Autors: Mitev, P. V.
Title: Development of a System for the Active Orientation of Small Screws †
Keywords: automatic parts feeding, machine vision, servo mechanism

Abstract: This paper reviews the process of research, development and production of a system for the active orientation of small screws. The parts feature two different shapes on each side, which is suitable for machine vision inspection and not for classical vibratory bowl traps. When a part enters the jig, it is rotated at an angle of 90° for inspection. Based on the orientation, it may stay in this position or be rotated at 180°. This allows for active orientation; regardless of how the screw is presented to the camera, it is always positioned in the correct orientation by a servo mechanism. The main challenges are related to the small dimensions of the part. First of all, it has a diameter of only 3 mm and a length of 7 mm. A vibratory bowl feeder is used only for feeding and there is no orientation functionality in it. Afterwards, a vibratory linear feeder is placed so the ready parts are stacked and, thus, some buffer is created. This is important because vibratory bowl feeders are known for having unequal productivity in time and this could be solved by the linear feeder. Another key difficulty is the quality of the source parts. They are produced by several suppliers and sometimes there are chips and other remnants alongside the packages with screws. This imposes the need for a cleaning system as part of the servo actuator’s mechanism. Cleaning does not occur on every cycle; it is based on a timer that is predefined.

References

  1. Mitev V. Malakov I. Analysis of the quality of polymer parts for automatic assembly AIP Conf. Proc. 2024 3063 060012 10.1063/5.0195873
  2. Mitev P. Malakov I. Development of a system for automatic feeding and orientation of cylindrical parts AIP Conf. Proc. 2024 3063 060017 10.1063/5.0195879
  3. Boothroyd G. Assembly Automation and Product Design Taylor & Francis Group Boca Raton, FL, USA 2005
  4. Tang S. Zhang Y. Jin Z. Lu J. Li H. Yang J. A Feature-Oriented Reconstruction Method for Surface-Defect Detection on Aluminum Profiles Appl. Sci. 2024 14 386 10.3390/app14010386
  5. Chakarski D. Andonova-Vakarelska T. Sarandeva A. Methodology for design of robotized technological modules for small parts (in Bulgarian) Proceedings of the NTC for Machine-Building Sozopol, Bulgaria 27 June 2000
  6. Gibson J.S.K.-L. Pei R. Heller M. Medghalchi S. Luo W. Korte-Kerzel S. Finding and Characterising Active Slip Systems: A Short Review and Tutorial with Automation Tools Materials 2021 14 407 10.3390/ma14020407 33467559
  7. Totev D. Dimitrova R. Dimitrov S. Main steps in the process of upgrade of existing systems for automation and control of industrial and manufacturing processes in order to fulfill the requirements of the concept “Industry 4.0” AIP Conf. Proc. 2024 3063 060007 10.1063/5.0195885
  8. Malakov I. Zaharinov V. Choosing an Optimal Size Range of the Product “Pipe Clamp” Recent Advances in Soft Computing and Cybernetics Matoušek R. Kůdela J. Springer Cham, Switzerland 2021 Volume 403 197 213 10.1007/978-3-030-61659-5_17
  9. Malakov I. Georgiev T. Zaharinov V. Tzokev A. Tzenov V. Demand modeling for the optimization of size ranges Proceedings of the 26th DAAAM International Symposium Zadar, Croatia 18–25 October 2015 Katalinic B. DAAAM International Vienna, Austria 2015 435 444 10.2507/26th.daaam.proceedings.058
  10. Dichev D. Koev H. Bakalova T. Louda P. A Kalman Filter-Based Algorithm for Measuring the Parameters of Moving Objects Meas. Sci. Rev. 2015 15 35 43 10.1515/msr-2015-0004
  11. Agrawal A. Sun Y. Barnwell J. Raskar R. Vision-guided robot system for picking objects by casting shadows Int. J. Robot. Res. 2010 29 155 173 10.1177/0278364909353955
  12. Kim K. Kim J. Kang S. Kim J. Lee J. Vision-based bin picking system for industrial robotics applications Proceedings of the 9th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI) Daejeon, Republic of Korea 26–28 November 2012 515 516
  13. Oh J.-K. Lee S. Lee C.-H. Stereo vision based automation for a bin-picking solution Int. J. Control Autom. Syst. 2012 10 362 373 10.1007/s12555-012-0216-9
  14. Hou L. Liu Z. You J. Liu Y. Xiang J. Zhou J. Pan Y. Tomato Sorting System Based on Machine Vision Electronics 2024 13 2114 10.3390/electronics13112114

Issue

Engineering Proceedings, vol. 70, 2024, Greece, https://doi.org/10.3390/engproc2024070055

Full text of the publication

Цитирания (Citation/s):
1. Vassilev V., Dimitrova R., Nikolov S., Optimization of the Operation of a Flexible Automated Assembly Line Using the Seven Basic Quality Tools, 2025, Engineering Proceedings, issue 1, vol. 104, DOI 10.3390/engproc2025104042, eissn 26734591 - 2025 - в издания, индексирани в Scopus
2. Naskinova I., Kolev M., Karova D., Milev M., Machine Learning-Based Blood Pressure Prediction Using Cardiovascular Disease Data: A Comprehensive Comparative Study, 2026, Electronics Switzerland, issue 2, vol. 15, DOI 10.3390/electronics15020312, eissn 20799292 - 2026 - в издания, индексирани в Scopus

Вид: статия в списание, публикация в реферирано издание, индексирана в Scopus