A Measuring Method of Flexible Bar Based on Machine Vision

Abstract

This paper studies a high-precision measuring method about minimum thickness of thin neck and bending rigidity of flexible bar based on machine vision. The measurement system combines with the advantages of image vision inspection and laser displacement detection. After rough leveling and precise leveling to the clamped workpiece through the interworking of the camera, laser displacement sensor and servo motor, the image of thin neck and the upper profile of workpiece are captured by camera and laser displacement sensor respectively. Then the workpiece is rotated 180 through servo motor so as to capture the image of thin neck again and detect the bottom profile. Based on the two groups of images and profiles acquired above, we can figure out the minimum thickness of thin neck and bending rigidity of the workpiece with high precision. The effectiveness and feasibility of this method are confirmed by computer simulation with ANSYS software and experimental results with needle gauge and flexible bar.

Non‐destructive discrimination of homochromatic foreign materials in cut tobacco based on VIS‐NIR hyperspectral imaging

“…As a common imaging technique, the computer vision method identified FM based on the visualization of external properties (shape, color, size, surface texture and external defects) rather than internal chemical composition, 9 which has been used in industrial production 10 . Liu et al 11 employed a computer vision method to detect FM in cut tobacco online, and obtained pixel‐level detection results by enhancing color images of samples and extracting features. However, in actual production, if the the color of FM is similar to cut tobacco, the computer vision method will incorrectly categorize FM as cut tobacco.…”