In 2019, Google Cloud identified six sectors as vital components of its growth: public, healthcare, financial services, retail, media, and manufacturing. Within manufacturing, the cost of quality control and inspection continues to be among the highest. The American Society for Quality estimates that the price of quality may be as high as 15 to 20 percent of annual sales revenues for many organisations. For larger manufacturers, this translates into billions of dollars every year. Additionally, the rapid increase in production volumes makes it difficult for humans to manually inspect defects in computer chips and other products. To combat this, Google Cloud has recently announced an approach, backed by artificial intelligence (AI), for visual inspection. 

The newly launched Visual Inspection AI is a purpose-built tool to help manufacturers and related workers and businesses to inspect and reduce product defects and decrease quality control costs. Powered by Google Cloud Platform’s computer vision technology, Visual Inspection AI goes beyond the traditional methods of supporting manufacturing quality control through its general-purpose AI product, AutoML. 

According to Kevin Prouty, Group Vice President of Energy and Manufacturing at IDC, “Google Cloud’s approach to visual inspection is the roadmap most manufacturing companies are looking for.”

Visual Inspection AI aims to automate quality assurance workflows, thus allowing companies to identify and correct defects before shipping products. Through this, the new AI tool automates visual inspection using a set of AI and computer vision to improve production by increasing yields, reducing re-work, and cutting back on return-and-repair costs.  

Previous methods

COVID-19 has increasingly driven manufacturers to adopt AI into their production processes. According to a Google Cloud survey, 76 percent of executives say they have embraced digital enablers such as AI, data analytics and cloud computing. Additionally, 66 percent of manufacturers who use AI in their daily operations have stated that their reliance on the technology is increasing.

With this advancement, traditional methods to quality control inspections fall short. Traditionally, manufacturers include one or more steps to inspect products for defects visually. The visual inspection process is typically highly manual, making it vulnerable to human error and highly time-consuming. Moreover, traditional machinery used in machines are not flexible enough to adapt to product changes and can only detect a handful of defects at any time.

Artificial intelligence then is an agent that manufacturers are hopeful will bring in a more significant wave of innovation. Google Cloud listed multiple benefits of utilising AI, ranging from the reduced cognitive load for operators, fewer missed defects, no programming required (making it more flexible than previous machines), and the ability to detect hundreds of areas of interest on a product in seconds. 

Google’s new solution

As per Kyocera Communications Systems, a major manufacturer of mobile phones for wireless service providers, Visual Inspection AI is an innovative service that non-AI engineers can use. Google Cloud says that its new Visual Inspection AI meets the needs of quality, testing, manufacturing, and process engineers who might not be well-versed in AI despite being experts in their respective fields. Thus, the new tool paves the way to many substantial benefits compared to general-purpose machine learning (ML) models, such as superior computer vision technology, shorter time-to-value and high scalability. Through this, customers can deploy solutions within weeks, and an interactive user interface guides them through the steps. 

Visual Inspection AI has also improved accuracy by up to 10 times from general ML approaches. Finally, Visual Inspection AI deep goes beyond simple anomaly detection. Instead, it allows customers to train models that detect, classify and locate multiple defect types in a single image—doing so provides follow-up tasks on production lines to be automated. 

There are multitudes of ways in which businesses can use Google Cloud’s Visual Inspection AI in manufacturing. Automotive manufacturing, for one, can use it for paint shop surface inspection or press shop inspection—to look for scratches, dents, cracks or staining. On the other hand, electronics manufacturing could employ the tool for defects in printed circuit board components, and general-purpose manufacturing could improve upon procedures like packaging and label inspection, fabric inspection, metal welding seam inspections—to name a few. 

As per the above mentioned Google Cloud survey on manufacturing trends, the most common roadblock to AI integration is the lack of talent to leverage AI properly. Given this, Google Cloud’s new Visual Inspection AI appears as a brilliant step towards the proper deployment of artificial intelligence in the manufacturing industry.

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Artificial Intelligence (AI) can be used in many way: for data processing, analytics, combinatory methods, …

AI is a concept that stretches over any possible application field. In the field of automated inspection, there are some basic concepts that can be applied to accelerate and improve the process. For inspection purposes, it is possible to use Artificial Intelligence to detect cracks or dents in the workpiece automatically, which could take a human worker up to hours to inspect, while trying not to lose focus on the task. For this process, the concepts of classification and localization in AI can be used to ensure the function of this inspection system. Classification in this case means, that the AI can classify incoming surface data as intact or as different types of damages. The more different ‚damage‘ classes the AI learns, the more different cracks, dents, shape deformations and other damages, the system can detect. For this classification to function properly, the AI has to be taught many different shapes and damage types to learn every possible facet of a certain crack or dent to learn the fundamental features of a damage in order to distinguish between an intact or damaged object. This process is called ‘machine learning‘. Many different surface data measurements of damages have to be taught to the AI, for it to learn this classification behaviour in completion. There can always be more data to be gathered, because the more data the AI knows, the more certain it can classify these damage types.

The other principle used is the concept of localization. This means, that the AI is able to detect its known damage types inside of a large 2D image. The AI has to learn, how to detect the damages in visual surface data or 2D projections of point clouds that are given to the AI program by the different measuring methods. If the data comes from either White Light Interferometry – a high precision 3D surface inspection method – or visual camera data, the AI can detect and show the position of damages on a connected screen. By combining these two concepts, the AI for inspection purposes can localize and classify any taught damage that occurs on the workpieces surface.

For a little extra usability, an AI can also be taught how to read texts on product labels via Optical Character Recognition. With this feature, all the information from the product label, like serial numbers and product names can be processed and digitalized together with the inspection data. 3D.aero has created a specialized neural network, that doesn’t only allow to read printed labels, but that can also process texts in any form on any surface. Whether it is handwritten, punched or milled in, the AI can read and digitalize each character correctly.

3D.aero use these advanced AI concepts to optimize the inspection and production processes in the aviation industry. 3D.aero expertise lies within the application of these systems to aviation parts and the usability within the hangar workspace.

3D.aero uses Artificial Intelligence for its robot based AutoInspect system. The AutoInspect system provides crack detection in µm-resolution which is used for stationary inspection of aero engine parts. The AI can also be used for the interactive Cobots, where robots work together with human workers on the shop floor where it is very important to have an integrated live tracking system of movement, so that the robot can interact with its surroundings in synchronization. If that would not be the case, the robot could hurt nearby workers or damage the workpiece by not adjusting to sudden movements.

The newest innovative usage of AI at 3D.aero, is their AI2Go system where the process of damage detection and classification can be managed on an edge device such as a tablet or mobile phone. This mobilizes the process and makes it possible for a worker, who is already equipped with a mobile device, to assess anomalies in places that are not reachable for any shop floor robots.

3D.aero has developed the expertise to use AI in the aviation industry, while being able to use the technology on almost any device. Either mobile or stationary, the 3D.aero approach to using AI for inspection purposes in damage detection and classification enabling a functional system early into the integration process, while constantly increasing the applications capabilities. 3D.aero state however “We don’t let our AI algorithms improve themselves during operation. For technical purposes in general and especially in aviation, reliability is key. Therefore we still require a human expert to assess new training data before updating the system.”

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