Machine vision method for non-contact measurement of surface roughness of a rotating workpiece

Abstract

Purpose -This paper aims to propose a non-contact method using machine vision for measuring the surface roughness of a rotating workpiece at speeds of up to 4,000 rpm. Design/methodology/approach -A commercial digital single-lens-reflex camera with high shutter speed and backlight was used to capture a silhouette of the rotating workpiece profile. The roughness profile was extracted at sub-pixel accuracy from the captured images using the moment invariant method of edge detection. The average (Ra), root-mean square (Rq) and peak-to-valley (Rt) roughness parameters were measured for ten different specimens at spindle speeds of up to 4,000 rpm. The roughness values measured using the proposed machine vision system were verified using the stylus profilometer. Findings -The roughness values measured using the proposed method show high correlation (up to 0.997 for Ra) with those determined using the profilometer. The mean differences in Ra, Rq and Rt between the two methods were only 4.66, 3.29 and 3.70 per cent, respectively. Practical implications -The proposed method has significant potential for application in the in-process roughness measurement and tool condition monitoring from workpiece profile signature during turning, thus, obviating the need to stop the machine. Originality/value -The machine vision method combined with sub-pixel edge detection has not been applied to measure the roughness of a rotating workpiece.

High Precision Edge Detection Algorithm for Mechanical Parts

“…With the rapid development of non-contact, high precision and automation computer vision technology, the computer vision and its applications become a research hotspot. Vision measurement can quickly and efficiently detect the contours and plane shapes, angles and positions of various complex parts, especially the microscopic detection and quality control of precision components, which achieves the rapid measurement of object size or relative position [1]- [4].…”

Survey of Procedural Methods for Two-Dimensional Texture Generation

“…Some patterns may look like cell division, gastrulation, or the formation of spots and stripes on furry animals. The partial differential equation given by Equations (1) and (2), which model this process, may be solved by different numerical techniques:…”

“…Tactile sensing and texture recognition are key components in the areas of aeronautical engineering, optical engineering, and many cyber-physical systems, as they can provide rich details regarding object surfaces. Novel sensor devices have been designed to improve tactile sensing, haptic perception, and surface texture recognition [1,2]. Accordingly, accurate and reliable methods for measuring the perceptual texture features of surfaces (e.g., roughness, fineness, and granularity) are in high demand, which can work together with sensors to provide integrated systems.…”

Research on automatic detection technology for the compensation hole shape and position parameters of a brake master cylinder

“…The main measurement range of the detection technology proposed in this paper includes: the main hole diameter of the brake master cylinder is 15-45 mm, the position of the compensation hole is 5-150 mm, and the diameter of the compensation hole is 0.3-1.2 mm. Through the detection method of machine vision, the subjective uncertain factors are avoided, and machine vision provides a non-contact measurement, [10][11][12] it will not produce measuring force on the tested workpiece, no wear and damage, and has a high degree of automation, facilitates simple operation, and provides stability. It has good performance and can realize 100% inspection or sampling inspection of production parts.…”