How Can Artificial Intelligence (AI) Improve Automated Optical Inspection (AOI)?

Using modern automated optical inspection (AOI) inspection machines is a useful and reliable method of streamlining the manufacturing process and getting rid of defects and errors from different types of products.

Due to the increasing complexity and number of product components in the manufacturing sector, advancements in automated vision inspection procedures are critical to fulfilling complex inspection needs.

Implementing modern artificial intelligence (AI) technology in AOI procedures is one of the best ways to enhance the quality and efficiency of AOI machines. In fact, even traditional machine vision is considered AI to some extent.

Keep reading to learn how AI can improve AOI.

What is Automated Optical Inspection (AOI)?

Different types of defects and errors are bound to happen in product manufacturing procedures. Such types of defects can affect the performance of the product and dramatically minimize its durability, causing losses to companies and users.

Modern image sensing technology and AOI machines are used for quality assurance to detect different types of defects in products and improve their quality.

Automated optical inspection is much more effective than manual defect inspection because it ensures higher speed, greater precision, and more stability in Quality Control with the help of high-quality AOI machines.

In recent years, there has been immense development in AOI solutions. With this technology, manufacturers can produce products with higher quality consistency via better measurement, detection, defect identification, and comprehensive product quality assurance.

Using AI tools and technologies is a major development in the innovation and advancement of AOI solutions.

The Rise of AI in Automated Vision Inspection of PCBs

The fundamental idea behind image recognition is to scan every image that is taken and use a variety of filters to identify patterns and features in products.

Edge detection filters are frequently used to identify objects in images while slope detection is applied by algorithms that can recognize human features like arms, shoulders, and legs.

As an additional defining criterion, it is also important to determine the orientation of these identified characteristics with respect to one another.

It is, however, difficult to define guidelines and develop algorithms for identifying and categorizing items in digital photographs.

Creating trustworthy algorithms for industrial inspection is costly and time-consuming. Solder joint quality is just one of the parameters that must be examined while assessing PCB components.

Each component’s presence, as well as its placement and orientation in relation to the solder mask, component coplanarity, and the presence of foreign objects, must be confirmed.

A never-ending process that necessitates continuous software upgrades is fine-tuning algorithms and introducing additional algorithms to cover more conditions.

New algorithms must be created to recognize new products as they are used in the market. In some ways, artificial intelligence (AI) can mimic human behaviour.

The Role of AI in AOI Solutions

Both consumers and users of AOI equipment may benefit from AI. If AI can estimate the likelihood of discovering a specific thing, it will make it easier for suppliers to design algorithms.

This reduces the need to specify each item and its related acceptance criteria, which helps shorten the time it takes for new products to hit the market and lower continuing software support expenses.

Users can simplify inspection system settings, programs, and quality judgment values by applying better AOI with AI. In comparison to conventional systems, the AI-AOI system offers higher accuracy, fewer false positives, and can be quickly trained to detect novel items or find previously undetected flaws.

Whether the AOI system is programmed by a novice or an expert, AI can automatically alter parameters faster than human experts and make decisions with a substantially lower risk of error. This enables consistent detection results.

In Conclusion

The aforementioned examples demonstrate how AI may improve inspection applications across a variety of industries, including security and retail. AI can streamline setup and programming, minimize human errors, reduce latency, and promote improved decision-making.

Manufacturing companies of various industries have been able to increase quality assurance, increase productivity, and constantly improve processes thanks to inline AOI, which runs at line speed. The next phase for AOI is enhancement using AI.

The decision-making capabilities of algorithms trained for optical inspection applications enable lower operator participation, easier programming, and robust performance that increases the certainty of defect identification while simultaneously lowering false calls.

The future of AOI’s development will focus on further advancements based on AI. Additional advantages of training algorithms for optical inspection applications will include improved decision-making abilities, streamlined processes, and higher manufacturing performance.