A novel non-contact measuring system for the thread profile of a ball screw

Abstract

Abstract. In order to meet the demand of high position accuracy, many methods have been developed for measuring the thread profile features of ball screws. However, most of these efforts are limited to measuring a small number of ball screw features and therefore less efficient. In this study, an automatic, non-contact measuring system for the thread profile of a ball screw was developed. It is capable of measuring most common features of the ball screw thread profile and can measure ball screws of different sizes and lengths. The main part of the system is the light curtain which has a high-resolution line-scan CCD camera to measure the projected outer contour of the target. Data points from the thread profile of a ball screw were collected by the measuring system and data pre-processing was performed. Then, the proposed method was used to calculate the most important geometric parameters and errors of the thread profile. The accuracy of the measuring system was verified by testing and evaluating four sets of ball screws from different manufacturers.

A Novel Non-Contact Measurement Method of Ball Screw Thread Profile Detection Based on Machine Vision

“…Also, 𝑀𝑀 will be blocked by 𝐵𝐵 when 𝑍𝑍 𝐵𝐵 = min {𝑍𝑍 𝑀𝑀 , min𝑍𝑍 𝐶𝐶 , 𝑍𝑍 𝐵𝐵 }, and the point of the projection boundary corresponding to 𝑀𝑀 is 𝐵𝐵. The differential equation ∆𝑧𝑧 2 between the thread profile and the projection curve in the axial section can be obtained from equations ( 6)-( 9), as shown in (10).…”

“…According to ( 9) and ( 10), 𝑍𝑍 𝐶𝐶 must be optimized and it is assumed that 𝑦𝑦 = 𝑍𝑍 𝐶𝐶 . From (10) it is evident that the search for the optimal value of 𝑦𝑦 is a typical One-Dimensional Optimization problem, as in [19], which can be solved by the Linear Search [20]. The Linear Search method can be divided into two stages: First, the search interval containing the optimal solution of the problem is determined.…”

“…At present, many relevant scholars at home and abroad have conducted research on this subject and have already formed a relatively complete thread detection system, as in [3]- [8], which can be described as follows: First, the original image of the thread profile by CCD or other optical camera is captured, then the features of thread profile based on image processing techniques such as filtering, boundary extraction and curve fitting are extracted, and finally the parameters are measured from the acquired features such as the thread profile, which are considered as the result of detection. Min and Zhao respectively designed a non-contact thread parameter measurement system based on machine vision to support efficient and accurate measurement of the thread contact angle, as in [9], [10]; Rao et al summarized the image processing technology and computer vision algorithms currently used for external thread detection, as in [11]; Senthilnathan used a diffuse reflection light source to obtain a thread profile projection, and proposed a profile processing algorithm to estimate the thread parameters, as in [12]; Chen et al integrated the photoelastic effect and an image processing algorithm to measure the contact angle of the ball screw, as in [13]; Li et al proposed a Res Unet-based thread edge recognition method that eliminates the need for thread area calibration and identifies the thread edge in a complex environment, as in [14]; Chen took the possible thread shape distortion during CCD shooting into account, and gave a corresponding compensation algorithm for the image distortion on the optical angle, as in [15]. However, the above solutions are only applied to the detection of ordinary threads with a linear profile, while the Journal homepage: https://content.sciendo.com thread profile of the ball screw is a curve, which is more difficult to detect.…”

Curvature-Based Machine Vision Method for Measuring the Dimension of Ball Screws

“…According to the design principle, the center position and radius are indispensable. The least square method is a standard approach to the approximate solution of overdetermined systems; this means that the overall solution minimizes the sum of the squares of the errors made in the results of every single equation [28].…”

High-speed measurement system for cord-rubber composites on conveyor belt based on machine vision

“…In 2016, Li et al proposed a conveyor belt longitudinal tear detection method, which used a line scan camera to capture conveyor belt images and then used an improved SSR algorithm to extract the information of conveyor belt longitudinal tear features to achieve online detection of conveyor belt longitudinal tear faults [8]. In 2018, Zhao et al developed a non-contact measurement system for ball screw thread profile by using a high-resolution line scan camera to capture the profile information of the ball screw and proposed an algorithm for calculating the main geometric parameters of the ball screw [9]. In 2018, Zhan et al proposed a measurement method for geometric parameters of railroad contact networks using a binocular vision system consisting of two line scan cameras and proposed an error compensation method for compensating the errors caused by vibrations during operation.…”