基于深度学习与遗传算法的铝罐二重卷封工艺参数多目标优化OA

The double seaming process of aluminum cans is a critical manufacturing procedure that determines the sealing reliability and operational safety of beverage packaging.Its forming quality directly affects the product's leakage resistance and the service life of production equipment.To synergistically improve the overlap rate and tightness while effectively controlling roller contact stress,a multi-objective optimization method integrating the Deep Feedforward Neural Network(DFNN)and Non-dominated Sorting Genetic Algorithm Ⅱ(NSGA-Ⅱ)was proposed.First,a high-precision finite element model was established to analyze the influence mechanisms of key process parameters on sealing performance and mechanical response.Then,a DFNN surrogate model was constructed to achieve accurate mapping of the overlap rate,wave peak count and maximum contact stress.Finally,the NSGA-Ⅱ algorithm was adopted for multi-objective optimization to obtain the Pareto optimal process parameter combination.The research results show that the optimized scheme is significantly superior to the initial design in terms of overlap rate,tightness and maximum contact stress,and the error between the experimental verification results and the simulation prediction results is less than 7％.The proposed method is effective and engineering applicable for comprehensively improving sealing performance and extending the service life of equipment.