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AI based Quality Control

AI based Quality Control

Quality assurance can be defined as the maintenance of a desired level of quality in a service or product, especially by means of attention to every stage of the process of production. Automating QA by using an autonomous, self-improving AI solution can drastically save time and resources. The automotive, life science, robotics and consumer goods industries are already benefiting from technologies brought to us by the so-called “Industry 4.0”.

 

Industry 4.0 is the translation of a German term coined by the German government. The phrase refers to the fourth evolution of the manufacturing revolution. While the first three were powered by such advances as mechanization, steam power, the assembly line, and computer automation, the fourth age of the Industrial Revolution involves cyber-physical systems. Cyber-physical systems are a combination of software, sensors, processors, and ICT.

 

AI is spearheading this new wave of technology and quick-thinking manufacturers are beginning to understand the potential digitalization and interconnectivity can bring to the table. When looking at quality assurance, there are several ways you can apply technological advances and turn them into an advantage in the market.

 

To get an understanding of how AI might improve quality assurance, manufacturers have to imagine their assembly lines as data-driven, interconnected and autonomous networks. The system works thanks to a set of parameters and algorithms that provide guidelines for the best possible layout of a certain production process. If sensors detect even the slightest deviations from the usual values, they immediately send out a signal which is turned into notifications users can see and react to ahead of time. The system can also make adjustments on its own, independently learning about best practices as time goes on.

According to Forbes, automating quality testing with machine learning can increase defect detection rates by up to 90%.

AI, machine learning and advanced analytics can also be combined into a powerful tool for predictive maintenance. Analyzing components’ attributes and production parameters during different timeframes, the system is able to gain insights about when a critical failure might occur. Predictive maintenance enables manufacturers to save huge amounts of resources by reducing downtime to a minimum.

 

Even though AI can’t match the cognitive ability of humans and help with the fine-tuning of certain quality assurance tasks, it can dramatically impact the speed at which this process is performed and continuously improve manufacturing systems through sheer repetition and advanced data analytics.

 

Machine learning enables a highly proactive approach to problem-solving which is based on predicting machine processes that slow down or fail well ahead of time. Quality assurance becomes much easier once you have an autonomous, self-improving network able to track various manufacturing processes and let you know when it’s time to act.

 

AI + Computer Vision

 

Computer vision plays a very big role in quality assurance. Most manufacturing errors are visually identifiable. If computer vision used correctly used in those places, we can achieve near perfect quality control result.

Examples:

 

Controlling food presentation: Domino’s Pizza Checker

 

Quality control can also take place at the very end of the line, to check a product’s compliance with presentation standards. This is the case, for example, at Domino’s Pizza, which has installed a “Pizza Checker” in its chains, a video control system, driven by artificial intelligence that checks that pizzas delivered to customers look the way they are supposed to.

Size, shape, distribution of ingredients… everything is checked automatically to ensure the satisfaction of even the most demanding customers.

 

 

 

 

Tomato Sorter

 

This amazing tomato sorter machine, powered by AI, is one of the examples for quality assurance. It separated tomatoes which are ripe enough from which are not.

 

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