On the Crucial Role of the On-Site and Visual Observations in Failure Analysis and Prevention

Kishore, Kaushal

doi:10.1007/s11668-021-01204-9

Cited by 5 publications

(1 citation statement)

References 8 publications

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“…Despite often being subtle and barely perceptible to the naked eye, defects can have profound implications, particularly in applications governed by stringent standards and subjected to high stress levels [2,3]. Even the slightest imperfections, whether in a low-cost capacitor or a high-end processing unit, have the potential to trigger product malfunction [4][5][6][7][8][9][10]. It can be disheartening to witness the failure of a top-tier product due to an unnoticed defect in a seemingly inconspicuous component valued at just one cent.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Defect Detection in Electronic Components during Assembly through Deep Learning

Weiss,

Caplan,

Horn

et al. 2024

Electronics

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This paper introduces a pioneering method for real-time image processing in electronic component assembly, revolutionizing quality control in manufacturing. By promptly capturing images from pick-and-place machines during the interval between component pick-up and mounting, defects are identified and promptly addressed in line. This proactive approach ensures that defective components are rejected before mounting, effectively preventing issues from ever occurring, thus significantly enhancing efficiency and reliability. Leveraging rapid network protocols such as gRPC and orchestration via Kubernetes, in conjunction with C++ programming and TensorFlow, this approach achieves an impressive average turnaround time of less than 5 milli-seconds. Rigorously tested on 20 operational production machines, it not only ensures adherence to IPC-A-610 and IPC-STD-J-001 standards but also optimizes production efficiency and reliability.

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Section: Introductionmentioning

confidence: 99%

Real-Time Defect Detection in Electronic Components during Assembly through Deep Learning

Weiss,

Caplan,

Horn

et al. 2024

Electronics

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Detecting Corrosion to Prevent Cracks in MLCCs with AI

Weiss

2023

J Fail. Anal. and Preven.

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In-Situ Screening of Defective Electronic Components through Edge Real-Time Big Data AI Analysis

Weiss,

Caplan,

Horn

et al. 2024

Preprint

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This paper presents a groundbreaking approach to real-time image processing in electronic component assembly, enhancing quality control in manufacturing. By capturing images from pick and place machines between component pickup and mounting, defects are identified and addressed in-line, significantly reducing the likelihood of defective products. Leveraging fast network protocols like gRPC and orchestration with Kubernetes, along with C++ programming and TensorFlow, this method achieves an average turnaround time of less than 5 milliseconds. Tested on 20 live production machines, it ensures compliance with IPC-A-610, and IPC-STD-J-001 standards while optimizing production efficiency and reliability.

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On the Crucial Role of the On-Site and Visual Observations in Failure Analysis and Prevention

Cited by 5 publications

References 8 publications

Real-Time Defect Detection in Electronic Components during Assembly through Deep Learning

Real-Time Defect Detection in Electronic Components during Assembly through Deep Learning

Detecting Corrosion to Prevent Cracks in MLCCs with AI

In-Situ Screening of Defective Electronic Components through Edge Real-Time Big Data AI Analysis

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