AI-Powered Real-Time Circuit Board Diagnostic System: Design, Development, and Implementation
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Description
This paper presents a novel AI-powered diagnostic system for electronics troubleshooting that combines computer vision and large language models in a portable, real-time device. The system achieves 95.3% component identification accuracy and reduces diagnostic time by 42% while lowering error rates from 18% to 3%. Through field validation across 12 repair facilities and 342 repair cases, we demonstrate the practical viability of AI-assisted diagnostics for electronics technicians. Key contributions include specialized diagnostic modes for different component types, continuous monitoring capability, and empirical validation of human-AI collaboration in technical work. The complete system costs $350-450 and achieves ROI within two weeks of deployment.
Keywords: artificial intelligence, computer vision, circuit board diagnostics, large language models, electronics repair, human-AI collaboration, real-time analysis, embedded systems
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References
- Brown, T.B., et al. (2023). Language models as technical advisors. Journal of AI Research, 76, 412-445. Chen, Y., Zhang, L., & Wang, Q. (2023). Learning with AI assistance. Computers in Human Behavior, 145, 107758. Johnson, R.K., & Lee, S.M. (2020). Time-motion analysis of electronics repair. IEEE Trans. Electronics Manufacturing, 43(3), 156-167. Kumar, A., & Patel, V. (2022). Deep learning for PCB component recognition. Expert Systems with Applications, 189, 116089. Martinez, C., et al. (2023). Multimodal AI for industrial inspection. Intl J. Production Research, 61(8), 2634-2651. Smith, D.A., et al. (2019). Expert troubleshooting strategies. Applied Ergonomics, 78, 142-153. Thompson, E.J., & Wilson, P.R. (2022). Design principles for AI-augmented work. ACM Trans. CHI, 29(4), Article 35. Zhang, H., Liu, Y., & Chen, X. (2021). AOI of solder joints using CNN. J. Manufacturing Systems, 59, 117-129.