Detecting Corrosion to Prevent Cracks in MLCCs with AI

Weiss, E.

doi:10.1007/s11668-023-01824-3

Cited by 3 publications

(6 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, we documented several use cases of corrosion detected using this method, subsequently verified by lab analysis [16,34]. Moreover, we devised a method to mitigate the risk of cracks in MLCCs (multilayer ceramic capacitors) by the early detection of the evidence of corrosion in the soldering terminals as we demonstrated that corrosion serves as a precursor for crack development [12].…”

Section: Introductionmentioning

confidence: 82%

“…In each section, we highlight the standard sections that are relevant to the presented method and that the method can detect automatically on all inspected components. The detection algorithm is only mentioned in this work and is elaborated in [12,16,30,32,34]. In this work, the novelty is in adapting the methods to real-time operation.…”

Section: Compliance With Ipc-a-610h and Ipc-j-std-001 Standardsmentioning

confidence: 99%

“…The visible defects are scratches, dents, distortions, deformations, peeling, shorts, etc. this work and is elaborated in [12,16,30,32,34]. In this work, the novelty is in adapting the methods to real-time operation.…”

Section: Defects On Component Leads/terminationsmentioning

confidence: 99%

“…Figure 4 presents examples of bent leads and coplanarity issues automatically detected by the algorithm that exceed 10% of the lead's width. this work and is elaborated in [12,16,30,32,34]. In this work, the novelty is in adapting the methods to real-time operation.…”

Section: Bent or Warped Leadsmentioning

confidence: 99%

“…Defects encompass a wide range of issues, spanning from cracks [11][12][13][14][15], fractures, and the peeling of metallization to deformations, discoloration, mold, corrosion [4,[16][17][18][19][20][21][22][23], bent leads, deformed leads, and misshapen BGA balls, among others (see examples in Figures 1 and 2). To provide clarity and establish a baseline for defect identification, leading standards such as IPC-A-610H [3] and IPC-STD-J-001 [24] offer comprehensive definitions of defects on assembled PCBs.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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

Weiss,

Caplan,

Horn

et al. 2024

Electronics

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 82%

Section: Compliance With Ipc-a-610h and Ipc-j-std-001 Standardsmentioning

confidence: 99%

Section: Defects On Component Leads/terminationsmentioning

confidence: 99%

Section: Bent or Warped Leadsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Weiss,

Caplan,

Horn

et al. 2024

Electronics

Self Cite

View full text Add to dashboard Cite

show abstract

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

Weiss,

Caplan,

Horn

et al. 2024

Preprint

View full text Add to dashboard Cite

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.

show abstract

Advancements in Electronic Component Assembly: Real-Time AI-Driven Inspection Techniques

Weiss

2024

Electronics

View full text Add to dashboard Cite

This study presents an advanced methodology for improving electronic assembly quality through real-time, inline inspection utilizing state-of-the-art artificial intelligence (AI) and deep learning technologies. The primary goal is to ensure compliance with stringent manufacturing standards, notably IPC-A-610 and IPC-J-STD-001. Employing the existing infrastructure of pick-and-place machines, this system captures high-resolution images of electronic components during the assembly process. These images are analyzed instantly by AI algorithms capable of detecting a variety of defects, including damage, corrosion, counterfeit, and structural irregularities in components and their leads. This proactive approach shifts from conventional reactive quality assurance methods by integrating real-time defect detection and strict adherence to industry standards into the assembly process. With an accuracy rate exceeding 99.5% and processing speeds of about 5 ms per component, this system enables manufacturers to identify and address defects promptly, thereby significantly enhancing manufacturing quality and reliability. The implementation leverages big data analytics, analyzing over a billion components to refine detection algorithms and ensure robust performance. By pre-empting and resolving defects before they escalate, the methodology minimizes production disruptions and fosters a more efficient workflow, ultimately resulting in considerable cost reductions. This paper showcases multiple case studies of component defects, highlighting the diverse types of defects identified through AI and deep learning. These examples, combined with detailed performance metrics, provide insights into optimizing electronic component assembly processes, contributing to elevated production efficiency and quality.

show abstract

Detecting Corrosion to Prevent Cracks in MLCCs with AI

Cited by 3 publications

References 28 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

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

Advancements in Electronic Component Assembly: Real-Time AI-Driven Inspection Techniques

Contact Info

Product

Resources

About