The paper deals with stiffness matrix identification of complex-shape robotic links. It proposes an enhancement that simplifies the identification procedure by splitting the link into simple segments for which the identification procedure is trivial. Further, the segments’ stiffness matrices are aggregated into the desired link stiffness matrix. The proposed enhancement allows users to avoid ambiguity with reference points for link connections with multiple surfaces. The developed technique is applied to real-world problem dealing with tool stiffness matrix identifications from the CAD-based virtual experiments.

This article introduces a frequency-based approach of obtaining preform shapes for multi-stage incremental robo-forming of sheet metals. Incremental sheet metal forming (ISF) is a die-less forming method that uses a general purpose single point tool. Robo-forming is a variant of incremental sheet metal forming that employs industrial robots to force the tool along a desired trajectory on the blank surface. The use of robo-forming in various industries has increased over the last decade. Challenges for ISF lie in optimal path planning and maximizing the formability, especially for higher wall angle components and complex shapes with intricate details. The formability can be further improved when deformed in multiple stages. The preform shapes in the initial stages improve the formability of the final stage. Currently, there is no standardized procedure to obtain preform shapes; instead, intuition based on experience and other heuristic methods are employed. The proposed methodology is a step in that direction, which provides a standard approach for generating preform shapes, based on the frequency decomposition of sectioning data of the component, leveraging the Fast Fourier Transform (FFT) algorithm, for application in incremental sheet metal forming and robo-forming. Experimental results utilizing the proposed preform show enhanced forming depth of the target geometry by 164% compared to the case without preform, validating the effectiveness of the methodology.