Machine vision in production

As it’s always advisable to start from the beginning, let’s start with the very definition of machine vision, which is simply the ability given to machines to see and analyze images. These systems mimic human vision and allow computers to recognize shapes, colors, sizes, and all the attributes we can capture graphically.

Among its countless applications across all sectors of activity, it is extremely useful in industrial production processes, which is what we’ll focus on here. Examples of task automation using machine vision include:

• identifying defective products
• guiding robot actions
• sorting products by size, color, or material
• and reading information from various types of codes, among others.

Tools and actions

Carrying out these tasks can involve anything from simple sensors to advanced cameras, depending on the process assigned to the system. In the latter, more complex case, once the images are captured, advanced algorithms break them down into pixels that go through different layers of analysis in search of preset patterns. This way, as already mentioned, anomalies in products can be identified as they pass along a conveyor belt, by detecting elements that the system doesn’t recognize as part of the product.

With the information obtained, two types of actions can be carried out: an informative one, which alerts teams to the detection of an inconsistency in the process so they can act accordingly, and an interactive one, which triggers a reaction from the machines themselves in response to the anomaly detected. If an item that isn’t in optimal condition is detected on a product selection line, it’s automatically removed from the line.

Advantages of machine vision

The advantages that machine vision offers in production processes are numerous, just as its possible applications are numerous. Let’s look at a few:

• Objectivity: inspection or selection processes are given total objectivity. The criteria for product validity or classification are always the same—those preset in the system beforehand—minimizing margins of error in the process.

• Consistency: following on from the previous point, this results in consistent products that meet set standards, which translates into end-consumer satisfaction, as their expectations will always be met.

• Processing speed: machine vision systems can analyze multiple product units or read codes in fractions of a second. Procedures become much faster and more agile.

• Cost savings: On one hand, no material is wasted on defective manufactured products, since once detected, they’re removed and don’t continue down the production line. On the other, the speed of analysis allows more items to be worked on simultaneously, increasing the productivity ratio.

• Improved quality: by detecting deviations from set standards based on objective criteria, the quality of the final product increases, minimizing failures in the process.

As we can see, the use of artificial intelligence, specifically machine vision, significantly improves production processes, which now bear little resemblance to those used ten years ago. Factories, warehouses, and companies in general are becoming automated, saving time and costs while improving product quality. This increases both productivity and profitability, making it possible to offer higher-quality products at lower prices.