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Automating Inspection – The Missing Link For End-to-End Automation

End-to-end automation is the idea that every process on the production line is automated using a variety of machines and robotic processes. While this concept is quite popular when it comes to manufacturing machinery, more complex and detail-oriented operations like quality inspection pose more challenges. In this article, Ofer Nir, VP Products and Marketing at Inspekto, the company that developed Autonomous Machine Vision, discusses how automating quality inspection is a must for a complete end-to-end automated factory.

According to research firm Gartner, end-to-end automation, also known as hyperautomation, is one of the top ten strategic technology trends at the moment. From its potential to replace outdated work processes to generating business growth, hyperautomation offers massive benefits for companies regardless of their size.

However, automating all steps of the production process and then rely on outdated and unreliable methods to verify the quality of incoming and outgoing goods leads to incomplete automation. Robotics and artificial intelligence may help speed up production and minimize costs, but if faulty products reach customers because an inspector missed a defect, this will negatively impact the bottom line of a business.

Automating quality inspection is the missing link to a true and complete end-to-end automation strategy that optimizes all the most important variables — production costs, speed, and most importantly, quality.

A Cost-Effective Investment

Given the cost and complexity of conventional machine vision solutions, manufacturers might think that automated quality inspection is out of their reach. So, they could  either avoid implementing quality control along the production line, or hire human quality inspection operators. However, humans are naturally prone to error — inspecting the same piece for an eight-hour shift, an inspector’s attention span might deteriorate leaving certain defects unnoticed or rejecting unfaulty products.

Manual inspection, in many cases, is based on sampling, which pose a risk on the quality of all items not inspected. Obviously, manual inspection means there is no image saved of the item inspected for future analysis – whether this is when a claim comes in, or for generating dataset and insights for production optimization.

Even when using conventional machine vision solutions, a team of technicians might still be needed to maintain the solution, control the complex system and its components, adjust and update it whenever there is even a small change in the product, the production line or the environment and make sure that items are thoroughly inspected.

In many industries, a single error could cause significant losses, both in costs and scrapped materials, and impact the service level agreements (SLAs) and the supply chains. Over time, this can irreparably damage a company’s reputation and erode customers’ trust.

Manufacturers should instead consider the costs in the long run and prioritize investments that will bring a fast ROI, while leading to higher-quality products. A single Autonomous Machine Vision system can replace a team of inspectors, freeing up personnel from tedious, repetitive tasks so that their skills can be used on more complex jobs that require decision-making and problem-solving.

Improved Product Quality

With the advancement of hyperautomation and an increased digitalization, quality inspection systems are also developing and becoming increasingly precise, but the main drawbacks of conventional machine vision solutions remain unaddressed—they are still inflexible and extremely costly and time-consuming to set up.

However, the new generation of Autonomous Machine Vision for quality assurance (QA) has overcome these barriers thanks to artificial intelligence, one of the core solutions for a successful hyperautomation strategy. Not only are Autonomous Machine Vision systems accurate for defects finding, but they are also able to adapt easily and quickly to new products and surfaces and they can inspect a variety of products, not just one, even on the same single unit.

Inspekto just launched the second generation of the only Autonomous Machine Vision solution on the market, the INSPEKTO S70 Gen.2. The system combines a unique electro-optic edge device with a powerful software powered by three independent and synergetic AI-based engines. The three engines work together to mimic the entire cognitive capabilities of the human vision process, starting from the image acquisition.

The first is the acquisition AI engine, in charge of dynamically adapting the operating parameters of the electro-optical imaging system (such as zoom, shutter etc.) in real-time to capture the best image required. The second is the detection and alignment AI engine, responsible for the identification, classification and 3D alignment of any object within the acquired image. This engine allows the system to recognize the parts it sees in a live video stream and identify the best moment to acquire an image and complete the last task – inspection, which is performed by the third AI engine.

These developments in quality inspection systems using AI make the idea of hyperautomation more attractive to manufacturers. This is because Autonomous Machine Vision systems are reliable, extremely flexible as they can inspect a huge variety of use cases, and do not require help from external experts to be set up because they are pre-trained. They are also extremely cost-effective compared to conventional machine vision systems, so manufacturers can consider deploying such systems at every step of the production line and not only in very critical spots or where the cost of an error is very high.

For a factory to be fully efficient, it is not enough that all the processes are automated.  If at the end of the production line products are inspected manually, or the production line is fixed for a very specific SKU, there is little room to change and adapt. Autonomous Machine Vision systems provide the missing link to the implementation of a thorough end-to-end automation and industry 4.0 strategy – keeping the production line digital, automated and agile.

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