Transporters find their way through factory halls on their own, plants optimize their power consumption during live operation, and machines perform quality-control checks – and make the necessary adjustments – while manufacturing is still in progress.
Artificial intelligence offers tremendous potential for the industry. It’s already making production more efficient, more flexible, and more reliable.
The industry is becoming increasingly digitalized, the digital enterprise is already a reality. Data is continuously generated, processed, and analyzed. The volumes of data in production environments are the basis on which digital representations of entire plants and systems are generated. These digital twins have been used for some time to structure the planning and design of products and machinery – and production operations themselves – and do so more flexibly and more efficiently while manufacturing high-quality, customized products faster and at an affordable price. But what would happen if the machines and processes could gather insights from these high volumes of data by themselves and optimize their processes during live operation? The potential would be enormous. The good news is that this can already be achieved, step-by-step, using artificial intelligence (AI).
AI and Industry 4.0
Big data and AI give Industry 4.0 a huge boost. Intelligent software solutions can use the high volumes of data generated by a factory to identify trends and patterns that can then be used to make manufacturing processes more efficient and reduce their energy consumption. This is how plants are constantly adapting to new circumstances and undergoing optimization. And as the level of networking increases, the AI software can learn to “read between the lines,” which can lead to the discovery of many complex connections in systems that aren’t yet or are no longer evident to the human eye. Intelligent software with sufficiently intelligent analytical technology is already available. But whether data processing is performed using a cloud solution or at the local level (for example, using Edge computing) will depend on the user’s requirements. Data on an Edge platform is available more quickly and at a higher resolution, whereas a considerable amount of computing power is available in the cloud. In many cases combining edge and cloud computing is required to benefit from both worlds.
MindSphere, the cloud-based, open IoT operating system from Siemens, can be used to link products, plants, systems, and machines. It is one of the most important foundations enabling the use of AI in industry. MindSphere performs extensive analyses to make the vast amounts of data generated by the Internet of Things (IoT) useful for optimization, simulation, and decision-making.
The digital twin enables virtual testing of a variety of scenarios and promotes smart decisions in areas such as optimizing production. In the future, using a digital representation of a machine tool and the associated manufacturing process, AI will be able to recognize whether the workpiece currently being manufactured meets quality requirements. Moreover, it determines the production parameters that need to be adapted to ensure that this remains the case during the ongoing production process. As a result, production is made even more reliable and more efficient and companies even more competitive.
Security is the highest priority
A precondition for both Industry 4.0 and for artificial intelligence is a state-of-the-art, end-to-end IT infrastructure, regardless of the size of the company. That’s the only way a business can become part of the digital future. But this must always be accompanied by an awareness that digitalization and cybersecurity need to go hand in hand. The risks are huge without the right safeguards in place. Comprehensive protection for industrial facilities will, therefore, play a key role in the future. After all, hackers are growing smarter all the time, and it is vital that companies stay ahead of them.
“It’s no use closing your eyes to the changes. We need to constantly keep up and try to influence the process.”
Professor Jan Madsen, DTU Compute