Electronics

MLCC Inspection

Increase MLCC automatic inspection rates while reducing overkill

MLCC Inspection

相關產品

In-Sight D900

In-Sight D900

採用 In-Sight ViDi 深度學習視覺軟體

ViDi software with all defect detection tools

VisionPro Deep Learning

A breakthrough in complex inspection, part location, classification, and OCR

A multi-layer ceramic capacitor (MLCC) consists of a block of stacked capacitors with metallized terminals for connection to integrated circuit boards. MLCCs suffer from a variety of possible manufacturing defects, including cracks, blisters, chips, contamination, and voids in the termination coating. These capacitors store significant energy, so failures affect not only the defective MLCC but can damage adjacent components or the integrated circuit board itself.

MLCCs are small and come in large numbers. They can have a variety of subtle defects that vary widely in appearance and location. In addition, they have shiny surfaces that limit the effectiveness of traditional machine vision.

As a result, manual inspection still plays a significant role. An automated optical inspection (AOI) machine inspects all six sides of all capacitors, followed by humans inspecting one side of a statistical sampling of the capacitors. But AOI machines have a high overkill rate, while manual inspection is far too slow for general use. The overall process is expensive, slow, error-prone, and does not produce useful data that could help in process improvement.

Get Pricing

Cognex has built a cosmetic optical inspection (COI) machine specifically for MLCC inspection, which combines both customized lighting and deep learning vision tools. First, a lighting module customized for MLCC inspection minimizes irrelevant surface variations while revealing otherwise easily missed defects on both the capacitor body and terminals.

After the MLCCs have been inspected by the AOI machine, they are then inspected by the COI machine in order to reduce the number of false positives and good parts being pulled out of production. This machine delivers better speed, accuracy, and process improvement data when compared to manual inspection.

Cognex Deep Learning’s classification tool is trained on labeled images of a wide variety of both defect-free and defective MLCCs. The classification tool learns to categorize the wide range of possible defects, as well as learning the full variation of normal parts. Once trained, it can scan through every MLCC part, and instantly flag any outside of the acceptable range or identify good parts that were previously flagged as defects.

The categorized defects can also be used for upstream process control to minimize part defects over time.

精選康耐產品

取得產品支援與訓練等等

加入 MyCognex

是否有任何疑问?

世界各地的康耐视代表可以随时为您提供支持,满足您的视觉和工业读码需求。

聯絡我們
Loading...