Influence of machining parameters on dynamic errors in a hexapod machining cell

Abstract

Dynamic errors from the robotic machining process can negatively impact the accuracy of manufactured parts. Currently, effectively reducing dynamic errors in robotic machining remains a challenge due to the incomplete understanding of the relations hip between machining parameters and dynamic errors, especially for hexapod machining cell. To address this topic, a dynamic error measurement strategy combining a telescoping ballbar, an Unscented Kalman Filter (UKF), and particle swarm optimization (PSO) was utilized in robotic machining. The machining parameters, including spindle speed, cutting depth, and feeding speed, were defined using the Taguchi method. Simultaneously, vibrations during machining were also systematically measured to fully comprehend the nature of dynamic errors. Experimental results indicate that dynamic errors in a hexapod machining cell (HMC) are significantly amplified in machining setups, ranging from 4 to 20 times greater compared to non-machining setups. These errors are particularly influenced by machining parameters, especially for spindle speed. Furthermore, the extracted dynamic errors exhibit comparable frequency distributions, such as spindle frequency and tool passing frequency, to the vibration signals obtained at the chosen sampling rate. This expands the application and enhances the comprehension of dynamic errors for spindle and cutting tool condition recognition.