Modeling, Analysis, and Optimization of Robotic Light Machining Tasks for Empowering Digital Twin: Generalized Impulse Model Approach

Abstract

Light machining tasks by robots are becoming an important issue to replace shortages of human resources. To improve the quality, safety and overall performance of manufacturing process, the modeling for estimation of forces and torques during the machining operations is on demand. In parallel, the digital model has also been developed which allow to detect the foul conditions, save energy & time and optimization of the real manufacturing process. Digital twins are one of them which use the offline and online data to simulate the physical manufacturing process. However, the empowerment of digital twins can be improved further by developing more accurate mathematical model which allow to simulate the physical machining process in real time. Accordingly, this paper presents a formulation for the mechanics of robotic light machining tasks to empower the digital twin. In this paper, a generalized impulse model is employed to analyze a light machining task that combines the linear and angular motions. For the implementation of an impulse model-based approach, the concept of both effective mass and effective inertia is newly introduced to reflect the dynamics of the environment, which depends on the hardness of the material and process parameters (feed rate and speed (rpm) etc.) of the machining task. Furthermore, optimal feed rates are calculated with consideration of effective mass/effective inertia and minimum task completion time. Moreover, simulations are carried out to choose the feasible direction of linear and angular velocities and optimal non-singular workspace for light machining tasks. Finally, the proposed methodology is validated through a quantitative comparison of simulation and experimental results by performing the drilling and milling tasks. A 6-DOF Universal robot (UR 5e) is used for simulations and experiments to corroborate the effectiveness of the proposed algorithms for light machining tasks. The developed methodology will certainly empower the digital twin for their physical analog during light machining operations.