AI-Driven Quality Control in PCB Manufacturing: Enhancing Production Efficiency and Precision
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This paper investigates the application of Artificial Intelligence (AI) in enhancing quality control within Printed Circuit Board (PCB) manufacturing processes, focusing on how AI-driven technologies improve production efficiency and precision. The research addresses traditional quality control methods, such as manual inspection and Automated Optical Inspection (AOI), highlighting their limitations in keeping up with the complexities and demand for high-precision PCBs in modern electronics.
The study delves into how AI technologies, particularly machine learning, computer vision, and predictive analytics, are being leveraged to overcome these limitations. By automating defect detection, improving accuracy, and enabling real-time analysis, AI systems not only streamline the quality control process but also significantly reduce human error and production costs.
AI-driven quality control systems are shown to increase defect detection rates, reduce inspection times, and enhance overall production throughput. The paper includes a comparative analysis between traditional and AI-based quality control methods, revealing a notable improvement in both detection accuracy and production speed when AI systems are employed. Additionally, cost efficiency is explored, demonstrating how AI systems reduce waste, minimize rework, and lower operational costs.
The potential challenges of AI implementation, such as the high initial costs, data requirements, and integration with legacy systems, are also discussed. The paper concludes that despite these challenges, AI offers a transformative solution to the growing demands of the PCB manufacturing industry, with the ability to scale effectively and adapt to future technological advancements.
Ultimately, this research underscores the significant role of AI in revolutionizing PCB quality control by providing a more efficient, precise, and cost-effective approach, paving the way for further innovation in the electronics manufacturing sector.
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1. Arora, M. (2023). AI-Driven Industry 4.0: Advancing Quality Control through Cutting-Edge Image Processing for Automated Defect Detection. International Journal of Computer Science and Mobile Computing, 12(8), 16-32.
2. Lodhi, S. K., Gill, A. Y., & Hussain, I. (2024). AI-Powered Innovations in Contemporary Manufacturing Procedures: An Extensive Analysis. International Journal of Multidisciplinary Sciences and Arts, 3(4), 15-25.
3. Huang, Z., Shen, Y., Li, J., Fey, M., & Brecher, C. (2021). A survey on AI-driven digital twins in industry 4.0: Smart manufacturing and advanced robotics. Sensors, 21(19), 6340.
4. Archana, T., & Stephen, R. K. (2024). The Future of Artificial Intelligence in Manufacturing Industries. In Industry Applications of Thrust Manufacturing: Convergence with Real-Time Data and AI (pp. 98-117). IGI Global.
5. Katari, M., Shanmugam, L., & Malaiyappan, J. N. A. (2024). Integration of AI and Machine Learning in Semiconductor Manufacturing for Defect Detection and Yield Improvement. Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023, 3(1), 418-431.
6. Busia, P., Marche, C., Meloni, P., & Reforgiato Recupero, D. (2024, June). Design of an AI-driven Architecture with Cobots for Digital Transformation to Enhance Quality Control in the Food Industry. In Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization (pp. 424-428).
7. Aravind, R., & Shah, C. V. (2024). Innovations in Electronic Control Units: Enhancing Performance and Reliability with AI. International Journal Of Engineering And Computer Science, 13(01).
8. Yadav, A. B. (2023, April). Gen AI-Driven Electronics: Innovations, Challenges and Future Prospects. In International Congress on Models and methods in Modern Investigations (pp. 113-121).
9. Khinvasara, T., Ness, S., & Shankar, A. (2024). Leveraging AI for Enhanced Quality Assurance in Medical Device Manufacturing. Asian Journal of Research in Computer Science, 17(6), 13-35.
10. Patel, B. (2023). Enhancing PCB Reliability through Cutting-edge Circuit Simulator Applications. American Digits: Journal of Computing and Digital Technologies, 1(1), 49-61.
11. Lodhi, S. K., Hussain, I., & Gill, A. Y. (2024). Artificial Intelligence: Pioneering the Future of Sustainable Cutting Tools in Smart Manufacturing. BIN: Bulletin of Informatics, 2(1), 147-162.
12. Sawlani, K., & Mesbah, A. (2024). Perspectives on artificial intelligence for plasma-assisted manufacturing in semiconductor industry. In Artificial Intelligence in Manufacturing (pp. 97-138). Academic Press.
13. Kausar, M., Ishtiaq, M., & Hussain, S. (2021). Distributed agile patterns-using agile practices to solve offshore development issues. IEEE Access, 10, 8840-8854.
14. Kausar, M., & Al-Yasiri, A. (2015, July). Distributed agile patterns for offshore software development. In 12th International Joint Conference on Computer Science and Software Engineering (JCSSE), IEEE (p. 17).
15. Kausar, M., Muhammad, A. W., Jabbar, R., & Ishtiaq, M. (2022). Key challenges of requirement change management in the context of global software development: systematic literature review. Pakistan Journal of Engineering and Applied Sciences.
16. Vaithianathan, M. (2024). Real-Time Object Detection and Recognition in FPGA-Based Autonomous Driving Systems. International Journal of Computer Trends and Technology, 72(4), 145-152.
17. Kausar, M., & Al-Yasiri, A. (2017). Using distributed agile patterns for supporting the requirements engineering process. Requirements Engineering for Service and Cloud Computing, 291-316.
18. Cena, J., & Harry, A. (2024). Blockchain-Based Solutions for Privacy-Preserving Authentication and Authorization in Networks.
19. Vaithianathan, M., Patil, M., Ng, S. F., & Udkar, S. (2023). Comparative Study of FPGA and GPU for High-Performance Computing and AI. ESP International Journal of Advancements in Computational Technology (ESP-IJACT), 1(1), 37-46.
20. Kausar, M., Mazhar, N., Ishtiaq, M., & Alabrah, A. (2023). Decision Making of Agile Patterns in Offshore Software Development Outsourcing: A Fuzzy Logic-Based Analysis. Axioms, 12(3), 307.
21. Vaithianathan, M., Patil, M., Ng, S. F., & Udkar, S. (2024). Integrating AI and Machine Learning with UVM in Semiconductor Design. ESP International Journal of Advancements in Computational Technology (ESP-IJACT) Volume, 2, 37-51.
22. Kausar, M. (2018). Distributed agile patterns: an approach to facilitate agile adoption in offshore software development. University of Salford (United Kingdom).
23. Seenivasan, D., & Vaithianathan, M. Real-Time Adaptation: Change Data Capture in Modern Computer Architecture.
24. Mazhar, N., & Kausar, M. (2023). Rational Coordination in Cognitive Agents: A Decision-Theoretic Approach Using ERMM. IEEE Access.
25. Wang, J. (2021). Impact of mobile payment on e-commerce operations in different business scenarios under cloud computing environment. International Journal of System Assurance Engineering and Management, 12(4), 776-789.
26. Wang, J., & Zheng, G. (2020). Research on E-commerce Talents Training in Higher Vocational Education under New Business Background. INTI JOURNAL, 2020(5).
27. Xiao, G., Lin, Y., Lin, H., Dai, M., Chen, L., Jiang, X., ... & Zhang, W. (2022). Bioinspired self-assembled Fe/Cu-phenolic building blocks of hierarchical porous biomass-derived carbon aerogels for enhanced electrocatalytic oxygen reduction. Colloids and Surfaces A: Physicochemical and Engineering Aspects, 648, 128932.
28. Xiao, G., Lin, H., Lin, Y., Chen, L., Jiang, X., Cao, X., ... & Zhang, W. (2022). Self-assembled hierarchical metal–polyphenol-coordinated hybrid 2D Co–C TA@ gC 3 N 4 heterostructured nanosheets for efficient electrocatalytic oxygen reduction. Catalysis Science & Technology, 12(14), 4653-4661.
Copyright (c) 2024 Harshitkumar Ghelani
This work is licensed under a Creative Commons Attribution 4.0 International License.
