Measurement method of screw thread geometric error based on machine vision

Jing, Min

doi:10.1177/0020294018786751

Cited by 20 publications

(18 citation statements)

References 11 publications

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“…The acquisition of a video sequence is accomplished by the hardware part of the system. With the threads being rotated at a uniform speed driven by the rotating platform, as in [7] and [8], CCD obtains the complete video sequence of a period of rotation as in [9], and then transmits the collected information to the computer through the image acquisition card. In addition to the traditional CCD camera, light source, image acquisition card, and computer, we need to design a rotating detection platform in the hardware of the system, which will make the measured parts rotate at a uniform speed under the control of a PLC.…”

Section: Boundarymentioning

confidence: 99%

Research on Modified Algorithms of Cylindrical External Thread Profile Based on Machine Vision

Gao

et al. 2020

Measurement Science Review

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In the non-contact detection of thread profile boundary correction, it remains challenging to ensure that the thread axis intersects the CCD camera axis perpendicularly. Here, we addressed this issue using modified algorithms. We established the Cartesian coordinate system according to the spatial geometric relationship of the thread. We used the center of the bottom of the thread as the origin, and the image of the extreme position image was replaced by the image of the approximate extreme position. In addition, we analyzed the relationship between the boundary of the theoretical thread image and the theoretical profile. We calculated the coordinate transformation of the point on the theoretical tooth profile and the coordinate function of the point on the boundary of the theoretical image. At the same time, the extreme value of the function was obtained, and the boundary equation of the theoretical thread image was deduced. The difference equation between the two functions was used to correct the boundary point of the actual thread image, and the fitting results were used to detect the key parameters of the external thread of the cylinder. Further experiment proves that the above algorithm effectively improves the detection accuracy of thread quality, and the detection error of main geometric parameters is reduced by more than 50 %.

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Section: Boundarymentioning

confidence: 99%

Research on Modified Algorithms of Cylindrical External Thread Profile Based on Machine Vision

Gao

et al. 2020

Measurement Science Review

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“…Man confirmed that the phenomenon of thread profile distortion always exists in measuring screw thread by projection image and proposed the compensation of thread profile distortion in image measuring screw thread [ 7 ]. Min designed the measurement method of screw thread geometric error based on machine vision, whose linear precision is less than 10 μm and can be used to detect the comprehensive parameters of screw thread [ 8 ]. Senthil et al proposed the vision measurement of metric screw thread parameters based on orientation invariant feature [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

“…Aiming at the above problems, inspired by the robotic arm in the literature [ 6 ], a machine vision system with an adjustable light source device based on a manipulator is designed to solve the first problem above. Based on the characteristics of the thread profile, this paper extended the concept of directional edge detection in the literature [ 8 ], and a directional edge detection operator is proposed to solve the problem of precise positioning of the thread teeth. Noise filtering algorithms are developed based on the connected domain to solve the third problem above, and the on-machine high-precision measurement of thread parameters is realized.…”

Section: Introductionmentioning

confidence: 99%

Automatic Measurement of External Thread at the End of Sucker Rod Based on Machine Vision

Wang

2022

Sensors

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Aiming at the low efficiency of manual measurement of threads and the lack of practicability in machine vision measurement before, online size measurement of threads at the end of sucker rods based on machine vision was studied. A robotic arm is used to carry an optical device to achieve high-quality image acquisition of threads. Based on the prior knowledge of the thread profile angle, the directional edge detection operator is customized to achieve the accurate detection of the left and right edges of the thread. Noise filtering, sorting, and left and right edge-matching algorithms based on connected domains are developed to eliminate the interference effects of electrostatic dust and oil pollution in online measurement, and the dimension of thread profile angles, pitches, major diameters, and minor diameters can be precisely calculated. The experimental results show that the screw thread parameter measurement time is about 0.13 s; the maximum and minimum average errors of the thread angles are 0.011° and 0.632°, respectively; and the total average deviation is less than 0.08°. For the screw thread pitch, major diameter, minor diameter, and pitch diameter parameter measurement, the deviation of the measurement results between the proposed method and the universal tool microscope (UTM) method is less than 10 μm. It fully proves the effectiveness and accuracy of the method in this paper and, at the same time, shows that the method has good real-time performance and high application significance, which lays a good foundation for the subsequent online thread measurement.

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“…In terms of the measurement method, Yang applied the radial basis function (RBF) neural network approach to measure and compensate for geometric displacement errors [26]. Min applied machine vision to measure screw thread geometric errors [27]. Yang applied differential motion matrices to identify position-independent geometric errors of five-axis serial machine tools [28].…”

Section: Introductionmentioning

confidence: 99%

Coupling and Decoupling Measurement Method of Complete Geometric Errors for Multi-Axis Machine Tools

et al. 2020

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Precision and ultra-precision machining technology rely mainly on the machine tools’ accuracy. To improve it, the measurement, calculation, prediction and control of geometric errors are critical. The traditional measurement methods have lower precision because of ignoring small angle errors. To obtain complete geometric errors of multi-axis machine tools, this paper proposes a new method of coupling and decoupling measurement. Specifically, we used a laser interferometer and dial indicators to measure 36 items of complete geometric errors of multi-axis machine tools. A homogeneous transformation matrix (HTM) was applied to model the error transfer route. The transfer law of complete errors for each machining point was explored and derived. Furthermore, we selected and calculated integrated errors of 36 machining points. Finally, we proved the correctness of the method by comparing the measurement result of a ball bar test and coupling and decoupling measurement of geometric errors. We found that items of small geometric angle errors have a greater impact on machining accuracy than those of geometric displacement errors. Complete geometric errors measured via the coupling and decoupling measurement method can evaluate integrated errors more precisely and comprehensively.

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Measurement method of screw thread geometric error based on machine vision

Cited by 20 publications

References 11 publications

Research on Modified Algorithms of Cylindrical External Thread Profile Based on Machine Vision

Research on Modified Algorithms of Cylindrical External Thread Profile Based on Machine Vision

Automatic Measurement of External Thread at the End of Sucker Rod Based on Machine Vision

Coupling and Decoupling Measurement Method of Complete Geometric Errors for Multi-Axis Machine Tools

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