Is a Photoelectric Sensor Best for Your Label Detection Application?

Label detection is in every industry of manufacturing. Detecting the placement, orientation, color, or size of a label is critical to product quality and it can even be required for safety purposes. Contrary to what some believe, not every label detection application requires an expensive vision system. This article reviews some common applications that can be solved with just a photoelectric sensor.

As with any sensor application, it’s necessary to specifically tailor the systems for individual applications. In label detection, the label properties dictate the necessary sensor for the job. Using the right sensor will ensure accuracy to the manufacturing process by limiting the possibility of errors or misreads when placing or cutting off the label. The chance for mistakes decreases when labels are designed to have markings used as a point of reference for the sensor to recognize, telling the PLC programming that it is time to cut off or place the label. When looking into a label detection application, several photoelectric sensor types are available.

Through-beam fork sensors

A through-beam fork sensor has an emitter and a receiver built into the same housing which provides a consistent light beam that is simple to configure to many applications. For label detection, fork sensors have teach-in buttons to set the target and background so unique markings can trigger the sensor. This helps find an identification marker that can identify where to cut the label. For applications with consistent markings on different labels, the sensor would not need to be retaught if the color of the identification mark and the background are the same. The common use for these sensors is on flexible manufacturing lines because operators can reteach the sensor to recognize a new label with a different style and color in less than 60 seconds.

Contrast sensors

Providing a high level of accuracy to find labels in an assortment of products, contrast sensors can be taught to identify a target on many different material types, providing an advantage when working with three-dimensional objects. They provide background suppression, allowing for applications using transparent objects, such as glass and plastic and work by distinguishing between objects based on their gray values. This means contrast sensors are highly accurate when detecting objects with similar colors.  

Color sensors

Color sensors are a fantastic choice when working with labels with many different colors. A traditional color sensor can be taught to up to 7 different color parameters to distinguish one label type from the others. Manufacturers with multiple production lines that have labels with various colors can use just one color sensor to detect them all. The more advanced IO-Link-capable color sensors provide an abundance of opportunities to configure many different label types. Using color detection software, one color sensor can be taught up to 256 different color parameters. Users can configure each color setting for the label’s colors and the background.

When it comes to selecting the right sensor for your label detection, you have options. You need to consider the specifics of your application and choose the solution that ensures accuracy and quality during the manufacturing process. For more information about the Balluff photoelectric sensors, visit https://www.balluff.com/en-us/products/areas/A0001/groups/G0103/products/F01325?page=1&perPage=10&searchTerm=.

Basic Color Sensor Overview

PrintIn the past, color sensors emitted light using red, green and blue LEDs’. The sensors were then able to distinguish colors using the RGB components of the reflected light back to the sensor’s receiver. As technology has progressed true color sensors have been developed that not only can compare colors but measure them more accurately than the human eye.

Color sensors are based on diffuse technology and can be compared to a fixed focus or convergent sensor because of the focused light spot. Unlike color contrast sensors that only detect the difference between two colors based on brightness, color sensors can detect a wide range of colors.

cielabTrue color sensors typically use white LED’s which allow for a greater color spectrum evaluation. Combine this with the CIELAB color system which is one of the most versatile color systems and the result is a color sensor that equals or exceeds the human eye. The CIELAB color system is a three-dimensional independent infinite representation of colors. The L component for lightness and a and b components for color are predefined absolute values. Lightness varies from black (0) to the brightest white (100). Color channel a varies from green negative 100 to red positive 100. Color channel b varies from blue negative 100 to yellow positive 100 with gray values at a=0 and b=0.

Due to the technology, color sensors can check only a small spot of color but can check this spot amazingly fast – up to 1.5 kHz in case of the Balluff’s fiber optic BFS 33M which also has a range of 400mm. Unlike a color sensor camera, which will focus on the object’s surface pattern and may cause false readings the true color sensor will ignore patterns thus providing more accurate color detection. In addition the true color sensor will have more outputs than the color camera.

Smart color cameras are working with RGB but could work also with HSV color models. They could be used to check larger areas for the same color or color codes on a part, but have slower update rate of 50 Hz. Special cameras for faster applications are available in the market but at higher costs. It is important that the light source for the smart color cameras be a white light with a standardized white balance, and that this light must kept constant for all checks to avoid errors.

The sophistication on the front end of the color sensor can be much more advanced and still remain a cost effective option for industrial use due to the fact that a camera requires a much larger processing system. The more sophisticated the sensors are in the camera the more robust the processor must be in order to process or map the data into an image.

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