Surface defects can be defined by the change in surface property. For example, surface defects such as dents can be seen as a change in surface inclination, scratch as a change in surface roughness while stain can be seen as a change in surface reflective.
But until now, conventional lighting products have been used to detect surface defects only by utilizing brightness values, which has caused poor and unstable results. Furthermore, if a surface had changes in reflective and geometry, this usually resulted in crucial information loss.
To overcome these obstacles, we have developed a lighting system that directly measures 3 surface properties (inclination, roughness and reflective) by combining multiple images and outputting it as processed images.
These high-quality images can be used to detect complex defects regardless of sample & lighting conditions. In addition, classifying defects can also be achieved by a combination of the processed images. These high-quality images can especially be a good fit when combined with deep learning (AI) solutions.
Engineers usually depend on their knowledge and experience when choosing the correct lighting but if unsuccessful, this can lead to an extensive and time-consuming process. This is because there are hundreds of different lightings that exist in the market and most applications require a different lighting depending on its surface property and geometry.
This is the main reason why many engineers still spend a lot of time selecting the correct lighting for different types of applications (e.g. lighting type, size, color, gradation, etc.).
The free-illumination mode is an all-in-one solution that can replicate most lighting products out in the market, just by using one lighting system. This has been made possible by using a proprietary lighting system where hundreds of LEDs are independently controllable.
Every single LED’s brightness and color can be controlled, meaning you can now draw & replicate all sorts of lighting conditions according to your needs. This will benefit engineers to a great extent when selecting correct lightings and can also simplify machine vision systems that use multiple lightings for inspection.
Surface defects can be defined by the change in surface property. For example, surface defects such as dents can be seen as a change in surface inclination, scratch as a change in surface roughness while stain can be seen as a change in surface reflectivity. But until now, conventional lighting products have been used to detect surface defects only by utilizing brightness values, which has caused poor and unstable results. Furthermore, if a surface had changes in reflectance and geometry, this usually resulted in crucial information loss.
To overcome these obstacles, we have developed a lighting system that directly measures 3 surface properties (inclination, roughness and reflectivity) by combining multiple images and outputting it as processed images. These high-quality images can be used to detect complex defects regardless of sample & lighting conditions. In addition, classifying defects can also be achieved by a combination of the processed images. These high-quality images can especially be a good fit when combined with deep learning (AI) solutions.