Industry 4.0

Digitalization and connectivity offer industry not only major opportunities, but also major challenges. The increasing degree of automation makes efficient, flexible and individual production environments possible in which the product maneuvers through the manufacturing process almost on its own. These transformations, often described as the fourth industrial revolution, are grouped in Germany under the term »Industry 4.0«. Several trends are associated with this development, including:

  • Flexible production approaches and batch sizes 1
  • Servitization and controls from the cloud
  • Increasing degree of connectivity and automation

The advantages of Industry 4.0: flexibility, automation and fault tolerance

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Modern industrial systems are considerably more complex than in years past. Today, machines are connected to one another and linked to large infrastructures. Companies are thus able to adapt their production processes to changing requirements in real-time, which improves efficiency.

Real-time data can be used to optimize the logistics systems and simplify collaboration with customers and suppliers. Using previously-collected and current data, algorithms calculate the ideal supply routes and optimize warehouse inventories, thus leading to the ideal flow of goods. Suitable interfaces enable simple collaboration with suppliers, logistics companies, manufacturers and customers.

Flexible production approaches enable mass customization

Industry 4.0 also makes »mass customization« possible, which refers to the mass production of special or custom-tailored products. Small-series and one-off products can be manufactured more cost-effectively with modern industrial systems. This type of production, which offers major competitive advantages and provides consumers tailored products at mass merchandise prices, can be deployed in a wide range of different industries. Examples include the automobile, food and textile industries.  

Servitization

One trend that Industry 4.0 enables is servitization, which refers to a new business model in which the product, a robot or machine component for example, remains the property of the manufacturer, who assumes responsibility for the service and maintenance. Utilization of the product thus becomes a service, which the customer make use of in a flexible manner. If the machine becomes idle because of a malfunction, the customer is not required to pay. This results in new demands on the serviceability and quality assurance of the machines.

Cloud controls

Cloud controls simplify data capture and data analysis. This allows service & asset management or maintenance to be carried out simultaneously on many machines at different locations. Cloud controls can also be used to set up cooperative and safety- or time-critical functions if the machine itself has limited resources. Even IP-protected code can be outsourced to the cloud instead of running in the machine.

In the future, the advantages of servitization and cloud controls can be combined in pay-per-use models, where cloud-based functions are activated, billed or even blocked down to the precise minute.

Connectivity and automation

Modern industrial systems feature an abundance of embedded systems. These so-called cyber-physical systems (CPS) can communicate with one another and adapt their behavior to each other. Through the use of state-of-the-art data processing technologies such as AI-based image detection, these systems can react automatically, or even autonomously, thus allowing the manufacturer to automate routine production steps and reduce costs. What makes these intelligent systems unique is that they can not only statically carry out predefined steps, but possess a certain amount of leeway to autonomously optimize their behavior. That means they can flexibly respond to the behavior of other machines and the factory environment.

One of the biggest challenges of this flexible behavior is maintaining a clear overview of the dynamic functions. However, an overview of the process chains is a prerequisite for the optimization of the value chains and the production systems.

Artificial Intelligence in Industry 4.0

Flexible workflows in automated industrial systems greatly increase the complexity. Conventional analysis and optimization methods run up against their limits in these environments.

By relying on machine learning methods and so-called data mining, the data from cyber-physical systems can be used to create a flexible finite state machine, which is an artificial intelligence (AI)-based model that not only represents a copy of the behavior, but the framework of the normal behavior.

With these finite state machines that represent process chains, contexts and dependencies that are too complex to identify with other methods become visible. With this approach, not only can the time behavior of individual machines can be observed, but the interactive behavior of entire systems. This digital counterpart of a physical system or machine is often described as a »digital twin«. More than just a copy of the previous behavior, it serves as a digital representation that accompanies the entire actual life cycle.

A monitoring instance then uses this model to observe the real system and analyze the entire production process. Latent behavior patterns that are learned from the data form the basis for comprehensive optimization and process automation.

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Complete overview in real-time with DANA

With its open analysis platform DANA, the Fraunhofer Institute for Cognitive Systems IKS created a tool that permits a connected industrial system to be monitored as an entire entity. This extendable tool platform helps to monitor and optimize the interaction between embedded connected systems.

Real-time analyses save time and reduce costs by detecting communications and interaction behavior anomalies in industrial systems at runtime. Machine learning processes are also deployed in this environment. The internal state of cyber-physical systems are analyzed via traces, allowing the overall behavior of the system to be observed. That means the optimal maintenance timeframe for individual machines can be anticipated and planned accordingly, thus avoiding system downtime, a strategy also referred to as predictive maintenance. DANA thus features automated debugging, which compares the specification with the implementation and detects discrepancies.

DANA also makes it easier to install new components into an existing system through a so-called »Design Automation« process that takes place prior to commissioning. Using a digital model, the interactive behavior of the planned constellation is analyzed in advance

This comprehensive analysis of the interactive behavior of the system is especially relevant with bottleneck analyses. In most cases, problems with availability in the supply and production chain are highly complex and the consequences costly. By using the DANA platform, processes can be optimized in order to reduce costs and optimize the utilization of resources.