Part precision improvement in incremental sheet forming of not axisymmetric parts using an artificial cognitive system

• Manufacturing small batches at low costs and time represents a challenge for the actual market.
• Incremental Sheet Forming (ISF) process is flexible, time and cost saving but formed parts have a low geometrical accuracy.
• Novel method based on Iterative Learning Control (ILC) for improving the geometrical accuracy.
• An intelligent system able to learn and correct itself without a priori knowledge was obtained by integrating ILC and ISF.
• Achieved accuracy is comparable with traditional processes independently from the part geometry, material or toolpath.

Incremental sheet forming is a flexible process that uses a hemispherical tool moved by a CNC machine to form a blank sheet. It is adopted in the production of prototypes, small series or customized parts since it is characterized by low costs and higher process times with respect to traditional forming technologies. One of its main lacks is represented by the low geometrical accuracy, therefore solutions for errors reduction or compensation are required to improve the process. In this paper, an iterative algorithm based on an artificial cognitive system is presented and validated on a not axisymmetric part adopting different tool paths and materials. The results show that the integration of the proposed algorithm with the ISF manufacturing system allows to improve its intelligence and to achieve a geometrical precision similar to the traditional forming processes.