorithms-II (NSGA-II). Meanwhile, a multi-objective optimal result was determined according to the error compensation feature of the forming process. On this basis, the effect of unsupported distances on dimensional errors was analyzed in detail, and a valid unsupported distance was proposed to further improve the forming accuracy of the whole forming region. Finally, the experimental result demonstrated that the combined optimal method proposed was accurate and feasible.