Multi-objective design and optimization of forklift gantry by using multiple surrogate models

Forklift is a kind of material handling robot, which is widely used in short-distance handling in the industrial field. However, at present, the structural design of forklifts is generally based on designers' experience, and there are still many problems in domestic forklifts compared with foreign countries. In this paper, the forklift gantry is taken as the research object, and four typical surrogate modeling techniques, namely PRS, KRG, RBF, and SVR models, are used for optimal design and analysis. The study shows that the KRG model has the best performance and RBF model has the worst performance in terms of global and local accuracy. Multi-objective optimization design of the weight and total deformation of the gantry is carried out with the maximum stress of the gantry and I-beam geometry as constraints. Taking the KRG model as an example, the comparison of the results before and after optimization shows that the weight of the I-beam of the forklift gantry is reduced by 11.9% and the maximum total deformation is reduced by 23.2% while satisfying the constraints. Global sensitivity analysis (GSA) of the forklift gantry reveals that the height of the I-beam has the greatest impact on the gantry performance.