ELECTRONICS MANUFACTURING TESTING: A TECHNICAL DEEP DIVE
Keywords:
Manufacturing Testing, Artificial Intelligence In Electronics, Automated Inspection Systems, Digital Twin Technology, ndustry 4.0 Quality ControlAbstract
Manufacturing testing represents a critical component in modern electronics production, encompassing various methodologies and technological innovations. This comprehensive article examines the evolution of testing approaches, from traditional inspection methods to advanced AI-driven systems. The integration of artificial intelligence and machine learning has revolutionized quality control processes, particularly in PCB manufacturing and component verification. Contemporary testing protocols have adapted to address the increasing complexity of electronic devices, especially in response to miniaturization trends and multi-layer PCB designs. The emergence of digital twins, edge computing, and predictive analytics has further transformed the manufacturing landscape, enabling real-time monitoring and adaptive testing systems. This article explores these developments while addressing technical challenges and optimization strategies, providing insights into the future direction of electronics manufacturing testing.
References
Atif Siddiqui, Pablo Otero, et al., "An Approach for Collection and Analysis of Manufacturing Test Sites Data for Electronic Products," IEEE Global Conference on Wireless and Optical Technologies (GCWOT), 2020. Available: https://ieeexplore.ieee.org/document/9391597.
Harshitkumar j Ghelani, et al., "AI-Driven Quality Control in PCB Manufacturing: Enhancing Production Efficiency and Precision," International Journal of Scientific Research and Management (IJSRM), 2024. Available: https://www.researchgate.net/publication/384992034_AI-Driven_Quality_Control_in_PCB_Manufacturing_Enhancing_Production_Efficiency_and_Precision.
Nemanja Paunovic, et al., "A methodology for testing complex professional electronic systems," Serbian Journal of Electrical Engineering, 2012. [Online]. Available: https://www.researchgate.net/publication/270464911_A_methodology_for_testing_complex_professional_electronic_systems
John Malian, Bill Eklow, et al., "Embedded Testing in an In-Circuit Test Environment," IEEE International Test Conference, 2008. [Online]. Available: https://ieeexplore.ieee.org/document/4700652
Yi-Ming Chang, et al., "Deep Learning-based AOI System for Detecting Component Marks," IEEE International Conference on Big Data and Smart Computing (BigComp), 2023. [Online]. Available: https://ieeexplore.ieee.org/document/10066828
Xuan Li; Sukai Wang, et al., "3-D Inspection Method for Industrial Product Assembly Based on Single X-Ray Projections," IEEE Transactions on Instrumentation and Measurement ( Volume: 70), 2021. [Online]. Available: https://ieeexplore.ieee.org/document/9383264
Hasan Tercan, et al., "Machine learning and deep learning based predictive quality in manufacturing: a systematic review," Journal of Intelligent Manufacturing 33(4), 2022. [Online]. Available: https://www.researchgate.net/publication/360922608_Machine_learning_and_deep_learning_based_predictive_quality_in_manufacturing_a_systematic_review, doi: 10.1007/s00170-022-09007-4
Mohsen Soori, "AI-Based Decision Support Systems in Industry 4.0, A Review," Journal of Economy and Technology, 2024. [Online]. Available: https://www.researchgate.net/publication/383306939_AI-Based_Decision_Support_Systems_in_Industry_40_A_Review, doi: 10.1007/s10845-023-02089-1
A. Shahzad, J. Lee and N. Sharma, "Digital twin in smart manufacturing," Journal of Industrial Information Integration, Volume 26, March 2022, 100289. [Online]. Available: https://www.sciencedirect.com/science/article/abs/pii/S2452414X21000868.
Garima Nain, et al., "Towards edge computing in intelligent manufacturing: Past, present and future," Journal of Manufacturing Systems,Volume 62, January 2022, Pages 588-611. [Online]. Available: https://www.sciencedirect.com/science/article/abs/pii/S0278612522000103
Kunal Rathour, et al., "Smart Manufacturing: Issues and Challenges," Advances in Manufacturing and Materials (pp.217-226), 2024. [Online]. Available: https://www.researchgate.net/publication/382162810_Smart_Manufacturing_Issues_and_Challenges
Abdulrahman Alqoud, et al., "Industry 4.0: a systematic review of legacy manufacturing system digital retrofitting," Manufacturing Rev. 9, 32 (2022). [Online]. Available: https://mfr.edp-open.org/articles/mfreview/pdf/2022/01/mfreview220051.pdf
Yunyeong Goh, et al., "Consumer Electronics Product Manufacturing Time Reduction and Optimization using AI-based PCB and VLSI Circuit Designing," IEEE Transactions on Consumer Electronics PP(99):1-1, 2023. [Online]. Available: https://www.researchgate.net/publication/367482758_Consumer_Electronics_Product_Manufacturing_Time_Reduction_and_Optimization_using_AI-based_PCB_and_VLSI_Circuit_Designing
Deogratias Kibira, et al., "Methods and Tools for Performance Assurance of Smart Manufacturing Systems," Journal of Research of the National Institute of Standards and Technology, 2016. [Online]. Available: https://pmc.ncbi.nlm.nih.gov/articles/PMC7339637/pdf/jres-121-013.pdf
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