How to prevent the quality problems of glass wine bottles

How to prevent the quality problems of glass wine bottles

Glass bottles are traditional beverage packaging containers in China. In the case of a variety of packaging materials flooding into the market, glass containers still occupy an important position in beverage packaging, which is inseparable from its irreplaceable packaging characteristics. So how do you tell whether a glass bottle is good or bad?

In the process of wine brewing, there are many factors that affect the quality of products, such as raw material quality, technology and brewing technology. In addition, bottle defects (bottle period, bottle body and bottle bottom) and dirt are also key factors affecting wine quality. Therefore, empty glass bottles must be tested before filling wine, and unqualified products must be eliminated before filling wine. At present, most wineries in China still use manual inspection of the quality of empty glass bottles, which is inefficient, slow and labor-intensive. Machine vision is very suitable for measurement, inspection and identification in mass production, and the application of linear CCD in continuous and scanning online measurement has great advantages. Using machine vision detection method can greatly improve the automation of production, and machine vision is easy to realize information integration, which can greatly improve product quality and production efficiency.

Therefore, aiming at the repetitive work of glass bottle inspection, it is of great significance to apply machine vision theory to develop a set of glass bottle inspection device to realize automatic inspection of empty bottles. Machine vision is to measure and judge with machines instead of human eyes. Machine vision system means that the captured objects are converted into image signals by machine vision products (that is, image pickup devices, which are divided into CMOS and CCD), which are transmitted to a special image processing system and converted into digital signals according to information such as pixel distribution, brightness and color. The image system performs various operations on these signals to extract the target, and then controls the action of the field equipment according to the discrimination result.