As the requirements for printing quality and consistency are constantly increasing, inspection systems are no longer considered a luxury but a necessity for staying competitive as a business.
More companies realize the need to implement defect detection systems to be able to accomplish complex printing tasks quickly and accurately.
Printing production services are not limited to printing itself – they also deal with systems, such as serialization, individual labeling for identification, and tracking. It calls for a comprehensive solution capable of working with all kinds of printing elements (labels, boxes, flyers, etc.) and suitable for implementation in multiple industries (medical, pharmaceutical, chemical, food, consumer goods). Our automated detection technology can fulfill these needs.
The printing process and finished products are always subject to certain limitations. In this context, customers are looking for an appropriate balance of reasonable costs and printing requirements (quality, time limits, among other factors). However, these tasks engage many people and even organizations, and constant interactions between multiple participants inevitably affect the results. This combination of circumstances leads to occasional printing errors.
Also, the challenge is keeping the quality as high as possible while contributing to the financial goals of the business. Companies strive towards speeding up capital turnover, reduced production costs, and increased productivity. From a business standpoint, quick and efficient operations result in higher capitalization.
However, goals for financial gain must not be placed above the quality of the product, and all deviations should be promptly identified. To address both concerns, businesses can utilize cutting-edge technologies and meet high standards of printing quality without it negatively affecting their expense management.
As for categorizing why these errors occur, there could be a variety of different reasons, such as unfocused or simply tired operators.
Also, there are different sources of errors, large printing volumes, and general wear and tear of equipment that contribute to the list of challenges. Taking all these additional external factors into consideration, there is a need to significantly limit human involvement in printing quality control processes, which could be done by automation. This way, automation replaces a person that has a limited resource for various reasons with an unlimited resource of the same (if not better) quality.
Any efforts to prevent failures in printing require an understanding of where you are most likely to find an issue. The common errors found during print inspections in packaging elements and labels are:
Detection systems can be classified based on two main areas of work they can focus on:
Some errors are impossible to compensate for, such as inconsistencies in design elements. In such cases, the inspection system can only detect and display errors. But then it must be reviewed by a live operator for subsequent action.
However, the majority of errors are suitable for compensation carried out by automated algorithms. These algorithms can be put into operation using AI for both error identification and compensation, which will ensure high accuracy and rate of operation. Built-in OpenCV (Open Source Computer Vision Library) capabilities allow for better results within a short time.
Based on the above analysis, we came up with an algorithm that will partly or fully substitute traditional quality control departments. Instead of manual or semi-automatic detection, it will process defects in a fully automated manner. It revolves around a key feature of modern printing, which is working with original design templates (a perfectly aligned large image).
Printed versions can differ from the master file by color proofing, control points, lines or cuts, and variable fields (expiration date, batch number), etc. To detect these cases and find a match between a master file and a printed copy, we use the following methods:
The master file consists of only one image, but there could hundreds printed copies all having fine detailing that is too fine to be seen with the naked eye. On the contrary, you can have multiple copies that should come together as one large cohesive image. In any case, it would take too much time and effort to match copies to the master file without a straightforward and accurate automated system.
To find accurate matches with the original design template, our solution operates based on the following algorithm:
A model project for quality control of print, labeling, and packaging requires a master file and a printed sample. These components are analyzed using the Master as a baseline for the rest of the job. The analysis is done for:
We can adjust the algorithm to meet specific requirements for the quality of work and speed of fulfillment. For example, the processing time may involve limits like under 10 seconds or a defined margin of error like 0-3px. The algorithm can even be pushed further to tighten the time constraints or move the acceptable shifts to 0-1px.
Our solution also takes into account particularly challenging tasks of defect detection on different surfaces. Some elements of design, such as brand logos or product descriptions, may not look identical depending on the surface material. The samples can be printed on various kinds of paper, cardboard, film, plastic, or metal. So, the technology accommodates peculiarities and limitations of the printing processes in general and a given surface.
Manual print quality inspection inevitably falls short of what is required for capturing defects. The shortcomings translate into lower revenues, reduced profitability, and deterioration in the quality of final products. Automated platforms help companies prevent that from happening.
Algorithms for detecting and compensating printing errors are at the core of automated proofreading platforms, which are simply indispensable for modern printing production. The technology has many applications and works for print, labels, and packaging inspection. Ultimately, the solution gets rid of pressure placed on operators and takes on the task of ensuring automatic detection with impeccable accuracy.
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