adminToday, most of the Giant industries, around 83 percent, believe that AI delivers better outcomes; however, only 20 percent have embraced this technology. It is pretty clear that a stronghold on the domain is important for successfully adopting artificial intelligence in the manufacturing industry.
Domain expertise is important for successfully adopting artificial intelligence in the manufacturing industry. Jointly, they form Industrial AI that uses machine learning algorithms in domain-specific industrial applications. AI can be potentially used in the manufacturing industry through machine learning, deep learning, and computer vision.
Let’s check out some of the critical needs in artificial intelligence technologies in the manufacturing industry to obtain a better picture of what one should do to keep the business up-to-date and seamless.
AI Is a Broad Domain
Artificial intelligence is not the correct way to describe all the technologies, and we’ll discuss that they have applications in manufacturing industries. AI is a big subject with different methods and techniques that fall under it.
There are robotics, natural language processing, machine learning, computer vision, and many other technologies that also need attention.
Keeping this in mind, let’s begin with artificial intelligence applications in the manufacturing industry. So here are some industrial uses of AI.
The Goal of AI in Manufacturing
Artificial intelligence studies how machines can process information and make decisions without human interference. The best way to understand this is that AI aims to mimic how humans think but not necessarily. We all know that humans are better and more efficient in performing certain tasks, and in certain tasks, they are not. The best type of AI is one that can think and make decisions rationally and accurately. The best way to explain this is that we all know that humans are not efficient enough to process data and the complex patterns that appear within large datasets.
However, AI can easily do this job using sensor data of a manufacturing machine and pick out outliers in the data that provide information about the machine that will require maintenance in a few weeks. Artificial Intelligence can perform this in a fraction of a human’s time analyzing the data.
Robotics: The foundation of Modern Manufacturing
Many applications of artificial intelligence include software in place of hardware. However, robotics is mainly focused on highly specialized hardware. As per Global Market Insights, Inc, the industrial robotics market is expected to grow more than $80 billion by 2024. In many factories, for instance, Japan’s Fanuc Plant, the robot-to-human ratio is approx 14:1. This reflects that its possible to automate a large part of the factory to reduce product cost, improve human safety and enhance efficiency.
Industrial robotics demands specific hardware and artificial intelligence software to assist the robot in accurately performing its tasks. These machines are specialized and are not in the business of making decisions. They can run under the supervision of technicians, and if not even, they make very few mistakes compared to humans. Since they make very few mistakes, the overall efficiency of a factory improves when integrated with robotics.
When artificial intelligence is integrated with industrial robotics, machines can automate tasks like material handling, assembly, and inspection.
Robotic Processing Automation:
Robotic processing automation, artificial intelligence, and robotics are among the most familiar. It is important to understand that this process is not related to hardware machinery but software.
It involves the principles of assembly line robots for software applications like data extraction, file migration, form completion and processing, and more. However, these tasks do not play very important roles in manufacturing; they are significant in inventory management and other business tasks. It becomes more important if the production operation requires software installations on each unit.
Computer Vision: AI Powering Visual Inspection
Quality control is the manufacturing industry’s most significant use case for artificial intelligence. Even industrial robots can make a mistake, though the possibility is less than humans. It can be a huge loss if a defective product reaches the consumer by mistake. Humans can manually monitor assembly lines and identify defective products, but no matter how attentive they stay, some defective products will always slip through the cracks. Therefore artificial intelligence can help the manufacturing process by reviewing products for us.
Adding hardware like cameras and IoT sensors, products can be interpreted by AI software to catch defects automatically. The computer can then automatically decide what to do with defective products.
Natural Language Processing: Improving Issue Report Efficiency
Chatbots driven by natural language processing is an important manufacturing AI trend that makes factory issue reporting and helps requests more efficiently. It is a domain of AI that specializes in mimicking natural human conversation. Suppose workers can access the devices to communicate and report their issues and questions to chatbots. In that case, artificial intelligence can support them in filing proficient reports more promptly in an easy-to-interpret format. It makes workers more accountable and decreases the load for both workers and supervisors.
Web Scraping:
Manufacturers can use NLP for an improved understanding of data collected with the help of a task called web scraping. AI can check online sources for appropriate industry benchmark information and transportation, labor, and fuel costs. It can help in boosting business operations.
Emotional Mapping:
Machines are quite poor when it comes to emotional communication. It is very challenging for a computer to understand the context of a user’s emotional inflection. However, natural language processing is enhancing this area through emotional mapping. This brings a wide variety of opportunities for computers to understand the feelings of customers and operators.
Machine Learning, Neural Networks, and Deep Learning
The three technologies used in the manufacturing industry are machine learning, neural networks, and deep learning, which are artificial intelligence techniques used for different solutions:
- Machine Learning: It is an artificial intelligence technique in which an algorithm learns from training data to decide and identify patterns in collected real-world data.
- Neural Networks: Using ‘artificial neurons,’ neural networks accept input in an input layer. The input is passed to hidden layers that increase the weight of the input and direction to the output layer.
- Deep Learning: It is a machine learning method where the software mimics the human brain like a neural network, but the information goes from one layer to the next for higher processing.
Future of AI in Manufacturing
What will be the next role of artificial intelligence in manufacturing? There are so many thoughts and visions coming from science and technology. The most visible change will be an increased focus on data collection. AI technologies and techniques used in manufacturing can do so much work independently. As the Industrial Internet of Things grows with increased use and effectiveness, more data can be gathered and then used by AI platforms to improve different tasks in manufacturing.
However, with the advancement in AI in the coming years, we may observe completely automated factories and product designs made automatically with less human interference. However, reaching this point is only possible through continuous innovation. All it requires is an idea- it can be the unification of technologies or using technology in a new case. Those innovations alter the manufacturing market landscape and help businesses stand out.