Hydraulic shovel digging phase simulation and force prediction using machine-learning techniques

The efficient and effective utilization of hydraulic excavators relies on, among other factors, a better understanding of the formation resistance during the excavation process. Studies on hydraulic shovel excavators have focused on optimizing the digging process by considering the reaction of the boom, stick and bucket, and analyzing the resulting moments at the shovel’s front-end joints. These studies failed to understand resistive forces from a statistical viewpoint. The purpose of this paper is to develop machine-learning models capable of predicting the formation of resistive forces during shovel excavation. The shovel’s excavation was simulated in the PFC 5.0 environment using a typical field-size hydraulic shovel bucket to generate the shovel contact forces required to overcome formation resistance