HYBRID PHYSICAL SIMULATION AND CONVOLUTIONAL NETWORK FOR RACK UPRIGHTS BENDING DAMAGE PREDICTION

Abstract

Accidental mechanical impacts such as forklifts may lead to serious degradation of the stability of industrial rack uprights.The finite element simulation model of the upright bending damage was established based on the physical test mechanism with five types of common upright in the industry as examples, and the analysis found that even a small impact deformation (1 mm) may lead to a decrease in the ultimate bearing capacity of the upright (maximum about 37%).Compared with other impacted positions, the bending damage at the prism made the upright stability decrease more significantly.Based on this, an intelligent prediction model of the bending damage state of the upright was established by physical simulation and convolutional neural network method.The results show that the residual load capacity values of the damaged upright obtained from the prediction model agree well with the finite element simulation data (the mean absolute percentage error is 5.99%) and can be used for rapid assessment of the bending damage performance of the rack upright.

Keywords

• Rack upright
• Finite element
• Bending damage
• Convolutional neural network
• Stability