The Machine is Quicker than the Eye.
The human eye is a wonderfully complex instrument. Yet it also has its limitations. When inspecting products, for instance, Machine Vision is not only faster, but also far more accurate. Working in combination with the brain, it allows us to perceive our surroundings. We can recognize objects in a split second, even when their exact shape varies. We use perspective to analyze our environment and have a wide field of vision capable of focusing very sharply on particular areas.
These abilities have gradually evolved over millennia as humans continually adapted to many different stimuli and environments in order to survive. Our visual perception also has important limitations. For starters, we only have two eyes. They are too slow to see fast-moving objects in detail and they are sensitive to only a limited portion of the light spectrum. Glare and reflection also impede our ability to focus on certain properties of an object, such as size or color, for a long time.
In addition, we are quite subjective in how we perceive and store images. No wonder, then, that the human eye cannot perform accurate measurements and is therefore not the ideal instrument to verify product quality.
Machine Vision: More Reliable and More Accurate Than the Eye
Machine Vision, or imaging-based automatic inspection and analysis, has everything it takes to surpass the human eye when it comes to accurate and reliable product inspection. With competition growing in every field, vision inspection has become a key differentiator for companies to remain competitive. Products need to evolve with consumers’ needs and today that means they must be smaller, faster and more complex.
Customers shop differently than before. Regardless of whether it’s B2B or B2C people can easily browse and shop from anywhere in the world. Cost & quality are the main drivers to someone towards a choice and both need to exceed expectations. When dealing with the precision and accuracy required to ensure a product properly represents a brand, manual inspection often doesn’t cut it anymore. It is expensive, inconsistent, and unreliable. Individual components can be too small to inspect with the human eye alone and a microscope is very time consuming to focus and refocus for each part. For example, the exact measurement of the depth of a needle can be imperceptible, as can dust or scratches or any other small defect that may be present on an item. Additionally, manual inspection is at risk to the subjectivity of the operator on duty. One opinion may differ from the next.
The power a defect will have on a product or on a company’s reputation can be substantial. Take a pacemaker, for example, the intention is that this product will live inside a person’s body for the rest of their life. If there is a scratch, or if 2 components were not bonded together properly, time will make it worse. As years pass, defects grow. What was once small and barely noticeable is now dangerous and being counted on. Another famous example involves a high-profile consumer electronics manufacturer. The newest release of their popular cellphone line contained flaws that allowed the devices to overheat and, in several cases, catch fire. Thousands of phones had to be returned to the manufacturer and the impact on the profitability of the newly released product was immediate. Needless to say, better solutions than manual inspection are available. As such, many manufacturers have implemented vision inspection into their manufacturing process.
Today, vision is commonly used to accelerate several applications and provide fast, reliable, accurate and repeatable results.
These applications include:
1. Feature & Defect Detection
Vision can find any scratch, crack or blister on the majority of surfaces and prevent them from getting out to the field. This defect detection can apply to different scenarios, volumes, and sizes.
Example 1: Products moving at a rate of 20 per second must be thoroughly inspected. The goal is to detect errors with an accuracy of 0.02 square millimeters.
Given the fast pace and the need for long-term reliability, visual inspection with the naked eye is not an option in this scenario. If attempted, nonetheless, such an experiment would involve a whole team of people, which would go against the objectivity of the inspection. Machine Vision is the solution: six cameras observe the fast-moving products using very short shutter speeds and brief and polarized light exposure (strobe). This creates sharp images on which defects are perfectly visible. Special software then searches all defects within 50 milliseconds, and it can be done 24 hours a day (by using a real-time operating system or FPGA). The result: a system that is superior in every respect to human inspection.
Example 2: Defects of a few microns (μm) must be detected in an area of 20 millimeters (mm) on objects that pass by at a speed of 5 meters (m) per second.
If inspection with the human eye was used in this instance, it would require a single person capable of seeing defects of a few microns on a 2m surface, while the product moves by at a speed of 18 km/h (i.e., one every second). So, here too, inspection with the naked eye is not realistic. The only option is a combination of high-tech Machine Vision such as 8k line-scan technology combined with fast lenses, LED line lights and super-high-speed, “on-the-fly” image-processing software.
Precise measurements and calibration are key when dealing with anything being inserted into something else. For example, vision inspection can ensure a needle is inserted correctly into a patient with accurate and repeatable dimensional measurements to micrometer levels. It can also measure the injected dosage of a vaccine by weight measurement with injection volume validation.
Labelling a product is as important as the product itself. Sending a mislabeled product out to market can lead to disastrous consequences. Machine vision removes this risk by validating products have the correct label, with the right warning symbols, bar codes and serial numbers. It also takes it to the next level by ensuring readability with optical character recognition (OCR) or optical character verification (OCV) image analytics.
4. Color Analytics
Appling spectral imaging, or other imaging technologies to displays, AR/VR devices and medical imaging instruments where color accuracy is critical to make a device effective. A vision system will take images and compare them to set test limits. This will determine the pass/fail result.. In manual inspection, pass/fail is determined by the operator’s opinion. It has been proven that results vary from person to person, from morning to afternoon shifts and from Monday to Thursday. Vision inspection delivers consistent, reliable, unbiased, and repeatable results, any time of day. Another color analytics example is hyper spectral imaging which can be used to distinguish seemingly identical parts from each other. This is commonly used in pharmaceuticals to identify pills are bottled correctly.
The Human Eye Versus Self-Learning Software
The human eye is more than capable of spotting anomalies or defects on products. We see a defect on a product right away, provided that it is big enough. Even though we have never seen the defect before, we immediately notice a scratch on a small object or a torn seam on clothing. Usually, we unconsciously perceive the anomaly when we pick up a product, turning it and observing the reflections. This, combined with our exceptional interpretative abilities, makes the human eye almost unbeatable.
In recent years, however, machine vision technology has evolved considerably and now matches our interpretative abilities in many cases. Using complex, self-learning vision algorithms, the current technology is now capable of processing images in the same way the human brain would perform the task, though much faster and for more components simultaneously. If supplied with a picture library with additional information, intelligent software can teach itself where to find the errors without anyone having to program a single line of code. This additional information can indicate which products are good and which are bad or show where defects are located. Even products with a changed design can be recognized quickly. Plus, when dealing with high volumes at high speeds, the amount of data is huge. The system will not only collect this information but also organize it. Receiving data is great but understanding and benefitting from it is much better.
With the smart data systems produce, the information they take in goes far beyond product quality. When there are discrepancies, the system’s smart algorithms easily look at the data in front of them and can pick out the anomalies. Through machine learning, they can detect if these inconsistencies are an indication of an issue with the batch or an issue with the machine. This information opens the door to proactive action like scheduling preventative maintenance.
In practically any situation, machine vision can match or even surpass the visual inspection abilities of our eyes and brain. Competition is fierce and high-tech companies are preparing for action. A team of well-trained machines is the best form of protection when looking after your brand. Machine vision and machine learning will see and recognize patterns that cannot be processed by the human eye and brain at the same speed. As a result, they are there to protect your product and grow your brand faster than the speed of light, or pretty close at least.