Future of quality control by artificial intelligence

                         
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In 2020, we will see accelerated adoption of deep learning, as a part of the so-called "Industry 4.0" revolution, in which the digitalization will transform the manufacturing sector. This is the last wave of the initiative is to be marked by the launch of the smart and autonomous systems fueled by data and deep learning, a powerful race of artificial intelligence (AI) to improve the quality of the factory for inspection. 
 

What are the benefits? With the addition of smart cameras and software for the production line, manufacturers are seeing improvements in the control of quality in high-speed and low-prices with the human inspectors can't be beat. And due to the current limitations of the human resources, as a result of COVID-19, such as the social distance from the factory, these benefits are even more important to keep the production lines. 
 

Even though the manufacturers with the help of computer vision for decades, the monitoring of the quality of the software, deep learning, support, represents a new frontier. So, how does this approach differ from traditional video surveillance systems? So, what happens when you click on the "RUN" button to enter any one of these systems for the quality control carried out by the AI?

The improvement of the quality 
 

The AI in the manufacturing process can be complex and difficult to make, especially with very complex products, or materials that require high precision and tolerance. Manufacturing companies use automation in order to improve the quality through the use of an automated visual inspection of instruments for the detection of defects on the production line. Of these devices, it reduces the processing time by reducing the number of defective products. 

As well as the production load of the equipment, and AI is the cure. A visual inspection of the equipment can detect the failure of a higher level of precision and speed that far exceeds that of a human being. With the help of an automated system for quality control, it may be replaced by people who will have to manually identify defects in a process, which is often prone to errors. 

An artificial intelligence system not only detects errors, but also to prevent them. The care and maintenance of the system, with the support of artificial intelligence, self-monitoring and reporting on production issues in real-time. Connected to the sensors on critical equipment, for the collection of data.

With the help of artificial intelligence, quality assurance 


When it comes to digital transformation, most companies have a vision for the customer experience, efficiency, flexibility and profitability to include the modernization of the infrastructure, processes, and applications. The Quality assurance (QA), it is often a matter of secondary importance. 

However, any digital program is running on, always in an agile development platform, or DevOps, and translate it into a shorter and shorter production cycles, with the additional pressure to deliver high-quality software code in a much shorter period of time. In order to help with this, organizations that provide additional checks on the web, DevOps, and don't forget to control the quality of the strategy. You will need to change the organization of the quality control of the method. In general, there are two driving forces for flexibility in how the tests are to be carried out continuously, quality assurance, and a faster time-to-market. The QA team will be able to keep up with the agile development mode, making traditional test automation it is not enough, and the creation of artificial intelligence, which is inevitable in the test system. 

                         
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How can AI be used, if the defects are noted 
 

There are plenty of solutions for the detection of defects in workmanship, with the help of the cameras for multiple purposes, but the AI and image processing, it goes beyond the camera. Image processing, collects all the data into a cloud-based database storage, from where it can be reviewed by the tens or even hundreds of machine learning algorithms to accurately identify defects, or vibration and allow for course correction measures. The results are analyzed in order to assess the risk of the development, and the programme as a whole. At the time the product is ready for production, all kinds of kinks, and the tea will be gone. Once a product has been in production for a AI-driven defect detection process that continually monitors the production of the reverse of the addition of the defective product (s) to the proper location and ready to be installed. 

The role of machine learning 

The Machine learning algorithms that have been set up at the basic level, which is a normal block, it looks like. As a design engineer, you will create an image-processing framework for the identification of the most important areas for you to explore. These algorithms are aimed at the point-of-interest, and to identify anything that is different than that of the base sample. Just like any other skill gained more and more advanced in time, but it's most useful feature is that the same algorithm can find a variety of defects. 

In a typical vision-based systems, any defect should be evaluated and programmed, before, before it's time. This can be costly. An algorithm is unable to perform the work of the various algorithms. Quickly learn how the product should look and work, and self-issues detected in real-time, save-money, resources, and prevent the occurrence of defective products from reaching the consumer.

With the help of a traditional deep-learning model for the control of the quality of the 


Data is the key to the effectiveness of deep learning. Systems such as deep neural networks (DNS), we learn to recognize certain types of things are in a safe and controlled manner. In a traditional management-task DNN can be trained to visually recognize a number of classes, such as the pictures, good or bad, with photos. Under the assumption that it was powered by a good amount and the quality of the data, DNN, it will come up with a precise, low-tolerance, is safe ratings. 

Let's take a look at an example of how to define a good and a bad relationship. As long as the bar remains the same, all you have to do so, the manufacturers, please click on the "RUN" button to start the scanning process on the line. However, if the line is set to have a new valve, of the type of the data collection, preparation, and execution is to be carried out. 

For a traditional, deep-learning, in order to be effective, it may be that the information is to be used for the course and must be taken into account."With a well-balanced dataset that contains as many photos of the good and valves, such as images, faulty valves, including all kinds of defects. Even though it is a very easy-to-assemble images of the great shut-off valves, a modern manufacturing and has very low levels of defects. This leads to the fact that the collection of broken images is time-consuming, especially when you need to make a collection of hundreds of photos of each type of defect. To make things even more complicated, it may be that, as soon as the system has finished and implemented, and a new type of defect, you will be required to remove it, the re-training and re-distribution of the system. When the crazy fluctuations in consumer demand for goods caused by the pandemic, as the manufacturers may also be impacted by manufacturing defects.

Solve the production problems, with the help of the intelligence 

The automation of the processes of human-level intelligence, we need to algorithms and technology with a personal, human-level intelligence. Fortunately, researchers from leading institutions and companies, many of the methods that can greatly improve the production. 

One such technology, which are covered in the artificial intelligence, artificial intelligence, computer visionthe neural networks (CNNs). CNNs automatically learn what is the difference between the good and the bad of the elements in a production line at an incredible rate of speed. With a team of AI is designed for the control of the quality and training of the photos are to show the good parts and not so good parts, the CNN training can be very fast. This is a great solution for high-mix environments in which the products are to the environment is constantly changing, and time is precious to us. In optics, in particular, CNNs are quickly able to respond to the different lens characteristics and the occurrence of errors and failures. 

In the low-mixing environments, the traditional computer-vision technology can make a big difference, performing a specific task very well, and very quickly, but with less flexibility. In addition to the developments in reinforcement learning, the robots will also learn how to correctly discern objects, and even the learning of human actions to perform simple tasks. This allows you to increase, and in turn, for general-purpose robots, or robots from the future, you can learn how to be self-reliant, without the continuous re-programming before each new product is required. In the optical industry, these robots can be trained to carry out the complex task of filling of the polymer lens types, which currently requires an experienced and qualified staff.

QA Automation for a better business 

Automation of the design has been around for many years to come. However, the benefits of automation have not been effective enough for companies to sit up and notice. 

In the first generation of the automation process, the main goal was to be based primarily on the user interface, as well as focusing on the power. The goal was to create a framework that could accelerate the automation of the trading tools. The automation is started to process a data-driven, keyword-and, later, business process, management structures, and that is the important customer to cost-cutting measures. However, the savings were mostly limited, it was not much of a difference for your business. 

The next wave of automation is a functional business page that is in the form of API / middleware, automation, automation, testing information, and more. This is really the value of the automation of test activities, in particular in the course of the test. The focus is shifted from the user interface, based on the automation of a multi-level, multi-stack automation, which has had an impact on the productivity and time-to-market. 

This wave of automation continues to grow, with an increased focus on continuous testing. Test Driven Design (TDD) and Behavior Driven Design (BDD), and the creation of an integrated automation solutions are entering the mainstream, and aren't limited to testing only. Developer unit testing (UT), and companies in the implementation of the First Testing (UAT), also make use of the automated scripts to test the functionality and time-saving. Tests have also been extended to the borders of the black-box functional testing white-box testing an internal perspective of the system, which will lead to a better quality of the code. 

Automation of the execution of the proof of concept will continue to grow with the widespread adoption of open source and automation solutions, agile, continuous testing, and integration of other vendors ' systems and solutions to the digital in a test environment.

Visual testing of the user interface 


AI will help you get a visual fix on your site and or pages. AI, you can test different types of content in the user interface. These tests are difficult to automate, and usually require a human intervention is needed in order to come up with a design to make a decision. However, the ML-based visualization tools, the contrast in the image is to be displayed in such a way that it is not possible to determine exactly one of them. The AI test, which eliminates the manual effort to modernize the document Object model( DOM), and the building of the structure and of the risk of scratching the paint.

Conclusion 
 

Before any technology is a manufacturer will often go to different companies for the installation of your products, and some of her clients include companies such as Apple and Microsoft. 

Back in 2015, in an effort to improve working conditions for its employees, the company is committed to the 10 000 industrial robots, to help cope with the increasing production and demand for products like the iPhone, as well as the X-Box. 

According to Foxconn, the process is slow, and how to automate simple, repetitive tasks as it is the one thing, which is the implementation of systems that use artificial intelligence, it is an entirely different matter. The company said that its artificial intelligence system and do not have the cognitive abilities of a person, an employee, who has been in a much stronger position to deal with the second phase of the monitoring of the quality of the product it is beautiful check. 

It goes without saying that in the future, while the number of jobs could be lost as a result of automation, while others have been created as humanly possible, and with the advent of AI-based solutions.