• Flavia Cristian

Debunked: 8 Myths or Facts about AI-driven Visual Inspection



Introduction

The world of automation is moving fast. Nowhere is this more true than in the manufacturing sector, where visual inspection is gaining value along with manufacturing's digital transformation. Indeed, many new investment opportunities have emerged for those who can leverage advances in sensor technologies and machine learning to improve their inspection processes. In this article, we'll explore some of the key trends that are shaping the future of visual inspection by looking at 8 different areas:


1. AI-driven visual inspection is just for big companies.


Myth!

This is a common misconception about AI-driven visual inspection, and it couldn't be more wrong! In fact, our AI-driven visual inspection solutions are perfect for any business that wants to improve efficiency and productivity in their manufacturing workflow.


Whether you're a small startup or a large enterprise, we can help you optimize your manufacturing processes with our AI-driven visual inspection solutions. We'll help you streamline workflows, increase throughput, and reduce errors—all while saving money on labor costs. AI-driven visual inspection is becoming more and more prevalent in the manufacturing space, but there are still some other myths that need to be debunked.


2. AI-driven visual inspection is only applicable to high-volume production lines.


Myth!

The truth? Visual inspection is applicable to all types of production lines, even low volume ones. It's just that when you're looking at high-volume production lines, it seems easier to use AI because there's so much more data available for training your system and getting it up to speed quickly. For example, if you have one hundred thousand parts coming off your line every day, then you'll have ten million data points per year (assuming they're made up of ten different features each). That's enough data to train your system quickly and accurately! If your line produces only ten parts per day and has twenty features total, then you'll only have twenty thousand sample images available—which will take longer to train your system on.


The key here is not whether there is enough data; rather, it's about how long it takes for the system to learn what “good” looks like versus "bad." And if there aren't enough samples available, we can sort that part out together with you. See our data lake solutions!


3. Most inspections are still done by humans.


Fact!

Manual inspections done by humans are simply cheaper. That is the primary reason why most small manufacturing companies are still relying on their employees to visually check every output piece. However, this is not the most efficient way. With the digital transformation that most industrial companies are going through at the moment, AI-driven visual inspection software will become a reliable part of everybody's manufacturing process.


Manufacturers don't have to go through this change and transformation alone. There are AI companies like craftworks that are specialized in industrial use cases and can support and guide industrial businesses towards the solution they actually need, while also saving money on mishaps along the way.


4. Automation tools belong in the office, not on the assembly line.


Myth!

Automation tools also have an important role outside the corporate office. This means that the time taken to complete a task is less than it used to be, and so there is an increase in productivity. For example, you can use an AI-driven visual inspection tool to quickly find defects on your product or assembly line. The fact that this tool will do the work for you means that it can save hours of manual labor and help reduce costs by reducing labor costs while improving accuracy at the same time.


Automation tools have also improved accuracy because they are able to detect more defects than humans can on their own (due to their ability to magnify images). They also provide more detailed analysis of these defects so that companies can take appropriate action based on what they discover during an audit report generated by AI-driven visual inspection software applications.


5. Computer vision can reduce many inspection hurdles from color and lighting inconsistencies to damage, but there are some challenges that remain.


Fact!

Computer vision algorithms can be programmed to look for specific defects in a product through image analysis during an assembly process. They can be used to detect problems such as cracks, holes, or scratches at an early stage in production. They also make it easier to find defects caused by human error—if someone incorrectly puts together two parts of a car seat frame before covering it with fabric, for example.


Light sources may change during assembly, so computers can adapt their settings accordingly while looking at each piece of work; they'll also need less training time than humans do in order to learn what's normal or not when inspecting objects too (as long as everything is kept consistent)


6. All you need is a camera to detect everything.



Myth!

In some cases, cameras can’t help you measure new parameters. For instance, they can’t tell you the temperature of a specific part of your product or whether it is operating within a safe range. They also can't tell you how much force has been exerted on a given area, or if there are any faults in the structure that might cause concerns later on.


In these situations, sensors will come to your rescue. Sensors offer the following benefits when it comes to measuring new parameters:

  • They allow for the measurement of multiple parameters at once (e.g., temperature, strain, and pressure);

  • They allow for measurement where cameras cannot reach (e.g., deep inside an engine) or view (behind walls);

  • They can provide more context to help understand what's going on with your product.


7. Sensors can also be part of your attire.


Fact!

Wearable sensors offer more flexibility and application scope than fixed sensors. Wearable sensors can be used in places that are hard to access with fixed sensors, such as the inside of a ship’s hull or an underground tunnel. Furthermore, wearable sensors can be used for longer periods of time because they are more comfortable to wear and easier to use than fixed ones. They also collect different types of data—for example, video footage instead of just infrared images—and thus enable a wider range of applications such as personal security, surveillance, and even search-and-rescue missions.


8. Learning more about trends in visual inspection will help you plan your approach, so that you can get the most return on your investment.


Fact!

If ROI is on the top of your financial priorities, talk to us about the ways in which we can optimize your workflows! Together, let's find out how your answers to these questions could look like:

  • The importance of visual inspection in the manufacturing process;

  • The importance of visual inspection in the inspection process;

  • The importance of visual inspection in the inspection process for manufacturing.


About our industrial expertise


AI-based defect detection or classification is a further development of machine vision inspection solutions. It uses an aspect of machine learning, called deep learning. Just like a human eye, visual inspection AI solutions capture an image and process it. The system learns from examples and understands differentiations of characteristics and anomalies.

Due to its repetitive nature, human visual inspection is error-prone. The advantage of an AI-based system is the reliability and high accuracy. Furthermore, these systems can support humans in their decision-making and thereby reduce time, costs, and security.


We have worked with clients from multiple industries and created an industry specific visual inspection solution, tailored to their needs. Read more about them down below:


Diminished pseudo scrap through computer vision


Yield prediction with artificial intelligence

Predict the yield of wheat grain fields through sample images on the ground, two weeks before harvest.


Minimizing inefficiency in a screw conveyor

By analyzing optimal operating parameters, improve efficiency through artificial intelligence


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