Adoption of Cost-Effective Autonomous Machine Vision to Improve Packaging Process
Published on : Friday 21-08-2020
According to the World Allergy Organization (WAO), 2.5% of the general population is allergic to one of more foods, while up to 10% can manifest adverse drug reactions. This is just one example of why in highly regulated industries, such as food or pharmaceutical manufacturing, it is particularly important to protect consumers by ensuring that items are properly packaged and labeled.
Packaging and labels contain key information on food products and drugs. A minor mistake on either could have serious repercussions that impact a product’s safety, regulatory compliance, and consumers’ satisfaction.
Errors in packaging are often the cause of huge recalls — the wrong label on the wrong product, for example, could lead to food containing ingredients that it shouldn’t, with dangerous consequences for consumers that are allergic. Other risks linked to incorrect packaging and labeling are premature spoiling, contamination with externals pollutants, and changes in the product’s taste and color.
For these reasons, quality assurance (QA) must be an integral part of the packaging process. Yet the huge amount of stock-keeping units (SKUs) and different labeling concepts, make quality assurance in packaging a task even more challenging than in other sectors.
Machine vision solutions can automate QA procedures, but their inherent complexity, coupled with the high number of SKUs and inspection scenarios, often prevents small and medium packaging plants from implementing them.
Worsening the problem, companies usually need the assistance of a systems integrator or machine vision expert to build and set up the solution. For many SMEs, the cost of these services, together with the initial investment in a traditional machine vision solution, is far too high.
As a consequence, many small to medium-sized packaging manufacturers rely on manual inspection, a method that carries an error rate of more than 25%. In the context of Industry 4.0, this is an anachronistic approach that cannot be relied on.
Autonomous Machine Vision for QA and traceability
The introduction of autonomous machine vision (AMV) gives manufacturers of every size peace of mind that their products are packaged and labeled correctly. AMV constitutes a whole new category in the world of visual QA since it provides users with universal inspection products that are completely autonomous such as self-setting, self-learning, and self-adjusting. Consequently, the plant’s personnel will be able to independently set up and operate the system, which comes ready to use, out of the box, like a laptop or a smartphone.
Various AMV systems on the market, advanced machines have a number of characteristics that differentiate it from traditional QA solutions. Initially, traditional solutions are tailor-made to inspect only one product at a specific junction of a production line and are designed and tested by a systems integrator to recognize all possible defects in a product.
It is expected that the exposing QA solutions to thousands of defective items in packaging, until it memorizes every potential production mistake, from missing labels to misspelling, to unsealed or partially sealed packaging. This process of developing and training can take several weeks, causing extended periods of downtime. In short, autonomous machine vision systems invert the parameters of traditional machine vision for QA, instead of learning what every single defect looks like, they learn what a perfect product looks like and flag up abnormalities.