Machine Vision Promises Nearly Flawless Quality Control
Robots are capable of incredible feats well beyond human abilities. Strength has long made robots a staple of assembly lines, where their immense power transcends human limitations. But companies are discovering that robots can be leveraged not just to overcome human physical limitations, but mental limitations as well.
Human Mistakes Happen
A prime example of this capacity for robotics to overcome human limitations is quality control, where mistakes can lead to returned products, rush orders and even damaged reputations. Humans have been traditionally tasked with quality control because it requires judgment. Is this bottle filled with enough water to satisfy customers as being “full?” Is it overfilled to the point that it may compromise the capping process? A human can easily make that determination, even as a bottle rapidly moves through a plant.
But an interesting thing happens when a human views not one bottle but hundreds of bottles, let alone thousands of bottles streaming along a high-speed bottling line. After repeatedly seeing an image, that image gets imprinted on the brain. So when an inspector sees a number of bottles at the proper fill level and then sees a bottle that’s half-empty, the inspector’s eyes send that signal to the brain — but the brain may instead use the imprinted image of a full bottle and not register a problem.
Machine vision can be used to determine when a bottle is too full or not full enough (both inadequacies indicated by a red rectangle in the above images).
Machine vision, where a camera system captures images and software analyzes them, overcomes this human limitation. A human being is simply not as ideally suited for repetitive tasks as a machine, and manufacturers have taken notice. For example, Heineken now uses R&D Vision’s machine vision system at a beer-bottling facility in France, where it inspects 80,000 bottles per hour and practically achieves a 0% failure rate. Machine vision can also be used to inspect everything from the threads of a pipe to product surface defects to component alignment.
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