A METHOD DEVELOPMENT FOR MODELING THE TECHNOLOGICAL PROCESS OF PRINTED CIRCUIT BOARD PRODUCTION BASED ON THE Q-SCHEME

Vladyslav Yevsieiev

Department of Computer-Integrated Technologies, Automation and Robotics, Kharkiv National University of Radio Electronics, Ukraine

Svitlana Maksymova

Department of Computer-Integrated Technologies, Automation and Robotics, Kharkiv National University of Radio Electronics, Ukraine

Ahmad Alkhalaileh

Senior Developer Electronic Health Solution, Amman, Jordan

Keywords: Printed Circuit Boards, Q-Circuit


Abstract

The article considers the development and modeling of the technological process of printed circuit board production based on the Q-scheme as an effective approach to optimizing production processes. A methodology is proposed that allows reducing the number of defects, improving product quality, and shortening the production cycle through digital modeling and automation of key stages. The use of the Q-scheme contributes to increasing productivity and adapting to the requirements of Industry 4.0, ensuring flexibility and efficiency of the technological process. The modeling results are presented, confirming the feasibility of implementing this approach in production practice.


References

1. Chala, O., & et al. (2024). Analysis of Systems for Coordination of Enterprise Subsystems Control. Journal of universal science research, 2(10), 127-137.

2. Yevsieiev, V., & et al. (2024). Improvement of SUSAN Image Filtering Method for PCB Quality Inspection. Journal of universal science research, 2(7), 106-116.

3. Maksymova, S., & et al. (2023). Defect Engineering: Application in Automation System Components Production Technological Processes. Multidisciplinary Journal of Science and Technology, 3(3), 243-251.

4. Vizir, Y., & et al. (2024). Lighting Control Module Software Development. Journal of Universal Science Research, 2(2), 29–42.

5. Zharikova, I., & et al. (2023). Automatic Machine of Plastic Bottles and Aluminum Cans Collection for Recycling. Journal of Universal Science Research, 1(11), 169–178.

6. Chala, O., & et al. (2024). Switching Module Basic Concept. Multidisciplinary Journal of Science and Technology, 4(7), 87-94.

7. Nevliudov, I., & et al. (2023). Monitoring system development for equipment upgrade for IIoT. IEEE 5th International Conference on Modern Electrical and Energy System (MEES), Kremenchuk, Ukraine, 2023, pp. 1-5.

8. Al-Sharo, Y. M., Abu-Jassar, A. T., Sotnik, S., & Lyashenko, V. (2021). Neural networks as a tool for pattern recognition of fasteners. International Journal of Engineering Trends and Technology, 69(10), 151-160.

9. Abu-Jassar, A. T., Al-Sharo, Y. M., Lyashenko, V., & Sotnik, S. (2021). Some Features of Classifiers Implementation for Object Recognition in Specialized Computer systems. TEM Journal: Technology, Education, Management, Informatics, 10(4), 1645-1654.

10. Sotnik, S., Mustafa, S. K., Ahmad, M. A., Lyashenko, V., & Zeleniy, O. (2020). Some features of route planning as the basis in a mobile robot. International Journal of Emerging Trends in Engineering Research, 8(5), 2074-2079.

11. Baker, J. H., Laariedh, F., Ahmad, M. A., Lyashenko, V., Sotnik, S., & Mustafa, S. K. (2021). Some interesting features of semantic model in Robotic Science. SSRG International Journal of Engineering Trends and Technology, 69(7), 38-44.

12. Lyashenko, V., Abu-Jassar, A. T., Yevsieiev, V., & Maksymova, S. (2023). Automated Monitoring and Visualization System in Production. International Research Journal of Multidisciplinary Technovation, 5(6), 9-18.

13. Гиренко, А. В., Ляшенко, В. В., Машталир, В. П., & Путятин, Е. П. (1996). Методы корреляционного обнаружения объектов. Харьков: АО “БизнесИнформ, 112.

14. Sotnik, S. Overview: PHP and MySQL Features for Creating Modern Web Projects / S Sotnik, V. Manakov, V. Lyashenko //International Journal of Academic Information Systems Research (IJAISR). – 2023. – Vol. 7, Issue 1. – P. 11-17.

15. Lyashenko, V. V., Matarneh, R., Baranova, V., & Deineko, Z. V. (2016). Hurst Exponent as a Part of Wavelet Decomposition Coefficients to Measure Long-term Memory Time Series Based on Multiresolution Analysis. American Journal of Systems and Software, 4(2), 51-56.

16. Lyashenko, V. V., Matarneh, R., & Deineko, Z. V. (2016). Using the Properties of Wavelet Coefficients of Time Series for Image Analysis and Processing. Journal of Computer Sciences and Applications, 4(2), 27-34.

17. Tvoroshenko, I., Lyashenko, V., Ayaz, A. M., Mustafa, S. K., & Alharbi, A. R. (2020). Modification of models intensive development ontologies by fuzzy logic. International Journal of Emerging Trends in Engineering Research, 8(3), 939-944.

18. Matarneh, R., Tvoroshenko, I., & Lyashenko, V. (2019). Improving Fuzzy Network Models For the Analysis of Dynamic Interacting Processes in the State Space. International Journal of Recent Technology and Engineering, 8(4), 1687-1693.

19. Lyashenko, V., & et al.. (2016). The Methodology of Image Processing in the Study of the Properties of Fiber as a Reinforcing Agent in Polymer Compositions. International Journal of Advanced Research in Computer Science, 7(1), 15-18.

20. Kuzemin, A., Lуashenko, V., Bulavina, E., & Torojev, A. (2005). Analysis of movement of financial flows of economical agents as the basis for designing the system of economical security (general conception). In Third international conference «Information research, applications, and education (pp. 27-30).

21. Deineko, Zh., & et al.. (2021). Features of Database Types. International Journal of Engineering and Information Systems (IJEAIS), 5(10), 73-80.

22. Sotnik, S., & Lyashenko, V. (2022). Prospects for Introduction of Robotics in Service. Prospects, 6(5), 4-9.

23. Ahmad, M. A., Sinelnikova, T., Lyashenko, V., & Mustafa, S. K. (2020). Features of the construction and control of the navigation system of a mobile robot. International Journal of Emerging Trends in Engineering Research, 8(4), 1445-1449.

24. Lyashenko, V., Laariedh, F., Ayaz, A. M., & Sotnik, S. (2021). Recognition of Voice Commands Based on Neural Network. TEM Journal: Technology, Education, Management, Informatics, 10(2), 583-591.

25. Ahmad, M. A., Baker, J. H., Tvoroshenko, I., & Lyashenko, V. (2019). Computational complexity of the accessory function setting mechanism in fuzzy intellectual systems. International Journal of Advanced Trends in Computer Science and Engineering, 8(5), 2370-2377.

26. Tahseen A. J. A., & et al.. (2023). Binarization Methods in Multimedia Systems when Recognizing License Plates of Cars. International Journal of Academic Engineering Research (IJAER), 7(2), 1-9.

27. Orobinskyi, P., Deineko, Z., & Lyashenko, V. (2020). Comparative Characteristics of Filtration Methods in the Processing of Medical Images. American Journal of Engineering Research, 9(4), 20-25.

28. Mousavi, S. M. H., Lyashenko, V., & Prasath, V. B. S. (2019). Analysis of a robust edge detection system in different color spaces using color and depth images. Computer Optics, 43(4), 632-646.

29. Sotnik S., & et al.. (2022). Key Directions for Development of Modern Expert Systems. International Journal of Engineering and Information Systems (IJEAIS), 6(5), 4-10.

30. Baranova, V., Orlenko, O., Vitiuk, A., Yakimenko-Tereschenko, N., & Lyashenko, V. (2020). Information system for decision support in the field of tourism based on the use of spatio-temporal data analysis. International Journal of Advanced Trends in Computer Science and Engineering, 9(4), 6356-6361.

31. Kuzomin, O., Lyashenko, V., Tkachenko, M., Ahmad, M. A., & Kots, H. (2016). Preventing of technogenic risks in the functioning of an industrial enterprise. International Journal of Civil Engineering and Technology, 7(3), 262-270.

32. Abu-Jassar, A. T., Attar, H., Amer, A., Lyashenko, V., Yevsieiev, V., & Solyman, A. (2025). Development and Investigation of Vision System for a Small-Sized Mobile Humanoid Robot in a Smart Environment. International Journal of Crowd Science, 9(1), 29-43.

33. Abu-Jassar, A. T., Attar, H., Amer, A., Lyashenko, V., Yevsieiev, V., & Solyman, A. (2024). Remote Monitoring System of Patient Status in Social IoT Environments Using Amazon Web Services (AWS) Technologies and Smart Health Care. International Journal of Crowd Science, 8.

34. Kobylin, O., & Lyashenko, V. (2020). Time Series Clustering Based on the K-Means Algorithm. Journal La Multiapp, 1(3), 1-7.

35. Lyubchenko, V., Veretelnyk, K., Kots, P., & Lyashenko, V. (2024). Digital image segmentation procedure as an example of an NP-problem. Multidisciplinary Journal of Science and Technology, 4(4), 170-177.

36. Matarneh, R., Sotnik, S., Belova, N., & Lyashenko, V. (2018). Automated modeling of shaft leading elements in the rear axle gear. International Journal of Engineering and Technology (UAE), 7(3), 1468-1473.

37. Vasiurenko, O., Baranova, V., & Lyashenko, V. (2024). Probability distributions of interest rates on loans and deposits in a study of banking activities. Multidisciplinary Journal of Science and Technology, 4(1), 49-56.

38. Omarov, M., Tykha, T., & Lyashenko, V. (2019). Use of Wavelet Techniques in the Study of Internet Marketing Metrics. Eskişehir Technical University Journal of Science and Technology A-Applied Sciences and Engineering, 20, 157-163.

39. Orobinskyi, P., Petrenko, D., & Lyashenko, V. (2019, February). Novel approach to computer-aided detection of lung nodules of difficult location with use of multifactorial models and deep neural networks. In 2019 IEEE 15th International Conference on the Experience of Designing and Application of CAD Systems (CADSM) (pp. 1-5). IEEE.

40. Kuzemin, A., & Lyashenko, V. (2009). Methods of comparative analysis of banks functioning: classic and new approaches. Information Theories & Applications, 16(4), 384-396.

41. Mousavi, S. M. H., MiriNezhad, S. Y., & Lyashenko, V. An Evolutionary-Based Adaptive Neuro-Fuzzy Expert System as a Family Counselor before Marriage with the Aim of Divorce Rate Reduction. Education, 1, 5.

42. Yevsieiev, V., & et al. (2023). An Automatic Assembly SMT Production Line Operation Technological Process Simulation Model Development. International Science Journal of Engineering & Agriculture, 2(2), 1–9.

43. Bondariev, A., & et al. (2023). Automated Monitoring System Development for Equipment Modernization. Journal of Universal Science Research, 1(11), 6–16.

44. Basiuk, V., & et al. (2024). Command System for Movement Control Development. Multidisciplinary Journal of Science and Technology, 4(6), 248-255.

45. Nevliudov, I., Yevsieiev, V., Baker, J. H., Ahmad, M. A., & Lyashenko, V. (2020). Development of a cyber design modeling declarative Language for cyber physical production systems. J. Math. Comput. Sci., 11(1), 520-542.

46. Nevliudov, I., & et al.. (2020). Method of Algorithms for Cyber-Physical Production Systems Functioning Synthesis. International Journal of Emerging Trends in Engineering Research, 8(10), 7465-7473.

47. Mustafa, S. K., Yevsieiev, V., Nevliudov, I., & Lyashenko, V. (2022). HMI Development Automation with GUI Elements for Object-Oriented Programming Languages Implementation. SSRG International Journal of Engineering Trends and Technology, 70(1), 139-145.

48. Nevliudov, I., Yevsieiev, V., Lyashenko, V., & Ahmad, M. A. (2021). GUI Elements and Windows Form Formalization Parameters and Events Method to Automate the Process of Additive Cyber-Design CPPS Development. Advances in Dynamical Systems and Applications, 16(2), 441-455.

49. Nassar, H., & Dahiya, R. (2021). Fused deposition modeling‐based 3D‐printed electrical interconnects and circuits. Advanced Intelligent Systems, 3(12), 2100102.

50. Rezaee, M., & et al. (2023). Eco-friendly recovery of base and precious metals from waste printed circuit boards by step-wise glycine leaching: process optimization, kinetics modeling, and comparative life cycle assessment. Journal of Cleaner Production, 389, 136016.

51. Adibhatla, V. A., & et al. (2021). Applying deep learning to defect detection in printed circuit boards via a newest model of you-only-look-once.

52. Xie, H., & et al. (2021). A method for surface defect detection of printed circuit board based on improved YOLOv4. In 2021 IEEE 2nd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE), IEEE, 851-857.

53. Wu, D., & et al. (2022). Research on the pyrolysis kinetics of resin powder on waste printed circuit board with different particle sizes at different heating rates: inspiration for the pyrolysis mechanism. Journal of Thermal Analysis and Calorimetry, 1-13.

54. Bhattacharya, A., & Cloutier, S. G. (2022). End-to-end deep learning framework for printed circuit board manufacturing defect classification. Scientific reports, 12(1), 12559.