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 expansion directly affects dimensional accuracy parts. In this paper, combined optimization process parameters unsupported distances was proposed to control sheet process. predictive model major errors established through response methodology, then multi-objective optimal obtained using Non-dominated Sorting Genetic Algorithms-II (NSGA-II). Meanwhile, a result determined according error compensation feature On basis, effect on analyzed detail, valid distance further improve whole region. Finally, experimental demonstrated that method accurate feasible.