Nonlinear kinematic constraints method for 5-axis machining based on PSO

Purpose This paper aims to tackle the problems of nonlinear motion and G1 discontinuity at transition points in the velocity planning for multiaxis robots or machine tools and it proposes a novel method called nonlinear kinematic constraints based on swarm intelligence for time optimization (NKCTO). The study aims to enhance machining efficiency within the scope of nonlinear kinematic constraints. Design/methodology/approach First, a 5-axis kinematic model based on the Frenet frame is established. Next, considering the nonlinear characteristics of 5-axis machining and the specifics of continuous line segments path, a nonlinear kinematic constraint model is developed. And the particle swarm optimization (PSO) method is combined with the overall solution method for the nonlinear kinematic constraint model. Additionally, we estimate the constraint velocity at joint points using a 5-dimensional curvature estimation method. The resulting kinematic parameter constraints are suitable and effective. Findings Simulation and machining experiments demonstrate that the NKCTO method can effectively achieve kinematic parameter constraints and improve the efficiency of 5-axis machining. In simulation experiments, compared to the CORNER-Based and SLAPT-Based methods, the NKCTO method achieves efficiency gains of 8.42% and 6.45%, respectively. In practical machining experiment, the machining time is reduced from 99.64 seconds to 91.11 seconds. Originality/value The method proposed in paper effectively considers the nonlinearities in 5-axis motion and enhances both efficiency and quality in continuous small line segments 5-axis machining.