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An intelligent adjustable spanner for automated engagement with multi-diameter bolts/nuts during tightening/loosening process using vision system and fuzzy logic

  • Mohammed A. H. AliEmail author
  • Mohammed A. Alshameri
ORIGINAL ARTICLE
  • 54 Downloads

Abstract

The paper presents the development of a novel automated engagement intelligent spanner that is capable of autonomously changing its jaws’ size according to the diameters of the bolt’s/nut’s heads. It is a complete innovative system that involves the utilization of the vision system and fuzzy logic to make decisions about the diameter of head of bolt/nut. Image processing techniques has been implemented to extract the features of the bolts/nut, such as borders, non-borders area, outer diameter, and inner diameter of the head of the bolt/nut. It can be generally divided into three different stages, namely image pre-processing, image processing, and image post-processing. In image pre-processing stage, the image is prepared by applying some operations, such as acquiring streaming video, image cropping, gray-scale transformation, and background separation. Many filters and functions are applied in the image processing stage to efficiently get a clear border for the bolt/nut. In image post-processing, the necessary calculations are applied to get the diameter of the desired bolt, which involves the use of Hough Transformer and fitting circles searching process. The fuzzy logic-based decision-making algorithm is applied to the images resulting from the post-processing stage in order to do a final decision on the diameter of the bolt/nut and approximate it to the nearest standards diameter. Three bolts sizes are used in the experiments, namely M4, M4 with dust; M5; and M6 which are tested with 80 samples (20 for each). The results show the capability of image processing and fuzzy logic algorithms in making the right decisions on the diameter of bolts/nuts with 99% successful rate.

Keywords

Intelligent spanner Vision system Fuzzy logic Image processing Decision-making 

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Notes

Funding information

This research is provided by facilities and supported using UMP-Research University grant, RDU160131 by the Universiti Malaysia Pahang (UMP) and Ministry of High Education (MOHE).

References

  1. 1.
    Jesse R (1990) Operation Spanner. Book on Demand Pod. ISBN-10: 5513914530.Google Scholar
  2. 2.
    Sabbaghian M, Hivnor B, Soignet G, Mayeaux B, Payne W (1999) Adjustable socket wrench. USA Patent: US5918511AGoogle Scholar
  3. 3.
    Plamondon WJ (1999) Adjustable socket wrench. USA Patent: US6000300AGoogle Scholar
  4. 4.
    Colace RJ, Deturris AB (1990)Adjustable socket wrench extension. USA Patent: US4905548AGoogle Scholar
  5. 5.
    O'brien NJ, O'brien PM (1996) A socket for a socket wrench. USA Patent: WO1998022262A1Google Scholar
  6. 6.
    Blake JD, Marks JS (1998) Tool bit adapter for universal socket tool. US 6.085.619. USAGoogle Scholar
  7. 7.
    Lu Yi group (2014) High-precision intelligent spanner for track fastener bolt. China Patent: CN 202507047U.Google Scholar
  8. 8.
    Qingdao University of Technology (2016) Wrench system having intelligent functions of inducing and inducing methods and measurement methods. China Patent: CN102773822BGoogle Scholar
  9. 9.
    Huzhou Special Equipment Inspection Institute (2015) Intelligence moment spanner. China Patent: CN205465938UGoogle Scholar
  10. 10.
    Dianat I, Jafarabadi SRMNMA, Oskouei AE (2017) Effects of tool handle dimension and workpiece orientation and size on wrist ulnar/radial torque strength, usability and discomfort in a wrench task. Appl Ergon 59(Part A):422–430CrossRefGoogle Scholar
  11. 11.
    Mohankumar G, Abin K, Akhil K, Akshay IV, Varghese JA (2017) Design and fabrication of vehicles multi wheel nuts tightner and remover. Int J Res Mech Civil Eng 4(3):7–12Google Scholar
  12. 12.
    Sharma A, Singh RP, Lehana P (2015) Evaluation of the accuracy of genetic algorithms in orientation estimation of objects in industrial environment. Int J Sci Tech Adv 1(4):7–14Google Scholar
  13. 13.
    Akinnuli BO, Kareem B (2007) Development of an oil filter spanner for small-scale automotive maintenance industries. J Eng Appl Sci 2(3):591–594Google Scholar
  14. 14.
    Ali MAH (2018) Intelligent spanner for automated engagement with fastening means having multiple dimensions. Malaysia Patent: PI 2018700726Google Scholar
  15. 15.
    Ali MAH, Mailah M, Yussof WAB, Hamedon ZB, Yussof ZB, Majeed APP (2016) Sensors fusion based online mapping and features extraction of mobile robot in the road following and roundabout. IOP Conf Ser Mater Sci Eng 114(1):12135CrossRefGoogle Scholar
  16. 16.
    Ali M, Mailah M, Kazi S, Tang HH (2011) Defects detection of cylindrical object’s surface using vision system. 10th WSEAS International Conference on Computational Intelligence, Man-Machine Systems and Cybernetics (CIMMACS'11), Jakarta. pp. 222–227Google Scholar
  17. 17.
    Ali MAH, Mailah M, Tang HH, Kazi S (2012) Visual inspection of cylindrical product’s lateral surface using cameras and image processing. Int J Math Model Methods Appl Sci 6(2):340–348Google Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Faculty of Manufacturing EngineeringUniversiti Malaysia PahangPekanMalaysia

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