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Benefits of artificial intelligence, machine learning

Artificial intelligence and machine learning technologies will be helpful for quality control and process optimization

May 19, 2023  By Angus Pady

Artificial intelligence and machine learning can be useful for quality control, automation, and optimization purposes. Photo © By Kaikoro / Adobe Stock

Our industry has been slow in adopting artificial intelligence (AI) and machine learning (ML), but they are starting to make some inroads. The packaging sector can benefit the most from this technology especially in the areas of quality control, automation, and optimization. Below are some of the areas where we will see early adoption.

Error detection

AI-powered cameras and machine learning algorithms can be trained to identify and detect errors such as misaligned images and incorrect colours in the printing process. This can be done in real-time, allowing for immediate correction and minimizing the need for manual inspection.


Quality prediction

Machine learning algorithms can be trained to predict the quality of the print based on various factors such as press settings, ink and paper characteristics, and other parameters.

Quality monitoring

AI and ML can be used to constantly monitor the print quality throughout the run. This can involve capturing images of the print and running them through machine learning algorithms in order to identify and correct errors as they occur.

Deep learning, specifically computer vision and natural language processing, can be designed to identify defects during the product packaging process. These deep learning models can verify that a label on a package is present, correct, straight, and readable.

Quality improvement

Machine learning algorithms can also be used to learn from past print runs and find ways to improve quality. For example, analyzing data from past print runs to identify the press settings and other factors that lead to the best print quality, and using these settings for future print runs.

Multidimensional quality control

Machine learning algorithms can be trained to analyze data from multiple sources such as visual, spectral, and mechanical data to create a multidimensional view of print quality and identify errors and issues that may be missed with traditional methods.

AI package structural creation

Designers use AI-enabled virtual reality environments to design packaging. In some cases, you can see how the package will look when placed on a shelf in a particular store. What will it look like behind glass in the freezer section and under various lighting conditions? How will it look next to other products? All of these can be tested before actual design.

Predictive maintenance

In AI and machine learning, predictive maintenance refers to the ability to use volumes of data to anticipate and address potential issues before they lead to breakdowns in operations, processes, services, or systems. 

AI-driven nesting

The ability to optimize how images are distributed on a page is already being used on various cutting tables and RIPs to minimize material waste.

Custom box creation and personalized printing

We can set up a system that looks at all outgoing shipments and categorizes the packages into groups. The groups then get sorted and placed together, a corrugated feeder matches up the packed groups with box sizes, boxes are automatically created to the exact size required and the packages are then fed into the production line. At the end of the line, the boxes are printed with a personalized message and shipping instructions.

AI-driven inspection

This is, by far, the most advanced use of artificial intelligence in our industry. AI-driven inspection systems can not only see defects faster than any human, but also learn. Computer vision modelling is incredibly adept at learning what an acceptable product/package should look like as it moves down the line. A properly trained model should easily detect wrinkles, rips, tears, warpage, bubbles, colour, and printing errors. The challenge at this stage is that these types of systems require significant amounts of CPU power and the sheer amount of data science knowledge it takes to train, deploy, and monitor a suite of deep learning models running in production is difficult to deploy correctly without outside support.

Adobe Sensei

If you have used Photoshop lately, you may have seen some of the AI-powered tools like Content-Aware Fill. Did you know Adobe Sensei has AI tools that can use your customer data for unique insights about individual consumers? You can use your data assets for predictions.

We are entering a new era where machines will allow us to do our jobs quicker and more efficiently. At the same time, technology will challenge some of our deep-seated beliefs. Embedding AI into your company’s operations, much less ensuring business impact, isn’t going to happen overnight. Organizations will need a short-term strategy for delivering quick, high-impact AI wins and a long-term strategy enabling a progressive artificial intelligence culture.  

Angus Pady is a G7-certified expert that has helped customers resolve colour management challenges for over 30 years. He can be reached at

An edited version of this article originally appeared in the March/April 2023 issue of PrintAction.

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