Among large industrial companies, 83 percent believe AI produces better results—but only 20 percent have adopted it, according to The AspenTech 2020 Industrial AI Research. Domain expertise is essential for the successful adoption of artificial intelligence in the manufacturing industry. Together, they form Industrial AI, which uses machine learning algorithms in domain-specific industrial applications. AI can be leveraged in the manufacturing industry through machine learning, deep learning, and computer vision.
Let’s explore some of the important trends in artificial intelligence technologies in the manufacturing industry to get a clearer picture of what you can do to keep your business up to date.
AI Is a Broad Domain
For all of the technologies that we’ll discuss that have applications in manufacturing industries, artificial intelligence is not the most accurate way to describe them. AI is a very broad subject that has many different methods and techniques that fall under its scope. Robotics, natural language processing, machine learning, computer vision, and more are all different techniques that deserve a great deal of attention all on their own.
Keeping that in mind, let’s talk about the many applications of artificial intelligence in the manufacturing industry– here are some industrial AI use cases.
The Goal of AI in Manufacturing
Artificial intelligence studies ways that machines can process information and make decisions without human intervention. A popular way to think about this is that the goal of AI is to mimic the way that humans think, but this isn’t necessarily the case. Although humans are much more efficient at performing certain tasks, they aren’t perfect. The best kind of AI is the kind that can think and make decisions rationally and accurately.
Probably the best example of this is that humans are not well equipped to process data and the complex patterns that appear within large datasets. However, AI can easily sort through sensor data of a manufacturing machine and pick out outliers in the data that clearly indicate that the machine will require maintenance in the next several weeks. AI can do this in a fraction of the time that a human would spend analyzing the data.
Robotics: The Keystone of Modern Manufacturing
Many, if not most, applications of artificial intelligence involve software instead of hardware. However, robotics is primarily focused on highly specialized hardware. The manufacturing industry utilizes this technology a great deal for many different types of applications. According to Global Market Insights, Inc, the industrial robotics market is forecasted to be worth more than $80 billion by 2024. In many factories, such as Japan’s Fanuc plant, the robot to human ratio is about 14:1. This shows that it’s possible to automate a great deal of a factory to reduce product cost, protect human workers, and achieve higher efficiency.
Industrial robotics requires very precise hardware and most importantly, artificial intelligence software that can help the robot perform its tasks correctly. These machines are extremely specialized and are not in the business of making decisions. They can operate supervised by human technicians or they can be unsupervised. Since they make fewer mistakes than humans, the overall efficiency of a factory improves greatly when augmented by robotics.
When artificial intelligence is paired with industrial robotics, machines can automate tasks such as material handling, assembly, and even inspection.
Robotic Processing Automation
A term that often gets thrown around related to artificial intelligence and robotics is robotic processing automation. However, it’s important to note that this is not related to hardware machinery and is instead related to software.
Robotic processing automation is all about automating tasks for software, not hardware. It applies the principles of assembly line robots to software applications such as data extraction, form completion, file migration and processing, and more. Although these tasks play less overt roles in manufacturing, they still play a significant role in inventory management and other business tasks. This is even more important if the products you are producing require software installations on each unit.
Computer Vision: AI Powering Visual Inspection
Within the manufacturing industry, quality control is the most important use case for artificial intelligence. Even industrial robots can make mistakes. Although these are much more infrequent than humans, it can be costly to allow defective products to roll off the assembly line and ship to consumers. Humans can manually watch assembly lines and catch defective products, but no matter how attentive they are, some defective products will always slip through the cracks. Instead, artificial intelligence can benefit the manufacturing process by inspecting products for us.
Using hardware like cameras and IoT sensors, products can be analyzed by AI software to detect defects automatically. The computer can then make decisions on what to do with defective products automatically.
In the video below, you can learn more about MobiDev’s approach to AI-based visual inspection system development.
Natural Language Processing: Improving Issue Report Efficiency
Chatbots powered by natural language processing are an important AI trend in manufacturing that can help make factory issue reporting and help requests more efficient. This is a domain of AI that specializes in emulating natural human conversation. If workers are able to use devices to communicate and report the issues and questions they have to chatbots, artificial intelligence can help them file proficient reports more quickly in an easy to interpret format. This makes workers more accountable and reduces the load for both workers and supervisors.
Manufacturers can leverage NLP for better understanding of data gained with the help of a task called web scraping. AI can scan online sources for relevant industry benchmark information, as well as costs for transportation, fuel, and labor. This can help optimize the entire business’s operations.
Machines are far behind humans when it comes to emotional communication. It’s very difficult for a computer to understand the context of a user’s emotional inflection. However, natural language processing is improving this area through emotional mapping. This opens up a wide variety of possibilities for computers to understand the sentiments of customers and feelings of operators.
Machine Learning, Neural Networks, and Deep Learning
These three technologies are artificial intelligence techniques utilized in the manufacturing industry for many different solutions.
- Machine Learning: an artificial intelligence technique where an algorithm learns from training data to make decisions and recognize patterns in collected real-world data.
- Neural Networks: using ‘artificial neurons’, neural networks receive input in an input layer. That input is passed to a hidden layer that assigns weight to the input and directs this to the output layer.
- Deep Learning: a method of applying machine learning where the software emulates the human brain just like a neural network, but information passes from one layer to the next for higher processing.
Machine learning is a huge trend in manufacturing, and we have an entire blog post about machine learning’s applications in the manufacturing industry that you should read if you are interested in how ML is fundamentally changing the way that manufacturing operates. These industrial AI use cases show how we can leverage AI in the manufacturing industry in 2022.
Future of AI in Manufacturing
What comes next for artificial intelligence’s role in manufacturing? There are many thoughts about this, some coming from the realm of science fiction and others as extensions of technologies that are already being utilized. The most immediate noticeable evolution will be an increased focus on data collection. AI technologies and techniques that are being employed in the manufacturing sector can only do so much on their own. As Industrial Internet of Things devices increase in popularity, use, and effectiveness, more data can be collected that can be used by AI platforms to improve various tasks in manufacturing.
However, as advances in AI take place over time, we may see the rise of completely automated factories, product designs made automatically with little to no human supervision, and more. However, we will never reach this point unless we continue the trend of innovation. All it takes is an idea– it could be a unification of technologies or using a technology in a new use case. Those innovations are what transform the manufacturing market landscape and help businesses stand out from the rest.