How significant is AI's role in Industry 4.0?

Many examples exist to demonstrate the effectiveness of AI in Industry 4.0. It entails the newest revolution in manufacturing, so naturally advanced tech like AI will play a crucial role.

The concept of Industry 4.0 is making waves in the tech industry as manufacturing facilities seek to take advantage of technological advancements that make their operations quicker and more efficient. It hasn't achieved buzzword status among the general public just yet in the way that automation has, but there's no reason to believe the trend will slow down anytime soon.

Industry 4.0 has, since it was coined in 2011, been a catchall term for the wave of process and technology changes sweeping through the manufacturing world. It is somewhat interchangeable with Digital Factory and Smart Manufacturing and comprises a number of distinct changes to legacy manufacturing. In particular, these new factories and factory systems are more efficient because they are deeply instrumented, highly networked, broadly automated and completely data driven.

Why AI is an essential pillar of Industry 4.0

Industry 4.0 relies on a broad array of technologies, including robotics/cobotics, IoT, 3D printing, additive manufacturing, digital twinning and analytics. A digital factory is saturated with both intelligent instrumentation that watches or controls every aspect of production and highly granular data on everything from materials quality to sub-millisecond status updates on machinery.

Artificial intelligence, including machine learning and both generative and discriminative AI, can create compelling value in most technological facets of Industry 4.0. AI's value is most often derived from raising the bar for automation by bringing human-like levels of understanding to software. This reduces the number of places where humans have to evaluate information and make decisions during manufacturing, which (when executed well) both reduces costs and improves productivity. Machine-speed understanding can guide robot operations, for example, by slowing them down, speeding them up or modifying their behavior to deal with variations in raw material quality or in the speed of other parts of a process.

Beyond automation in production lines, AI still has myriad uses in this same type of environment. Firstly, it can aid in construction of digital twins, another technique for speeding up product development lifecycles. Secondly, AI systems aid in utilizing industrial IoT (IIoT) infrastructure, for example, by filtering event information to both spot and predict potential production problems based on sensor data. AI can also assist with production data analysis by uncovering previously unseen patterns in production and usage data and then using that information to suggest design or process changes.

AI use cases within Industry 4.0

Since AI can assist throughout an entire manufacturing process, many use cases for it exist within Industry 4.0 environments. At the beginning of a product's life, generative AI can assist with the design phase as well as with prototyping physical objects through 3D printing or computer-controlled machining and additive manufacturing. Generative AI systems can optimize designs to make them more efficient in terms of use of materials. For example, in clothing manufacturing, these systems control the cutting layout of clothing panels on bolts of cloth in a way that minimizes wasted fabric.

In other types of factories, AI systems can optimize for simplicity when manufacturing and assembling complex items by reducing the number of parts required for a design. Also, in order to concoct a design to speed up production, for instance, AI can reduce the number of separate cuts required to produce a finished chair leg on an autolathe.

Other concrete use cases in factories today include the following:

  • Nutella's use of generative AI to design millions of unique packages for its products;
  • in 3D printing, ADDMAN's use of hybrid modeling tools incorporating AI to design and prototype machine parts more efficiently;
  • FANUC's plant producing computerized numerical-control machine tools that learn from errors and improve control as they operate;
  • various plants controlling cobots that let humans work the production line without being physically present, especially in oxygen-free environments or extremely hot temperatures;
  • plants like 3B-Fibreglass monitoring machinery performance by speeding and slowing lines or operations in response to real-time events; and
  • factories like BMW using cameras and other sensors to monitor product quality and remove items as soon as faults are detected to save energy and material.

Computing power, that's available both on-site and in the cloud, will continue to increase, while AI algorithms and techniques won't stop maturing. Manufacturers know this, and understand they need the efficiency and responsiveness these tools offer to compete in the 21st century. As new manufacturers spring up and older factories retool for modernized processes, AI will continue to expand its role in Industry 4.0.

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