Posture-dependent stability prediction of a milling industrial robot based on inverse distance weighted method

Abstract During robotic milling process, vibration is one of mainly factors that affect machining accuracy and surface quality, due to the low stiffness of robot structure. Robotic milling stability is dependent on the frequency response function (FRF) at tool tip, which is posture-dependent within the work volume. An approach to rapidly predict the industrial robot stability of any posture is presented in this paper. A lot of tool tip positions are arranged to conduct the impacting tests. For each tool tip position, five corresponding robot postures are obtained by determining the redundant freedom and solving inverse kinematics problem. FRFs at the tool tip of all tested robot postures are identified, and modal parameters are acquired. Based on the sample information that contains tested robot postures and corresponding modal parameters, inverse distance weighted (IDW) model is utilized to predict tool tip FRFs at any posture. Consequently, the milling stability lobe diagrams of robot at different postures are available to be obtained. Finally, the approach is proven to be feasible by performing robot milling experiments.