AI is not only useful in manufacturing to automate processes, but also to connect them: analyzing data, identifying patterns, and making decisions based on them.
A modern manufacturing environment can involve various interconnected machines, equipment, and processes. Many can benefit from generating some form of operational data and providing valuable insights based on them.
However, data collection is usually only the first step in the process. The real engineering task is to create a system that makes the best use of data to create valuable information.
Here are several examples of how AI is used in manufacturing.
- Predictive Maintenance
Unexpected equipment failure can halt an entire production process.
Predictive maintenance uses information from various sensors and machines to identify patterns associated with abnormal behavior. By analyzing them, it becomes possible to detect incipient problems and take action before they occur.
A very simple workflow for such a system can look like this:
Machine → Sensor → Predictive maintenance → Maintenance
It is important to note that such a system involves not only the choice of an appropriate machine learning model but also the study of sensor characteristics, training data, feature selection, false positives, and feedback from maintenance.
- Computer Vision
One of the areas of computer vision is the analysis of images of manufactured products to detect defects.
In this case, the system can take pictures of the product and analyze them with computer vision algorithms to select those that require additional inspection or take other actions:
Camera → Image processing → Computer vision → Decision
The model can classify images of products and identify those that require closer attention. In addition, such an algorithm can be applied in cooperation with human experts who examine suspicious products.
- Data About Production
Various data can be generated in the course of production: information about the state of machines, production schedules, materials, and much more. Such data can be useful in making business decisions and optimizing the production process.
Many of these data sets are typically related, allowing you to train machine learning models that can find patterns and help make decisions. However, working with such models always involves working with databases, systems, and other software.
The structure surrounding the model can be as important as the model itself.
- IoT and AI
IoT technology can help create an ecosystem in which various objects and devices interact with each other. Such a system can collect a lot of data, after which AI can analyze and find correlations that are not visible at first glance.
A model of the AIoT ecosystem can look like this:
Physical objects → Sensors → Connectivity → Data processing → AI/ML → Application → Decision → Action
Each stage has its own challenges and activities:
• Physical objects: sensors, actuators, smart devices, and robotics;
• Sensors: collect and transmit information about the state of objects;
• Connectivity: transfer data through communication channels;
• Data processing: prepare and analyze data;
• AI/ML: analyze data sets to detect patterns;
• Application: provide an interface for viewing and interpreting data;
• Decision: make decisions based on data;
• Action: implement these changes in the physical world.
Such an ecosystem can be useful in many areas of manufacturing. Moreover, the architecture of such a system is actively explored in the article AIoT and Physical AI architecture .
- Data Quality
One of the main challenges in applying machine learning in manufacturing is working with data. Although there are many interesting machine learning algorithms, real-world applications are more often hampered by the peculiarities of data sets: their structure, quality, format, and much more.
Before using such data in a machine learning model, it is necessary to study and, where necessary, improve:
• Data collection and validation;
• Data cleaning and preparation;
• Selection of features and their formatting;
• Storage and data visualization;
• Training and validation of a machine learning model;
• Testing and applying a trained model;
• Work with a model in production.
These steps are extremely important because even the best machine learning model cannot solve all the problems if the input data are of poor quality. Thus, data processing and transformation should be seen as a critical element of the AI system.
- Contextualization
The context in which the work of machine learning models falls under one of the main challenges in applying AI to manufacturing. For example, if an anomaly detection model sends out an alert, an engineer must understand what factors could have contributed to it.
It is important to remember that an isolated machine learning model typically does not have enough information to make the right decision. This is why such systems are most often used in conjunction with human expertise.
A useful approach to solving such problems is to make the information coming from machine learning models more understandable and provide decision-making support.
From Models to Engineering Challenges
An interesting feature of manufacturing and industrial AI is that the most interesting challenges are related to connecting a machine learning model to the real world.
Thus, a complete chain can include many different stages, for example:
Sensors → Connectivity → Data → Processing → AI → Application → Human or Machine Decision → Action
Each of these stages has its own challenges, from weak sensors and poor data to the threat of data interception and inadequate reaction time. In addition, data processing and transmission methods are crucial to achieving the desired results. Thus, the current system architecture also needs to be considered.
A Practical Question
A useful question for developers and manufacturers to ask themselves is not only where they can implement AI but also which manufacturing problems they want to solve and what data they have for this.
It is always important to keep in mind what goals the AI system should meet and how it can achieve them. Only then, after determining the problem, it is possible to select the tools for its solution, whether it is neural networks or traditional data analysis.
AI can be used in many areas of manufacturing, from predictive maintenance to quality assurance of products. However, for the most useful application, it must be combined with proper data processing, the choice of a good model, and critical thinking about the obtained results.
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