Measurement of bending deformation of sheet metal using machine vision

The aim of this study is to propose a fast and effective method to measure the curvature distribution and bending angle of sheet metal in the bending deformation using machine vision. An open-source computer vision library (OpenCV) was used for image processing. First, the image is calibrated for perspective and lens distortion. After that, the image is subjected to color extraction and binary conversion. The outer geometry of the material was obtained using edge detection. With the proposed method, the bending angle and curvature distribution in the four-point bending and V-bending tests are measured. The proposed machine vision method exhibits errors of 0.55% and 0.12% compared with the 3D scanner measurements for the bending angle in the four-point bend and V-bend tests after springback, respectively. The curvature distribution in bending deformation was also obtained. As an application of the proposed method, the moment–curvature diagram of the material was determined based on the obtained curvature at the center. Additionally, the optimal stroke was calculated for the target bend angle during the V-bending process. The proposed method proved to be an accurate and efficient way to measure bending deformation of sheet metal.