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How AI and Machine Learning Is Helping in Quality Management of Electronic Components

How AI and Machine Learning Is Helping in Quality Management of Electronic Components

The quality management of electronic components is a difficult task when it comes to the sheer number of products that are manufactured. It’s hard for manufacturers to keep up with all the different specifications and regulations governing each product, as well as any recalls or updates that might be necessary. 

Artificial Intelligence (AI) and Machine Learning (ML) can help manage this process by automating tasks like identifying defects in a product, understanding customer feedback, and even predicting which parts will need updating next. This helps manufacturers avoid costly mistakes while also providing customers with high-quality goods they expect from their favorite brands.

Machine Learning and Image recognition

Before the advent of machine-learning algorithms, computers were just brute force machines with no understanding. Now they can learn from their mistakes and pinpoint exactly where a problem is to make accurate decisions every time! 

When developing an ML algorithm for any industry, you need three things: robust algorithms that are fast enough to accommodate data updates; large datasets which allow your model to recognize patterns between different sets of information; as well as specific domains or industries because it’s only when we have clear goals that our systems will be able to optimize processes efficiently.

Machine-Learning (ML) Algorithms use massive amounts of data from a specific domain like finance or healthcare so these fields can become more efficient by optimizing processes through recognition and correlations connecting vast quantities

The more examples of the correct pictures that an ML-based system is exposed to, the more accurately it can pick up patterns and identify differences in patterns. This helps with image recognition so as not to only recognize if a picture belongs but what category or classification it falls into.

Machine Learning algorithms help to detect defects on circuit boards

In the past, visual inspection of PCBs was done manually. With recent advancements in deep learning and computer vision algorithms, however, AOI machines can perform automated inspections with greater accuracy than ever before. This is particularly true for defects that can’t be found by a human inspector – such as scratches or tiny holes too small to see without magnification- making it an invaluable tool for any manufacturer looking to improve quality control while also reducing costs on labor hours spent inspecting boards after they’ve been finished.

Researchers from Yuan Ze University in Taiwan have successfully achieved a 98.79% accuracy of detecting defects on PCBs using you-only-look-once (YOLO) convolutional neural networks¹, which is the simple and unified object detection model that can be trained directly with full images as opposed to other systems that require more complex training methods for testing. Fast YOLO has been found to provide both accurate detections and high speeds across various datasets while providing an excellent tradeoff between speed and accuracy when compared against other models like it during real-time testing.

Fraud detection, counterfeit products, and component performance

The OECD has reported that counterfeit and pirated goods account for 3.3% of global trade volumes every year, while the United States is heavily affected by these products with 24%. This percentage continues to rise as more fakes are seized internationally from counterfeiting countries like China in 2019 alone.² 

In this case, especially, electronics is a major industry due to ongoing price increases on critical components which allows fake products such as imitation Apple chargers or Samsung phones to enter the market until they can be distinguished easily through various methods including serial numbers found at their manufacturing plants.

Counterfeit detection on finished goods can be achieved by visual inspection, but it would take a well-trained eye and knowledge to distinguish between an authentic product. 

There is no need for this when using ML algorithms that analyze millions of images in seconds to pinpoint even the smallest inconsistency or anomaly. When dealing with electronic components, however, just visual inspection may not suffice as there are counterfeit units out there lying dormant waiting for you! 

The prospect of AI and ML in the electronic component supply chain

We’ve all seen the effects of a global supply chain disruption by the pandemic. As part of this, manufacturers are not always able to find what they need when it comes time for production and there is an increased risk that counterfeit parts will be used in these goods as well. 

To avoid such occurrences, we can use ML technologies with image recognition which continually improve both accuracy and speed – making quality control much easier than ever before! And because cloud computing requires less training data on physical hardware systems that may have been difficult or impossible to acquire. Due to several issues, more industries are expected to turn towards using them instead as it guarantees effective quality management.


  1. Defect Detection in Printed Circuit Boards Using You-Only-Look-Once Convolutional Neural Networks.
  2. OECD/EUIPO (2019), Trends in Trade in Counterfeit and Pirated Goods, Illicit Trade, OECD Publishing, Paris,
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