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Preventing Defects with AI Quality Control

Preventing Defects with AI Quality Control

If you run a business that focuses on manufacturing products, you know product quality is an essential feature for success. Excellent production quality will help you minimize production costs, scraps while increasing the overall yield and product quality.

Defect prevention is a critical step in quality control, but it's prone to human error and employee skills. However, with the right artificial intelligence solution, manufacturers can prevent defects with higher accuracy. Let's see how AI improves quality control and why it's the best solution for preventing defects in manufacturing.

Problems of Traditional Visual Inspection Practices

Traditionally, most manufacturers put their products through multiple rounds of visual inspections. Employees are trained to identify defects and prevent the products from making it to the stores. Visual inspection used to be a manual process that took a lot of time, and no matter how good someone is at finding product defects, some of them still pass under the radar.

Of course, as technologies evolved, manufacturers started introducing rule-based visual inspection machines to improve efficiency and reduce errors. But there's a problem with this approach. Manual inspection depends on the operator's skill, experience, and perception. Inspectional machinery has to be programmed, so it's not flexible enough to understand product changes. Even the best visual inspection machines can only detect some defects while leaving others to go unnoticed.

The manufacturing industry has been looking for ways to improve the visual inspection of products for decades. As an industry that's not afraid to introduce new technologies, manufacturing practices keep evolving faster than most other industries. Manufacturers were among the first businesses to introduce AI into their processes, including advanced visual inspection solutions. Today, AI solutions are replacing human employees across all manufacturing stages because they provide all kinds of benefits. Moreover, AI is much more capable of preventing defects and finding issues humans simply can't.

Visual Inspection AI

Visual inspection AI is the next step in quality assurance. It's a technology that uses advanced AI and computer vision to improve quality control. AI is the best solution for preventing defects because it doesn't make mistakes and is far more efficient and accurate than any human. Its ability to detect defects automatically is helping manufacturers save a lot of money on quality control. 

Of course, AI is a direct result of powerful machine learning (ML) models that use large amounts of data to create inspection rules and bring quality control to a higher level. AI-driven quality control solutions offer an entire list of benefits that lead to better product quality and happier customers. Let's see how AI quality control improves existing manufacturing practices.

1. Autonomous on-premises capabilities

Instead of hiring quality control experts, manufacturers can now inspect models and look for defects remotely. However, they can also install small IIoT sensors with built-in laser technology, allowing the AI to inspect products directly on the factory floor.

2. The ability to learn from images

Visual inspection AI solutions can be trained very quickly. All you need to do is to feed the ML with hundreds of product images. The ML will learn how to differentiate product defects from non-defect patterns, allowing AI to sort all products extremely fast. As soon as a unit is scanned, the AI can identify hundreds of different design defects and remove them from the production line.

3. Results that surpass simple anomaly detection

As mentioned above, AI solutions are much more accurate when it comes to finding defects. It can differentiate the tiniest differences between a good and a bad product. Apart from using visual inspection, AI relies on a specific set of rules it creates automatically. It scans all products, works out all combinations of features, and defines product quality. It's ideal for preventing defects and identifying potential design flaws. 

4. Highly scalable

Once an ML model learns how an entire production line works, it can apply to other product types and factories. As long as the ML model has access to relevant product data, it can identify the smallest defects a human never could. The same approach can then be applied to other production lines and factories anytime in the future.

5. Real-time quality assurance during production

Most industrial AI solutions for preventing defects use force-torque and laser-line sensors to monitor production in real-time. The AI oversees the entire assembly line and can identify defects during production. It can identify product defects immediately, increasing precision and reducing inspection times. 

Benefits of AI for Quality Control

It should be clear by now that AI-powered quality assurance is much more accurate and reliable than any other existing technique. Its ability to identify design flaws has a profound effect on overall product quality and customer satisfaction. Let's see how AI improves quality control in other ways.

1. Reduced Downtimes and Increased Productivity

Product quality checks almost always rely on visual confirmation. Visual inspection is needed to ensure that all parts are connected correctly and that the product has the right size, shape, color, texture, etc.

As you can imagine, manual confirmation of all of these features takes a lot of time and focus. However, AI-powered solutions for preventing defects can inspect more products and product varieties much faster and with supreme accuracy. 

Moreover, the system copies human behavior and takes the same approach when looking for mistakes in the images. It elevates quality control to a higher level, reduces the downtime needed to inspect every unit, and increases productivity. 

2. Predictive Maintenance

Predictive maintenance is a practice that ensures product quality. The AI solution uses IoT sensors to scan the entire production line or factory. The sensors keep feeding the ML with real-time data, allowing the AI to monitor every machine closely. Faulty machinery is usually the main reason for product defects, and AI can help identify underperforming machines before the damage is done.

Knowing which machines are close to breaking down can help you save money by stopping production and making adequate repairs. Of course, the AI can predict the exact moment when a machine will break down, allowing you to make repairs on time. That leads to a significant reduction in downtimes and a massive boost in quality control. 

The Future Of Quality Control

The process of digitalization is completely changing traditional manufacturing practices by introducing modern technologies. Manual labor is taken out of the equation as most manufacturers introduce robots and AI solutions into their production lines. 

AI systems still don't have the same cognitive abilities as humans, but they are much better at detecting product defects. AI uses automation to get more work done, and it doesn't make mistakes. With that said, humans are still a key part of the process, but today they work together with AI and robots. 

It's clear that the future of quality control largely focuses on advanced sensors, robots, and AI. Quality assurance is becoming automated, some manufacturers are switching to AI solutions, while others combine their human workforce with AI-powered solutions. We expect that quality assurance won't change anytime soon. 


AI-powered quality assurance solutions designed to prevent defects from reaching the markets are finding an application across many different industries. AI offers higher accuracy and increased productivity while minimizing risks and errors. It's the next logical step in quality assurance.

Artificial intelligence and machine learning are already redefining manufacturing through digitalization. They are improving business practices across all manufacturing stages, and quality assurance is one of the areas with the most advancements.