The central element of automated optical inspection is the software algorithm that analyzes the images captured by the optical system.
It is the brain that processes the visual data and draws conclusions about the presence of defects.
- Pattern-matching algorithms compare the captured images to a predetermined "good" sample. Where there are differences, statistical pattern matching comes to the rescue. This algorithm determines the acceptable variation in the appearance of a component by examining a series of good samples to determine the statistical norm. It takes into account natural variations and detects only true anomalies.
- Feature-based algorithms categorize an image into key features and characteristics, making decisions based on the geometry, texture, and intensity of these features.
- Machine learning algorithms are trained on an image dataset. They improve over time and identify defects and tolerances with greater accuracy.
AI and machine learning algorithms are becoming increasingly autonomous and will be able to more accurately identify patterns and anomalies with minimal human intervention. Expanding data analytics in AOI systems is another area for development. With more data, systems will be able to perform more comprehensive analysis, leading to a deeper understanding of the manufacturing process.
- Pattern-matching algorithms compare the captured images to a predetermined "good" sample. Where there are differences, statistical pattern matching comes to the rescue. This algorithm determines the acceptable variation in the appearance of a component by examining a series of good samples to determine the statistical norm. It takes into account natural variations and detects only true anomalies.
- Feature-based algorithms categorize an image into key features and characteristics, making decisions based on the geometry, texture, and intensity of these features.
- Machine learning algorithms are trained on an image dataset. They improve over time and identify defects and tolerances with greater accuracy.
AI and machine learning algorithms are becoming increasingly autonomous and will be able to more accurately identify patterns and anomalies with minimal human intervention. Expanding data analytics in AOI systems is another area for development. With more data, systems will be able to perform more comprehensive analysis, leading to a deeper understanding of the manufacturing process.