The fourth industrial revolution
In the recent decade we could observe a nearly complete digitalization of almost all aspects of our daily life. Affordable personal computers were a solid foundation of that process, however the change in the way we live today was significantly accelerated by another device – the smartphone. The ability to be continuously connected to the internet from any part of the world allowed us to reach across the globe faster than ever in a convenient and effortless manner. Digital communication and services are nowadays taken for granted and it seems difficult to imagine how could life possibly look without them. This disruption forever changed not only us but also enterprises which needed to adjust their business models to the rapidly changing world. The new reality generated many new opportunities, never known before, but also sieved the losers. In all this pacing race of technologies some industries stayed a little bit aside, one such example being the manufacturing sector which was truly lagging. Manufacturing industries were always more traditional. Reliability, safety and standardization were priority, therefore innovation and disruption did not take place here too often.
Automatization, which was industries last revolution, left us with efficient high volume production system. The current tendency for product customization and high variability coming from demand oriented, flexible production showed that our present way of looking at manufacturing might not be enough and we need to make another leap to match growing expectations. Industry 4.0 is just a catchy phrase which tells us, that for manufacturing time to change has finally come.
What is industry 4.0 all about?
If to be answered with one word, it would be about connectivity. Of course there is more into that but connecting industrial machinery to each other and to the cloud is one of the main pillars of industry 4.0. Information will flow with a broad stream from factories to the cloud. There, data will be appropriately clustered by various algorithms and finally processed to obtain synthetic and useful information that might be of interest to many. On one side, users will obtain great insight into their manufacturing systems, knowing how orders of their customers are being processed, what is lagging, what is lacking, what is the health condition of their machines and what are the upcoming actions to be taken in order to keep things going smoothly. A complete set of information about the state of a manufacturing system will certainly improve communication along the supply chain by going beyond what is possible with currently available Supply Chain Management Systems (SCMS). Another added value of industry 4.0 is that machine builders will also be able to access information about how their hardware is being used, what often is currently missing. Collected reliability data will be a motor for improvements that will surely extend lifecycle of the machines. Information of machinery utilization can also bring benefits in terms of energy efficiency, as knowing with greater detail and resolution how manufacturing process is managed will allow to implement the application of tailored energy-saving strategies. Machines equipped with smart predictive maintenance algorithms will advise in advance both user and machine builder about incoming need for part replacement.
Where will all this data come from?
There are numerous potential sources of data. In modern manufacturing systems there are already thousands of data samples traveling through a closed internal network infrastructure via a number of different industrial communication links. These data samples come from various independent sensors or internal sensors of machines. However, some quantities cannot be simply measured with a sensor or it would be expensive/impractical, an example being reliability or wear. Researchers of the field believe that implementing the so called cyber-physical twin of device may be a solution. The concept is actually quite simple. Each machine will have its virtual, mathematical model that represent the key features, properties and functionalities of the machine. As the real machine performs actions, the digital twin is fed by information coming from the machines sensors and logic, virtually replicating its actions. Well aligned twin can reveal, through the state variables of the internal mathematical model, desired but practically unmeasurable quantities.
Straight to the cloud
The goal of industry 4.0 is to redirect parts of these data streams through an internet gateway straight to the cloud (with obvious security protocols and respecting privacy rules), where they will be elaborated by some powerful big data algorithms. Not only whole manufacturing systems are a good source of information – any standalone machine, device or sensor can be directly connected to its cloud storage, forming the so called Internet of Things (IoT). We estimate the size of IoT will grow to around 20-30 billion devices by the year 2020. Such architecture is very flexible as it can benefit not only from highly concentrated networks of manufacturing systems but also in industries with distributed resources, such as farming.
Reading about industry 4.0 one can think that in the fully automatized and smart manufacturing of the future, human being has been completely removed from the equation. Nothing could be further from the truth. Demand for mass customization will leave far behind soulless production lines making millions of copies of identical products. Human, with its excellent physical agility and unprecedented perception and decision making skills is going to be one of the key element of the next generation of manufacturing systems. Operators will work hand in hand with machines, sharing their physical space with robotic arms. In order for this cooperation to be successful it will be essential to exchange information between humans and machines. The operator himself can be considered a beacon of information, passing his synthetic yet elaborate commands and feedback to the cloud.
Industry 4.0 and thin clients
This sort of communication requires a flexible interface and one great example are HMI panels run with a thin client. These lightweight, cost efficient and yet functional devices allow user to pass their communication through a touch panel to a server and quickly receiving feedback from it. User can send commands, failure reports or anomalous behavior, request for assistance or ask for instructions or technical documentation required to smoothly complete his tasks. Analyzing cloud stored history of his requests may be used to improve his working environment and personalize given tasks.
In the light of what the industry 4.0 is becoming, smart devices play an important role inside factories. As a matter of fact, the number of smart devices needed from factories is increasing as they need to face challenges like minimizing downtime, reducing maintenance and management costs, as well as ensuring reliable and secure systems. Challenges that can be easily overcome by choosing reliable thin clients, which can help the IT management in improving overall company performances. Furthermore, other important advantages that thin clients can provide to factories we can find in their simplicity of use: they are really easy to set up, configure, as well as to manage, characteristics that are becoming more and more important in factories nowadays. But benefits of these devices go far beyond this. Inside factories, in many cases devices are used in extreme environments or in really dusty ones, conditions in which PCs would be less performant, but in which thin clients fans will not be blocked, as long term hardware. Last but not least, if used with the automatic configuration, thin clients can also assure business continuity of the machine in case of need.
The author: Jeremi Wójcicki
Jeremi Wójcicki obtained a master degree in mechatronics at University of Science and Technology (AGH) in Cracow, Poland in 2012 when he was working in a commercial research lab, as younger R&D specialist. In December 2014 he was hired as research fellow at the Institute of Industrial Technology and Automation belonging to the National Research Council in Italy and simultaneously he is following a PhD programme at Mechanical Department of Polytechnic of Milan. In years 2013-2016 he collaborated within the European funded project EMVeM “Energy Efficiency Management for Vehicles and Machines”, aiming at reduction of environmental impact of manufacturing in Europe. His research concerns sustainable manufacturing and focuses on aspects regarding energy efficiency of machine tools and discrete manufacturing systems. In his research he uses model-based methods for energy consumption estimation and smart, wireless sensing for cyber-physical systems identification.