A machine learning based optimization method towards removing undesired deformation of energy-absorbing structures

Optimization for the energy-absorbing structures can achieve their better crashworthiness and lightweight performance. However, traditional optimization methods cannot handle categorical responses such as deformation modes. This results some undesirable deformations which often appear in the optimization solution, making it difficult to guarantee the accuracy of optimization. To this end, a machine learning based optimization method for energy-absorbing structures was proposed in this study to remove the undesired deformations. In this method, a DOE method was used to get representative sample points in the design space; the machine learning techniques were adopted to build the prediction models for deformation modes and numerical responses; the Nondominated Sorting Genetic algorithm II (NSGA-II) was utilized for the multi-objective optimization. A case study on optimization of a shrink tube used in train energy absorption was used to verify the effectiveness of the optimization method. The optimization result for the shrink tube illustrated that the machine learning based optimization method can effectively remove the undesirable deformations for energy-absorbing structures. This study may pave a new way to improve the accuracy of energy-absorbing structure optimization.