Industry 4.0 Here and Now
The digital factory may seem like a far off place but the technology can provide value today.
The concept of Industry 4.0 (I4.0) has been around for a few years now, but it’s only been in the last 18 months where there has been a significant acceleration in communications, whitepapers, products and articles. It’s the main focus of every industry event and tradeshow, and it’s the front and centre marketing topic for the majority of automation technology providers.
There seems to be a disconnect, however, between that hype and real-world manufacturing operations. The perception is that Industry 4.0 is just something for the future. The reality is that it can provide OEMs with competitive advantage, and help manufacturers respond to demand and decrease costs today.
Industry 4.0 encompasses a lot of different technologies, but let’s focus on four areas that can be used right now to move a company’s automation engagement towards that futuristic Smart Factory concept, while benefitting in the meantime. These four areas are connectivity, modularity, end-to-end engineering, and people, key points along the path to successful I4.0 adoption.
If an automation device is connected to the Internet, it becomes an Internet of Things (IoT) device. And it can provide data or Big Data, which can be used by ERP, MES or in analytics for the purpose of machine learning. It’s all acquired in real time, so the data can be acted upon dynamically. But data is just data unless it tells you something. Useful data can be turned into information, and information about an operation can be very valuable.
Business decisions can be made in real time; energy usage can be monitored and optimized; and predicative maintenance can be implemented. An example might be an electric actuator: The motor current will be proportional to the forces required to move the actuator. Over time the current profile will change as the bearings wear. Using machine learning algorithms may help the operator identify when the bearings will fail. Maintenance can be scheduled beforehand and just-in-time. Spare parts can be ordered as they are needed rather than stockpiled.
To facilitate that kind of data flow, OPC UA is becoming the de facto standard for connecting machines to each other and to the Internet. Data can be transferred between two systems with completely different control architectures. Data can be accessed from intelligent devices but also from simple devices like sensors or actuators. This enables IoT, implements the idea of the cyber physical system and achieves the vertical integration of manufacturing operations.
Cloud computing providers are increasingly supporting OPC UA connectivity, allowing automation devices to be connected easily. Microsoft’s Azure IoT suite is an example, with quick connectivity and pre-configured solutions for common requirements like remote monitoring, asset management and predicative maintenance. Automation data can be processed now by advanced analytics software, running on powerful cloud computers where valuable insights about an operation can be identified.
Connecting information on the web requires knowledge of what the information is and how it should be displayed, which is one reason for metadata. The same requirements are being put in place for IoT. In the manufacturing world, devices need to be identified for their function and the data they will provide. The idea of “plug and produce” capability can be realized more effectively by implementing this method of identification rather than having every system preprogrammed to understand every possible device.
The I4.0 component concept is an element of RAMI 4.0 (Reference Architectural Model for Industry 4.0). The model’s purpose is to illustrate the connection between IT, manufacturing, product life cycles (through a three-dimensional space). This provides a means of reference when discussing I4.0 architecture, the design of automation products and for engineering manufacturing systems.
The I4.0 Component is a real object in a production environment with an administrative shell to provide a virtual representation of all information and services available. The real object could be an individual component or an entire production system. Devices (or automation modules) can then be plugged into systems and automatically recognized for their function and data.
Connecting the factory floor to the enterprise provides real time information, allowing dynamic decision-making to take place. But taking action requires automation that is capable of responding.
Modularity can provide agility. If a system can be organized into sub modules, then they can be scaled up or scaled down as production requirements change. That could be from a functional or a production capacity standpoint. Single, customized parts could be manufactured efficiently and profitably. Disparate part types could be produced on the same line, even at the same time, using only the functionality required for that given workpiece. This idea is often referred to as the Lego principle – being able to build up or build down with the versatility and simplicity (standardized connections) of Lego blocks.
If systems are to be designed as a collection of modules, then there are a few considerations to take note of. In addition to a control fieldbus or an industrial Ethernet, the modules must have a common connection method like OPC UA for vertical integration. Plug and produce functionality is essential. And smarter diagnostics will be required because of the increased complexity that modularity adds to the automation system. The modules then become cyber physical systems, the core of which will have some form of intelligence.
The challenge of engineering a system of automation modules that maintain their value is understanding the potential requirements of the future. A certain synchronicity then is required in order to align product development with automation and the manufacturing operation.
Systems can be designed with more versatility and purpose by connecting the product’s development into an integrated process for automation engineering. Costs could be reduced through object-oriented design and virtualization, the value life of manufacturing systems could be increased, and the products themselves could be brought to market faster.
AutomationML (Automation Markup Language) is a data format based on XML. It’s used for the storage and transport of automation system information, and the intent is to connect heterogeneous engineering tools for product design, factory topology organization, mechanical engineering, electrical engineering, PLC programming, and HMI development. The amount of repeatable information that needs to be entered into the different tools is loaded only once because everything is connected throughout the entire process.
With AutomationML, physical and logical components of the automation system are stored using an object-oriented approach. An object could be an articulated robot, for example, with sub-objects for end of arm tooling and more sub-objects for grippers and sensors. All the mechanical and electrical designs are included in the object as well as its PLC programming.
The information from AutomationML can also be used in virtualization software to build a virtual copy of the automation system and begin PLC integration before the actual mechanical assembly begins. Application and performance issues can be identified before expensive parts are manufactured; and the PLC program can be debugged and functional by the time the system is ready for power up. And the objects used in the design can be archived, readily available for future projects.
AutomationML can also be combined with OPC UA to connect the engineering data to the physical machine. Now changes in the engineering data can be automatically recognized by the system, allowing more dynamic and reconfigurable operations. The workpiece, for example, has a particular geometry. A handling system moves that workpiece at some point along the automation. If the geometry of the workpiece is changed or redesigned, the AutomationML file is updated, and the handling system automatically adjusts for the new dimensions.
End-to-end engineering along with connectivity provide a means to implementing the promise of Industry 4.0, but it will require people with the knowledge and the skills to actually realize it.
New technologies and manufacturing principles are being developed every day. It is critical that partnerships are created between industry and educational institutions to benefit from their full potential. How can Industry 4.0 factories be designed? How can legacy systems be connected? How does the manufacturing worker operate equipment? How do we improve the engineering process? Technology is only useful if it can be implemented, and implementation requires education.
Human labour is not eliminated in Industry 4.0, but redefined. People are needed to design systems and automation products, develop software for control systems and communication, operate and maintain new technologies, and create machine learning algorithms for manufacturing operations.
The automation of Industry 4.0 can exist today, and we can start piecing it together to move towards a manufacturing economy that can meet the demands of the modern world while remaining profitable and productive.
Ben Hope is the Technology Driver for Advanced Manufacturing and Industry 4.0 at Festo Canada.