LEUZE
BAR CODE IDENTIFICATION WITH THE BEST RECOMMENDATIONS FROM AI Using Artificial Intelligence (AI) can be very worthwhile when it comes to identifying the bar codes on goods. Interfering factors can be identified quickly and easily both during commissioning of a system and during operation.
No need for time-consuming searches Bar code readers are sensors used to identify goods and materials in production or logistics. They do so by detecting bar codes that meet one of a number of standards and then supplying the IDs of the bar codes to a superior system. When using these devices in automated applications, the main objective is to achieve the highest possible reading quality: Essentially, when bar code readers detect the labels, the quality with which they perform this task varies, and this quality can be indicated as a percentage. The percentage relates to the contrast detected. If the value is below a certain threshold, the label is no longer read. One challenge faced by system operators is to find bar code readers as quickly as possible when they are no longer providing sufficient reading quality, and to determine the reasons for this – without additional data regarding the possible sources of error, this can be a time-consuming task. Particularly in large systems, for example in intralogistics, that have up to 1,000 bar code readers 05 - Electronics Journal - JULY 2022
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and kilometer-long transport routes, the search is like looking for a needle in haystack: If in doubt, a technician must trace the entire route of a transport material in order to identify a poorly aligned sensor or the interfering factors in its direct environment, all while under time pressure. The situation is made worse by borderline cases, such as when the bar code reader is somewhat aligned and reads successfully most of the time, but occasionally does not detect labels. This may be because the bar code reader is slightly inclined or only reads in the border area, or other factors may play a role, for example labels of insufficient quality. Factors that influence reading quality However, generating corresponding data to find the causes of errors using the bar code reader itself is only possible under certain circumstances. It is true that the sensors monitor their own status and transfer data to the superior system via OPC UA if required. However, this self-monitoring has only very limited
functionality – a sensor only considers its own view. This means that it sends information such as “I’m currently reading,” “Excellent reading,” or “Very poor reading” – i.e. its calculated percentage reading quality. The reason for the poor reading quality cannot be identified by the individual device. There are three possible influential factors in this case: The device itself, the bar code label and interfering factors in the environment. Possible sources of error relating to the bar code reader itself include poor alignment to the labels to be detected or a technical fault. In turn, labels can be damaged, soiled or poorly printed, which, depending on the degree of damage or printing quality, may only reduce the reading quality or may prevent identification entirely. Interfering factors in the environment include vibrations, dust, and glare caused by sunlight or emitters in the background. Humidity, for example in cold stores, can be an interfering factor if this causes fogging on the scanning window of the bar code reader.
