i4.0 technologies in labs – IIoT

Following the piloting process of Advanced Manufacturing Labs for H/VET through the Collaborative Learning Factory (hereafter CLF), the EXAM4.0 partners we have piloted 16 technologies embedded in Industry 4.0.

The following image shows the overall structure of the piloting process.

The difference between the Internet of Things (IoT) and its industrial version (IIoT) is that while IoT focuses on services for consumers, IIoT focuses on increasing security and efficiency in production centers.

Not all systems can be classified as IIoT. In general, it requires that they be networked systems that generate data for analysis and produce specific actions. The operation of IIoT systems is based on a layered structure:

• Devices. The visible part of the system are the devices: sensors, GPS locators, machines, among others.

• Network. Above it is the connectivity layer, that is, the network that is established between these devices and the servers through cloud computing or edge computing.

• Services. They are the computer applications that analyse and process the collected data to offer a specific service.

• Content. It is the interface with the human operator, which can be a computer, a tablet or even devices such as virtual reality glasses or augmented reality.

Among the applications that we can find in the IIoT are:

• Autonomous vehicles: The transport of components to the plant or to the warehou- se, can be carried out by autonomous vehicles that are capable of moving from one side of the factory to the other detecting obstacles.

• Machine performance optimization: An idle machine represents a loss of revenue. Thanks to sensors and data processing, it is possible to optimize machine uptime within a manufacturing plant. They can also detect times of use or errors for predictive and preventive maintenance.

• Reduction of human errors: Human operators will continue to be essential in many tasks, but the tools they use will be connected to the system, to save time and avoid errors.

• Improved logistics and distribution: The stored products incorporate sensors that provide real-time data on their location and even on their temperature or environmen- tal conditions.

• Decrease in the number of accidents: Wearables, such as glasses, bracelets or gloves, allow data collection from the operator who wears them – from their location or proximity to the machines to their pulse, temperature or tension – and thus reduce the possibility of accidents.

The CLF that is going to be launched has divided its production process into 4 stages (product design, process engineering, production and assembly) as can be seen in the following image. Within these stages, robotics is going to help in the process engineering, production and assembly stages.

Figure 6: Exam 4.o manufacturing process. Source: Author’s creation

The projects and services mentioned above will be used to connect machines in labs and collect data that will allow us to introduce process improvement iterations and share information between partners. This requires forming a new profile that is capable of managing both the installation and the development of the data collection and analysis ecosystem.

Based on our experience in this field, we will design a teacher training course focused on VET teachers with the aim of equipping them with technology to implement in the labs, but also allowing them to introduce this new knowledge as soon as possible in the classroom. In the pilot course, all the tools mentioned above will be taught to analyze the path traveled by the data from the production process (OT) to the cloud (IT).

There are huge benefits of adopting a fully connected IIoT manufacturing operation in the Labs. Some of these advantages are:

• Increase efficiency
• Reduce errors
• Improve security
• Reduce costs
• Share information

The greatest benefit of IIoT is that it provides the ability to automate and therefore optimize the operational efficiency of CLFs. Robotics and automated machinery can work more efficiently and precisely, increasing productivity.

Additionally, physical machinery can be connected to software through sensors that constantly monitor performance. This allows teachers and students to have a better understanding of the operational performance of the process.

Other advantages are:

• Data-driven decision making for all manufacturing functions.
• Performance monitoring from anywhere.

Among the competences that students will obtain working with IIoT platforms are:

• Obtain information to perform associated operations with the installation and commissioning of IoT systems.
• Configure the elements of the IoT system.
• Verify the operation of the IoT infrastructure, conducting functional tests on connected devices and systems, on site or remotely.
• Adapt to new work situations caused by technological and organizational changes in production processes.
• Identify the range of options for the way things can communicate.
• Select the most appropriate standards for building successful communications.
• Build addressing architectures that can scale to the required sizes.
• Analyse and record the interactions.
• Visualise the results of interactions.
• Deliver the security that modern services demand.
• Build new service networks that can support the future IIoT.