We all know that three-dimensional laser profile is widely used in daily life, and it is used in many factories. So do you know which band of laser diode is mainly used? Let's take a look at it together.

I have been doing three-dimensional laser profile detection for so many years. To be honest, I have been exposed to laser diodes of different bands. Many people ask me as soon as they come up: What kind of band of laser is used for 3D laser profile? In fact, there is no absolute answer to this question. It depends on what you want to measure, under what environment, and how high the accuracy is.

Generally speaking, the most common three-dimensional laser profilometers on the market mostly use laser diodes in red band, such as 635nm and 650nm. One advantage of red light is that it has strong visibility, and the naked eye can see the light, which is much more convenient for installation and debugging. In the past, when we were doing an automobile parts inspection line, the customer asked to monitor the contour data in real time, and we used a 650nm red laser module. When debugging, you can directly see the red line light sweeping across the metal surface, and you don't need to turn on an external monitor to find the light spot, which saves a lot of trouble. The red light is stable and the price is suitable, so it is almost standard in industrial testing.

However, not all scenes are suitable for red light. Sometimes you may find that when red light is measured on plastic parts or black rubber, the signal will jump. In that case, we will switch to near-infrared band, such as 780nm, 850nm or even 905nm laser diode. Infrared light is invisible, and the interference to ambient light is much less, especially in the strong light workshop, the signal-to-noise ratio of infrared measurement is obviously higher.

Which band of laser diode does the three-dimensional laser profiler use? It depends on how to use it. Red light, infrared light and blue light are all capable, but people who really use it well will never just look at the data, but know how to adjust, try and grind in the actual environment.