photo: Siemens/Digital transformation is a question of the survival of the Czech industry. What does Tomáš Duba have to say about it?
Implementing digital production means, among other things, thinking about the future. Primarily, the world's major players determine it. Tomáš Duba, Director of the Motion Control unit at Siemens, explains in detail what concept of digitalization Siemens is creating for the future.
Let's imagine a modern automated line, imagine the entire production process step by step, and see how digitalization can increase the success rate of each stage.
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What tools does Siemens offer for fast production start-up and optimal set-up of the production line?
We start from the end (laughs) because the start-up of production is only one of the last steps in the entire production cycle from its point of view. And for the production start-up to be successful and in the shortest possible time, I think it is most crucial that there is a crossover of ideas between the technology supplier (line manufacturer) and its future user (end customer). It is not easy for the user to correctly define his ideas and requirements for the new equipment. And it can be equally difficult for the technology manufacturer to understand these requirements. It is where digitalization comes to the fore. A digital twin is perfectly suited to ensure that the manufacturer and the future user understand each other as well as possible. And not just one, but three.
Alright, which is first?
At the heart of it, all is the product's digital twin. Imagine, for example, the drone that many of us use to film the landscape. What might have preceded it before we got our hands on it? When I want to make a drone, I have to start with the design of the product and the creation of its digital twin. I don't mean a 3D model in a CAD system. To successfully launch a new type of drone, you need to start with optimizing its design, including simulations of its behavior in a real environment, and that's what its digital twin is for. It is the only way to test its future flight characteristics by simulating flow (airflow), mechanical and thermal stresses. Thanks to this simulation and optimization, its topology can be modified. If we were to start straight away with production planning and skip product design and optimization, we would significantly limit the future performance of the drone. The manufacturing technology has to be outlined only following the designed materials and their dimensional arrangement. In fact, we may choose such a progressive drone profile that it cannot be manufactured with standard technologies. We might solve the production by 3D printing composite materials, or we might have to take a step back and change the design.
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We have the product designed and fine-tuned. What's next?
Nowadays, we can move on to the digital twin of a production machine, a line, or even an entire factory. The principle is still the same. The only difference is scalability. Based on the product's digital twin, we start designing and optimizing the production process. The result is a digital twin of a production machine that saves its manufacturer considerable commissioning costs. In fact, it can simulate the machine's behavior itself long before it starts physical production. Any possible error in the machine's design is detected and corrected in the virtual environment. It leads to a reduction of up to 30% in the time to market of a new production machine.
What about the third digital twin?
To successfully launch each new product in the shortest possible time and at the lowest possible cost, a digital twin of production is still needed. Only then will the full circle of the three digital twins be complete. Essentially, we start virtual production without even digging in the ground and starting to build a new production line. We take the digital twin of the product, put it into the digital twin of the production machine or the entire line, and start the digital twin of the production process. We're virtually producing a virtual product on a virtual machine, but that makes a huge difference.
Thanks to all this, the end-user, who is also an investor, is sure that they are buying production technology that exactly suits their needs. What's more, they can simulate the production times, the tact of the entire production line, the fixtures, tools, and energy used and thus calculate the return on their investment. That makes sense, doesn't it?
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Quality control is particularly important during the start-up phase of production, how prepared is Siemens for this phase?
If we have all three of the digital twins described earlier, it doesn't get any easier. After all, the digital twin is the standard to which we can always return and measure the deviations of the real environment against the ideal values of the digital normal. Based on the growth trend of these deviations, we can monitor what is happening to our production line and thus intervene in time.
This has two levels. Firstly, the viewpoint through our own production: we monitor compliance with production procedures, production quality, and the quality of the product produced.
The second angle is focused on the production machine or the entire line. We monitor the condition of our production technology. By measuring the deviation against the digital twin of the production machine, we have an overview of the state of the machine. We can anticipate the occurrence of a possible fault and thus intervene in time to prevent production downtime and damage to the machine.
The line is running and everyone is striving for the shortest cycle times. What will you offer to increase production speed?
By closing the circle of the production cycle, we are back to square one and can proceed to a new optimization. We simply take the current production data and use it as input to re-optimize the entire production cycle. This gives us a never-ending process of continuous improvement of the quality of the product itself, as well as its production costs and times.
For example, if an end-user operates the same production lines on different continents, they can use data from one location to improve the production parameters of all other plants. It makes the improvement process even more powerful. Without digital twins, it would be extremely difficult to determine what the optimal state is and what corrections to take to get closer to it faster.
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Even if predictive maintenance solves many problems promptly, it can always happen that an unexpected failure occurs. What digital tools will Siemens offer for fast service?
We offer remote service, where the machine manufacturer simulates on its digital twin a fault that occurred in an actual environment at the end-user. The line manufacturer thus works on repairing the machine in its own facilities while it is physically installed, for example, in the opposite hemisphere. It's similar to the story of Apollo 13 that got the call: "Houston, we have a problem!" and Houston solved the problem.
Energy is a big issue at the moment. Do you have the tools to monitor and reduce it?
Of course, we have tools to monitor energy consumption, optimize production and, therefore, reduce it across the portfolio. But I would prefer to go back to the beginning of our conversation regarding the drone example. If we optimize the design of a product for the efficiency of its features and the productivity of its operation, we can save much more in the end than simply monitoring our existing production technology to reduce its energy consumption.
Are you suggesting a different path?
My answer is a continuous process of innovation in the production cycle and investment in new advanced technologies and, of course, in quality employees. We must not forget them because even the most advanced technology cannot do without passionate people who are not afraid to take risks in new and unprecedented ways. We are facing an intergenerational renewal as the average age of the technical workforce is constantly increasing. Yet technical schools, apprenticeships, and universities have the opportunity to offer precisely what many of today's companies in the industry so desperately lack. That is, skilled technicians who are open to new approaches, think critically, and are interdisciplinary, including in the field of IT.
Is all this already in place in any Czech plants? At what stage of digitalization is our industry now? When do you estimate, for example, that all the tools mentioned above will be standard for every company?
Of course, we do, and we are delighted and often organize expert seminars in these plants so that we can show our customers in practice what digital transformation can bring them. I would mention here, in particular, the so-called Digital Arena at the servo motor plant in Bad Neustadt, Germany. But our Czech motor plants in Mohelnice and Frenštát are also digitalization showcases.
When it comes to mass implantation data across industrial plants in the Czech Republic, it is much more complicated here. Behind every transformation, there must be a clear and long-term management vision, which is not to be discounted even after the obstacles that arise during implementation. And this is what I find completely lacking in most industrial companies, although there are, of course, exceptions. Being an optimist by the foundation, let's say, I expect mass deployment within two years. It is a question of the survival of our domestic industry.
Source: Strojirenstvi.cz
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