Applications and cases of artificial intelligence in Industry 4.0

Imagine a factory where machines can whisper to each other, predict malfunctions before they occur, and robots adjust tasks in real-time to manufacture customized cars. This isn't science fiction; it's the reality of Industry 4.0. Two years ago, I visited Siemens' Amberg Factory via VR and witnessed robots collaborating with artificial intelligence to assemble equipment with 99.9% precision. This isn't just manufacturing; it's a kind of magic. I had to dig deeper. So, I dived in—reading reports, browsing forums, and chatting with engineers to get the inside scoop. I immediately understood to what extent artificial intelligence was driving it all. Picture this: the machines in the factory no longer just blindly mass-produce products. They're sharp enough to spot brewing problems, adjust themselves, and make production run smoother than any human could. This is smart manufacturing, the core of Industry 4.0—transforming old-fashioned factories into interconnected, self-improving systems.

Artificial intelligence isn't just a trendy buzzword here. It's the real brain, capable of processing vast amounts of data, predicting malfunctions, and quickly creating customized products. Combine that with robots of astonishing precision, and you've got a whole new way of manufacturing.

Now that we understand the transformative potential of artificial intelligence, let's explore how these smart factories actually operate in practice—the place where the real magic happens.

How Industry 4.0 Works: The Combination of Artificial Intelligence and Automation

Industry 4.0 aims to connect physical machines with digital tools (artificial intelligence, sensors, cloud settings) and make them work together seamlessly. It's turning the traditional way on its head:

Real-time adjustment: Artificial intelligence keeps a close eye on every production process. If a machine malfunctions (e.g., a drop in speed or a rise in temperature), artificial intelligence steps in quickly, recalibrates in real-time, and restores normal operation. This ensures the continuous operation of the machines, minimizes disruptions, and maintains optimal production efficiency.

Predictive maintenance: Sensors cover the entire operation process, capturing every faint signal, while artificial intelligence filters out all the clutter. Strange buzzing or a sudden temperature drop? Artificial intelligence can detect it before any problem occurs and schedule maintenance to avoid any malfunctions.

Collaborative robots (Cobots): Robots can now be paired with humans to assemble circuits or mark defects. Guided by artificial intelligence, they can sense their surroundings and respond in real-time, creating a safer and more efficient workplace.

Customized operation: Need some unique features? Artificial intelligence can quickly redirect robots to smoothly switch from regular tasks to customized orders without any hitches.

Self-adjustment: Artificial intelligence can learn from small mistakes, such as misaligned parts or slight drifts, continuously improve itself, reduce waste, and immediately adapt to new requirements.

Smart decision-making: Artificial intelligence balances inventory, machine status, and deadlines to ensure efficient and on-time operations.

This integration doesn't just create smarter and more adaptable factories. It's not just about speed; it's about creating a future where humans and machines collaborate to achieve what was once out of reach.

While these technological capabilities sound impressive in theory, it's the real-world application in actual factories that reveals their truly revolutionary impact. Let's take a look at three companies leading this manufacturing revolution.

Real-world Applications: Three Outstanding Cases

Siemens in Amberg, Germany: Predicting Before Problems Occur

At Siemens' Amberg Factory, robots move purposefully under the guidance of artificial intelligence that can predict problems. In the spring of 2024, the factory's prediction system detected abnormal vibrations in a motor and triggered a preemptive replacement before any malfunction occurred. This foresight reduced quality inspection time by 95% because robots can now detect defects autonomously at a speed that humans can't match. As Maximilian Metzner, the global head of autonomous manufacturing at Siemens, said: "The future of artificial intelligence is incredibly exciting. We discover new functions almost every day and put them into practice as soon as possible, making our products better and more efficient."

While Siemens' approach is groundbreaking, they're not the only ones pushing the boundaries in artificial intelligence manufacturing. Across the Atlantic, another company is reshaping production methods and achieving equally impressive results.

Tesla in the United States: Accelerating Artificial Intelligence Manufacturing

Tesla isn't just assembling electric vehicles; it's revolutionizing manufacturing and transportation through advanced artificial intelligence. In its "smart factory," predictive maintenance based on machine learning has reduced unexpected machine malfunctions by over 30%, thereby reducing downtime and maintaining efficient production. Artificial intelligence-driven quality inspections use computer vision to detect microscopic defects—far beyond what the human eye can capture—ensuring stricter tolerances and top-notch vehicle quality.

Then there's the Full Self-Driving (FSD) setup—cameras all over the vehicle transmit data to a clear network, allowing the vehicle to drive autonomously. Tesla's battery optimization represents another major advancement, maximizing range efficiency while minimizing energy consumption through iterative improvements. With bold moves and an eco-friendly momentum, Tesla is shaking up the entire automotive industry.

While Western manufacturers continue to make remarkable progress, some of the most influential implementations of artificial intelligence in manufacturing are happening in Asia, and one company stands out for its comprehensive approach.

Haier in China: Achieving Customized Production through Artificial Intelligence Innovation

Haier's factories in China are a prime example of how artificial intelligence can transform factories into living, intelligent systems. At Haier's Hefei Factory (one of the global lighthouse factories in China), artificial intelligence isn't just a tool; it's the pillar of a revolution. Here, the artificial intelligence system has reduced the defect rate by 58%, increased per capita efficiency by 49%, and lowered the manufacturing cost per unit by 22%. Meanwhile, robots and cameras can handle tasks with a precision that humans can only dream of. But it's not just about speed—these intelligent systems reduce 447,600 tons of carbon dioxide emissions annually, replacing guesswork with big data and ant colony optimization algorithms to improve every move. Hefei was named the world's first lighthouse factory for household central air conditioners in 2023, demonstrating how artificial intelligence is redefining production.

This isn't an isolated case. Haier's "Smart Home Brain" integrates over 140 product categories and 130 million devices into an artificial intelligence-driven ecosystem, proving that smart factories can provide data for smarter homes (Forbes, September 13, 2024, forbes.com). In July 2024, Haier's HomeGPT received a Level 4 rating from the China Academy of Information and Communications Technology, enabling home appliances to make offline decisions with astonishing precision—a leap that marks the arrival of the Industry 4.0 era. This is artificial intelligence at work: it's not just manufacturing home appliances; it's building the factories of the future.

These three pioneers show us the current state of artificial intelligence-driven manufacturing, but what's truly exciting is what the future holds as these technologies continue to evolve and spread across industries worldwide.

What's Next: Progress or Challenges?

This is just the beginning. Artificial intelligence will soon be able to design during the product production process—Toyota is conducting experiments in this area, sketching during the production of automobile parts. South Korea will invest $2.2 billion in building automated factories by 2028. The potential is huge: imagine a world where humans focus on creativity and strategy while machines handle execution in real-time.

But questions remain: Can workers adapt quickly? Is this progress or a dilemma?

The biggest challenges faced by smart factories range from adjusting the workforce to dealing with cybersecurity threats, managing data responsibly, and addressing ethical issues. There are still many questions to be answered.

This article is from the WeChat official account "Data-Driven Intelligence" (ID: Data_0101), author: Xiaoxiao, published by 36Kr with authorization.