How does AI change the manufacturing industry and the industrial Internet of Things? -Smart Manufacturing Channel

According to data from Business Insider, the manufacturing industry is about to usher in another substantial increase in the Internet of Things (IoT) and artificial intelligence (AI) applications. It is estimated that by 2027, the Internet of Things market will reach 2.4 trillion US dollars.

In addition to obvious applications in areas such as automation and robotics, AI systems can also optimize manufacturing processes, send early warnings, improve quality inspection and quality control, and predict equipment failures in machinery.

The key to optimizing the manufacturing process is to collect the correct data. By doing so, manufacturers can develop innovative AI applications that differentiate themselves from the competition.

Many manufacturing companies have begun to use various AI algorithms in their Industrial Internet of Things (IIoT) applications to make real-time decisions. Understanding that data is king in AI-based applications is crucial. Collecting, cleaning, and preparing unique data are the most important aspects of using AI to optimize organizations and gain insights.

Before AI engineers start training their machine learning models, they usually spend up to 75% of their time simply processing the starting data. Remember, to train a machine learning model that can run on an IIoT device, there must be a data set or a series of data sets to reflect the actual situation of the application when it is running.

The process of creating a data set needs to be implemented in several steps. It usually starts from collecting data for many years, and engineers need to determine the overall structure of the data. Next, they need to eliminate any defects, discrepancies or gaps in the data, and then convert these data into the form required by the algorithm in order to interact with it effectively.

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Edge AI is an important part of the overall AI development of the manufacturing industry. Edge AI can process data locally on hardware devices instead of relying on centralized databases or processing nodes connected via the Internet.

In most IoT solutions, the back-end server receives data through multiple devices and Internet-connected sensors. One or more servers host the machine learning algorithms used to process the data to create any value provided by the AI solution.

The problem with this AI architecture is that many devices may overload the network traffic, or you may be using a network that is already heavily used. In these cases, sending data back to the central server may result in unacceptably slow processing speeds. And this is where edge AI plays its value, because some less complex machine learning and AI processes can be executed locally on hardware devices.

Edge AI is critical to many industries. An example is self-driving cars, where edge AI can reduce battery power consumption. Surveillance systems, robotics, and several other industries will also benefit from edge AI models.

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The introduction of Knowledge Distillation technology has great potential to improve edge AI solutions.

Knowledge distillation is a model compression method based on the principle of knowledge compression. Using techniques such as reinforcement learning, neural networks can learn how to produce expected results, so that a smaller network can also learn to create similar results to those created by a larger network.

This smaller network size is more suitable for edge devices such as mobile devices, sensors and similar hardware. Knowledge distillation can reduce the space burden of edge devices by up to 2000%, thereby reducing the energy, physical constraints, and the cost of the device itself required to run the network.

An example of applying knowledge distillation techniques is the use of video sources to detect gender in real time on a surveillance system. Generally, identifying gender requires a fairly large cloud-based neural network. But in a real-time system, returning to the cloud is not always the best option. Through knowledge distillation technology, the entire process can be reduced to a smaller network, which can accurately identify gender while being installed on edge devices.

Predictive maintenance is a particularly fruitful area where machine learning and AI have an impact on manufacturing. In fact, according to a study by Capgemini Consulting, nearly 30% of AI implementations in manufacturing are related to the maintenance of machinery and production tools. This makes predictive maintenance one of the most widely used application areas in the current manufacturing industry.

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The two most important benefits of predictive maintenance based on machine learning are its speed and accuracy. AI can identify mechanical problems quickly and accurately enough to correct them before they occur or even malfunction.

For example, General Motors uses AI cameras installed on assembly robots. Through the use of cameras, it can detect failures of dozens of components in a group of more than 5,000 robots, thereby avoiding possible failures.

Predictive maintenance based on machine learning can use a variety of models and methods, from regression models and classification models that use historical data to predict failures, to anomaly detection models that analyze systems and components to look for signs of strain or abnormality.

The automotive and consumer product industries are facing stringent requirements from regulatory agencies, and maintaining compliance with these regulations is an area where AI and machine learning can play a role. The cost of high-quality cameras is declining every year, and AI image recognition and processing software is constantly improving rapidly. Therefore, AI-based detection methods are becoming more and more attractive to enterprises.

Especially in the automotive industry, for example, the German car manufacturer BMW took the lead in adopting this technology. BMW uses the AI application as the final step of the inspection process, comparing the newly manufactured car with order data and specifications. Nissan, another automaker, has also made significant progress in incorporating AI vision inspection models into its quality assurance process.

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Part of the reason for the increasing popularity of visual inspection algorithms is the maturity of these algorithms. Now, neural network-based systems can identify various potential problems, such as cracks, leaks, scratches, warpage, and many other abnormalities.

The parameters to be checked by the application can be adjusted or adapted to a given situation according to complex rule mapping. When paired with GPUs and high-resolution cameras, AI-based inspection solutions can greatly exceed traditional visual inspection systems in terms of accuracy and speed.

From a certain perspective, the future of manufacturing is almost synonymous with the future of AI based on IoT. In 2019, there are an estimated 8 billion IoT devices, but by 2027, it is estimated that there will be 41 billion IoT devices, and the largest share of this growth will be manufacturing. The valuation of AI in the manufacturing industry is expected to increase by more than 15 times, from the current approximately US$1.1 billion to more than US$16 billion in 2026.

All the characteristics of efficient production—standardization, economies of scale, task automation, and specialization—are largely due to the implementation of machine learning and AI solutions. Therefore, in the next few years, AI embedded in IoT devices will inevitably continue to be closely integrated into more manufacturing processes.

Founded in 2015,Zunhua Baorui Titanium Equipment Co.,Ltd. is a manufacturer specializing in pvd vacuum ion coating equipment. The company’s products mainly include large plate coating machine, large tube collating machine, tool coating machine and LOW-E glass production line. Mr.Wang baijiang ,general manager of the company ,has been engaged in vacuum coating industry for more than 30 years. He continuously improve production technology, improve product performance and devote himself to provide customers with better product experience and higher production efficiency.