How does AI change manufacturing and industrial IOT?

According to the data of business insider, the manufacturing industry is about to see another significant growth of Internet of things (IOT) and artificial intelligence (AI) applications. It is estimated that by 2027, the market scale of the Internet of things will reach US $2.4 trillion.

In addition to obvious applications in 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 correct data. By doing so, manufacturers can develop innovative AI applications to stand out from the competition.

Many manufacturing enterprises begin to use various AI algorithms in their industrial Internet of things (iiot) applications to make real-time decisions. It is important to understand that data is king in AI based applications. Gathering, cleaning up, and preparing unique data is the most important aspect 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 that to train a machine learning model that can run on an iiot device, you must have a data set or a series of data sets to reflect the actual situation of the application when it runs.

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, differences or gaps in the data, and then convert the data into the form required by the algorithm to effectively interact with it.

Edge AI of embedded system

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

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

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

Edge AI is critical to many industries. An example is the autonomous vehicle, in which the edge AI can reduce the power consumption of the battery. Surveillance systems, robotics and several other industries will also benefit from the edge AI model.

Stimulate the potential of edge AI

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 learn to create similar results as a larger network.

This smaller network scale 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%, thus reducing the energy, physical constraints and the cost of the device itself required to operate the network.

An example of applying knowledge distillation technology is the real-time detection of gender on a monitoring system using a video source. In general, gender identification requires a considerable cloud based neural network. But in real-time systems, returning to the cloud is not always the best choice. Through knowledge distillation technology, the whole process can be simplified into a smaller network, which can accurately identify gender while installing to edge devices.

Predictive maintenance based on machine learning

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 manufacturing AI implementation is related to the maintenance of machinery and production tools. This makes predictive maintenance one of the most widely used application fields in the current manufacturing industry.

The two most important advantages of predictive maintenance based on machine learning are its rapidity and accuracy. AI can quickly and accurately identify mechanical problems so that they can be corrected before or even before a fault occurs.

For example, General Motors uses the AI camera installed on the assembly robot. Through the use of the camera, it can detect dozens of component failures in a group of more than 5000 robots, thus 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 faults, to anomaly detection models that analyze systems and components to find signs of strain or anomalies.

Computer vision for quality control

The automotive and consumer product industries are facing stringent requirements from regulators, and maintaining the compliance of these regulations is an area where AI and machine learning can play their part. The cost of high-quality cameras is decreasing every year, and AI image recognition and processing software is also improving rapidly. Therefore, AI based detection methods are more and more attractive to enterprises.

Especially in the automotive industry, for example, the German carmaker BMW took the lead in adopting this technology. BMW uses the AI application as the last step in the inspection process to compare newly manufactured cars with order data and specifications. Nissan, another automobile manufacturer, has also made significant progress in integrating AI visual inspection model into its quality assurance process.

The growing popularity of visual inspection algorithms is partly due to the increasingly mature development of these algorithms. Now, neural network-based systems can identify various potential problems, such as cracks, leaks, scratches, warpage, and many other anomalies.

And the parameters to be checked by the application can be adjusted or adapted to a given situation according to complex rule mapping. When used with GPU and high-resolution camera, AI based detection solutions can greatly exceed traditional visual detection systems in accuracy and speed.

The future of manufacturing

From a certain point of view, the future of manufacturing industry is almost synonymous with the future of IOT based AI. In 2019, it is estimated that there will be 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 the manufacturing industry. It is expected that the valuation of AI in the manufacturing industry will increase by more than 15 times, from about US $1.1 billion at present to more than US $16 billion in 2026.

All the features 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.