Digital Twin - an element that has been sorely missing! The era of transformers: “digital twins” are already here New approaches to work

Perhaps, anyone who watched the Terminator films or The Matrix wondered when artificial intelligence will become a part of our daily lives, and whether people and robots will be able to coexist in peace and harmony. This future is much closer than you think. Today we will tell you about such technology as “ digital twins", which is already widely used in industry and, perhaps, will soon become part of our everyday life.

Who are digital twins?

It is a mistake to believe that the term “digital twins” refers to robots and artificial intelligence in the guise of some kind of humanoid creature. The term itself is currently applied mostly to industrial production. The concept of “digital twins” first appeared in 2003. The term came into use after the publication of an article by Michael Greaves, professor and assistant director of the Center for Lifecycle Management and Innovation at the Florida Institute of Technology, “Digital Twins: Manufacturing Excellence Based on a Virtual Prototype Factory.” The concept itself was invented by a NASA engineer who was a colleague of the professor.

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At its core, “digital twins” are a concept that combines artificial intelligence, computer learning and software with specific data to create living digital models. These “digital twins” are constantly updated as the physical prototypes change.

Where do digital twins get their data for self-updating?

The digital copy, as befits artificial intelligence, constantly learns and improves itself. To this end, a digital twin uses knowledge from humans, other similar machines, and the larger systems and environment of which it is a part.

Michael Greaves proposed his three requirements that “digital twins” must meet. The first is compliance with the appearance of the original object. You need to understand that similar appearance– this is not only the whole picture, but also the correspondence of individual parts to the real “twin”. The second requirement is related to the behavior of the double during testing. The last and most difficult thing is the information that is received from artificial intelligence about the advantages and disadvantages of a real product.

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As Michael Greaves points out, when digital copies were introduced, even the criterion of superficial similarity was considered difficult to achieve. Today, as soon as a digital twin is identical in the first parameters, it can already be used to solve practical problems.

Why do we need digital twins?

Digital copies are created to optimize the performance of physical prototypes, entire systems and production processes.

According to Colin J. Parris, Ph.D., vice president of software research at GE Global Research Center, digital twins are a hybrid model (both physical and digital) that are created specifically for specific business purposes, e.g. predict failures, reduce maintenance costs, prevent unplanned outages.

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Colin J. Parris states that when we talk about “digital twins”, this system works in three stages: seeing, thinking and doing. The “seeing” stage is about obtaining data about the situation. There are two types of information: operational data (eg boiling point) and environmental data. The next step, which Colin J. Parris conventionally called “thinking,” is due to the fact that at this stage the “digital twin” can provide options for various requests on how best to act in a given situation or which options are preferable for business purposes. Artificial intelligence uses for analysis, for example, historical information, revenue and expense forecasts and provides several options that are based on risks and the confidence that these proposals can reduce them. Last step– “to do” – is directly related to the implementation of what needs to be done.

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With the help of “digital twins”, for example, can see from within the problem of a physical object.

In production, we no longer need to see, for example, the entire turbine in front of us in order to detect a hole. Digital twin technology will allow you to see the problem in real time using computer visualization.

According to Zvi Feuer, executive vice president of software development at Siemens, the digital twin is a PLM solution on the path to Industry 4.0.

What types of “digital twins” already exist?

As we said earlier, “digital twins” are actively used in industry: part twins (which are built for a specific production part), product twins (related to the release of a product, their main goal is to reduce the cost of maintenance), process twins ( their purpose may be, for example, to increase the service life), system twins (optimization of the entire system as a whole).

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According to the high-tech research and consulting agency Gartner, hundreds of millions of “digital twins” will soon replace human labor. Some companies already use this. It is not necessary to have an employee on staff who would diagnose problems in production. In real time, with the help of “digital twins”, you can receive all the necessary data and be ready to repair equipment in advance.

What about the “digital twin” of the person himself?

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For those who want to have a Terminator friend who thinks like you, helps in everything, is a brother and a friend, we have good news. According to futurist and technologist John Smith, such a future is already near. He believes that in the near future there will be so-called software agents, who will predict in advance the wishes and behavior of their real copy and will perform some actions for their human double.

The “Digital Twin” will be able to make purchases, make business decisions, engage in social activities - in general, will be able to do everything that we sometimes do not have enough time for.

We will also be able to transfer all the routine work to our double. In addition, according to John Smith, our digital clones will know our interests, preferences, political views and, if necessary, will be able to defend them, since they will have a more complete historical context and see the modern picture of the world as a whole. And even a feeling of compassion. For example, a “digital twin” will show affection towards us, as it will be able to guess our emotional state.

This all sounds like a utopian movie script. I feel something is wrong. What are the disadvantages of “digital twins”?

The disadvantages of digital twins are obvious. First of all, the question of our safety arises. Digital clones will use all possible resources to supplement information about us. These are the algorithms that collect data from accounts social networks, and our personal correspondence, and any documents and files that, one way or another, concern us. Of course, this cannot but be alarming: as we have already found out, “digital twins” are capable of constantly updating and improving. Therefore, one of the primary tasks should be the creation of a legal framework for determining the “limits of permissibility” of artificial intelligence.

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However, do not panic about this. Take John Smith as an example: he remains optimistic and believes that “digital twins” will not replace humanity. They will simply become different versions of humans who can peacefully coexist with us.

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In Russia today it is difficult to talk about the 4th industrial revolution, but we believe that it is necessary to talk. Among the technological drivers at industrial enterprises in the new generation, industrial Internet of things platforms will appear that implement the concept of a digital twin.

Forrester analysts define a digital twin as the creation of a real physical object in an abstract digital form that acts as an intermediary for any communication with a real device.

According to General Electric, the idea of ​​a digital twin is to go further than just working with digital models. The company says that Maintenance will also occur in synchronization with the digital model with the real object through sensor systems and communications.

Analyst agency Gartner predicts that by 2021, half of large industrial companies will use digital twins and as a result, these organizations will receive a 10% increase in operational efficiency.

“Digital twins are driving the business impact of IoT by offering a powerful way to monitor and manage assets and processes,” says Alfonso Velosa, research vice president at Gartner. This especially excites our team, since we are very closely involved in the SAYMON project automated monitoring and management, including information systems and the Internet of things. Of course, competition in the market for platforms for IoT management is quite high - literally every major digital corporation today claims to have platforms, but not everyone has managed to make their own developments or acquire a company with a ready-made solution. Often a statement of availability is a tribute to decency - there is a technological trend, there is a statement from a corporation.

Today we do not yet work with digital models and drawings - we are open to partners with experience in this field. At the moment, we have experience collaborating with a company that creates photo-realistic copies of industrial facilities and as a result, a separate project VIOTR was born, combining the power of digitized space with the ability to obtain data from real sensors and video cameras, the ability to control switches, relays and dampers in the real world. The VIOTR project today has a focus on educational technologies future, but is essentially part of the digital twin concept.

This is exactly what our colleagues from Computer Weekly put it - the new approach involves managing communications between edge devices and internal systems and mirroring changes in the virtual model of the device - in other words, a digital twin appears.

Examples show that even such simple operations as controlling door locks can achieve significant operating savings. Dormakaba, which makes smart door locks, has been using ServiceMax's field management software since 2012, helping it monitor its installations. Detailed data on the performance of each door helps Dormakaba and its partners manage buildings more efficiently. A recent Vanson Bourne study for ServiceMax found that industrial companies lose $260,000 per hour due to unplanned downtime. Predicting failure using digital twins can help overcome this problem. The digital twin can provide engineers at Dormakaba with the most up-to-date record of every activity or event recorded by the door sensors, records the installation of components and firmware updates, and can be used by the Dormakaba service team to determine the lifespan of a product along with detailed description security log which is connected to the door. It is also important to ensure close communication with parts and component suppliers and product life cycle management, ensuring an extremely clear level of control and service. By using digital lock forecasting, Dormakaba expects to reduce the number of customer calls and improve the quality of service. Together with Swisscom, a cloud platform for lock management was created. Partner training is an important element of innovation and business transformation, Dormakaba recognizes.

In a Gartner report Digital Twin s Will Impact Economic and Business Models, the analytics firm draws an analogy between the amount of data collection done by companies like Google, Amazon and Netflix and how much data digital twins in industrial firms will create to continuously monitor the performance of equipment connected to control systems.

Analysts warn that this will require even more control of components and software updates, and will require car manufacturers to become software providers. “Asset operators will need to add software skills to their operations teams as they add smarter assets, and add software and data ownership to support contracts,” the analysts warn.

Neural networks, digital twins, artificial intelligence. Industry 4.0 technologies will change the oil industry beyond recognition

Architects of the Digital Age

Usually the most technologically advanced spheres are considered to be information technologies and biomedicine. The attitude towards companies in traditional industries, such as metal rolling or oil production and refining, is completely different. At first glance, they seem conservative, but many experts call them the main architects of the new digital era.

Industrial giants began automating production processes back in the mid-30s of the last century. Over the course of many decades, hardware and software systems have continuously improved and become more complex. Automation of production processes - for example, in oil refining - has made great progress. The operation of a modern oil refinery is monitored by hundreds of thousands of sensors and instruments, and fuel supplies are monitored in real time by systems satellite navigation. Every day, the average Russian refinery produces more than 50,000 terabytes of information. For comparison, the 3 million books stored in the digital storage of the Russian State Library occupy hundreds of times less - “only” 162 terabytes.

This is the same “big data”, or Big Data, is a flow comparable to the information load of the largest websites and social networks. The accumulated array of data represents a unique resource that can be used in business management. But traditional methods of information analysis are no longer suitable for this. Working truly effectively with such a volume of data is only possible with the help of Industry 4.0 technologies. In the context of a changing economic paradigm, rich industrial “historical experience” is a serious advantage. Big data is at the heart of artificial intelligence. Its ability to learn, understand reality and predict processes directly depends on the amount of loaded knowledge. At the same time, industrial companies have a powerful engineering school and are actively involved in introducing and improving new technologies. This is another circumstance that makes them key players in the “new economy”.

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Finally, domestic industrialists know the price of business efficiency. Russia is a country of long distances. Often, production assets are located at a great distance from consumers. In these conditions, it is very difficult to quickly respond to market fluctuations. Traditional technologies allow saving no more than a tenth of a percent. Meanwhile, digital solutions today make it possible to reduce costs by up to 10-15% per month. The fact is obvious: in the era of the fourth industrial revolution, the one who learns to most effectively apply new technologies in the context of accumulated experience will be competitive.

Petr Kaznacheev, Director of the Center for Resource Economy, RANEPA: “As a first step towards an “integrated” artificial intelligence system in oil and gas, one could consider “smart” management and corporate planning. In this case, we could talk about creating an algorithm for digitizing the entire key information about the company's activities - from the field to the gas station. This information could be sent to a single automated center. Based on this information, using artificial intelligence methods, forecasts and recommendations could be made to optimize the company’s work.”

Leader of digital transformation

Realizing this trend, industrial leaders in Russia and the world are restructuring business processes that have developed over decades, introducing Industry 4.0 technologies into production based on the industrial Internet of things, artificial intelligence and Big Data. The most intensive transformation is taking place in the oil and gas industry: the industry is dynamically “digitalizing”, investing in projects that seemed fantastic just yesterday. Factories controlled by artificial intelligence and capable of predicting situations, installations that tell the operator the optimal operating mode - all this is already becoming a reality today.

At the same time, the maximum task is to create a management system for production, logistics, production and sales that would unite “smart” wells, factories and gas stations into a single ecosystem. In an ideal digital model, the moment a consumer pulls the nozzle lever, company analysts in the operations center instantly receive information about what brand of gasoline is being filled into the tank, how much oil needs to be extracted, delivered to the plant and refined to meet demand in specific region. So far, none of the Russian and foreign companies have been able to build such a model. However, Gazprom Neft has advanced the furthest in solving this problem. Its specialists are currently implementing a number of projects, which should ultimately become the basis for creating a unified platform for managing processing, logistics and sales. A platform that no one else in the world has yet.

Digital twins

Today, Gazprom Neft refineries are among the most modern in the industry. However, the fourth industrial revolution opens up qualitatively new opportunities, while simultaneously placing new demands on automation. More precisely, we are talking not so much about automation, but about almost complete digitization of production.

The basis of the new stage will be the so-called “digital twins” - virtual copies of refinery installations. 3D models reliably describe all processes and relationships occurring in real prototypes. They are based on the work of artificial intelligence based on neural networks. The “digital twin” can suggest optimal operating modes for equipment, predict its failures, and recommend repair times. Among its other advantages is the ability to constantly learn. The neural network itself finds errors, corrects and remembers them, thereby improving its performance and forecast accuracy.

The basis for training the “digital twin” is an array of historical information. Modern oil refining plants are as complex as the human body. Hundreds of thousands of parts, tens of thousands of sensors. Technical documentation for each installation occupies a room the size of an assembly hall. To create a “digital twin”, all this information must first be uploaded to neural network. Then the most difficult stage begins - the stage of training artificial intelligence to understand the installation. It includes readings from sensors and instrumentation collected over the last few years of plant operation. The operator simulates various situations, forces the neural network to answer the question “what will happen if you change one of the operating parameters?” - for example, replacing one of the raw material components or increasing the energy supply of the installation. The neural network analyzes the experience of past years and, using a calculation method, excludes non-optimal modes from the algorithm, and learns to predict future job installations.

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Gazprom Neft has already completely “digitized” two industrial complexes involved in the production of automobile fuel - a hydrotreating unit for catalytic cracking gasoline at the Moscow Oil Refinery and an installation operating at the company’s oil refinery in Omsk. Tests have shown that artificial intelligence is able to simultaneously take into account a huge number of parameters of their “digital twins”, make decisions and notify about possible deviations in work even before the moment when the trouble threatens to develop into a serious problem.

At the same time, Gazprom Neft is testing comprehensive solutions that will minimize the impact of the human factor on the scale of the entire production. Similar projects are currently being implemented at the company’s bitumen plants in Ryazan and Kazakhstan. Successful solutions found experimentally can subsequently be scaled up to the level of large refineries, which will ultimately create an effective digital production management platform.

Nikolay Legkodimov, Head of the Advanced Technologies Advisory Group at KPMG in Russia and the CIS:“Solutions that simulate various components, assemblies and systems have been known and used for quite a long time, including in the oil and gas industry. We can talk about a qualitative leap only when a sufficient breadth of coverage of these models has been achieved. If we manage to combine these models with each other, to combine them into a whole complex chain, then this will indeed make it possible to solve problems at a completely new level - in particular, to simulate the behavior of the system in critical, unprofitable and simply dangerous operating conditions. For those areas where re-equipment and modernization of equipment are very expensive, this will allow preliminary testing of new components.”

Performance Management

In the future, the entire added value chain in the logistics, refining and sales block of Gazprom Neft will be united by a single technological platform based on artificial intelligence. The “brain” of this organism will be the Performance Management Center, created a year ago in St. Petersburg. This is where information from “digital twins” will flow, here it will be analyzed and here, based on the data obtained, management decisions will be made.

Already today, in real time, more than 250 thousand sensors and dozens of systems transmit information to the Center from all the company’s assets included in the perimeter of the Gazprom Neft logistics, refining and sales block. Every second 180 thousand signals arrive here. It would take a person about a week just to view this information. The digital brain of the Center does this instantly: in real time it monitors the quality of products and the quantity of petroleum products along the entire chain - from the exit from the refinery to the end consumer.

The strategic goal of the Center is to radically increase the efficiency of the downstream segment, using the technologies and capabilities of Industry 4.0. That is, it’s not just about managing processes - this can be done within the framework of traditional systems, but making these processes more efficient: through predictive analytics and artificial intelligence at every stage of business, reducing losses, optimizing processes and preventing losses.

In the near future, the Center must learn to solve several key problems that affect the efficiency of business management. This includes forecasting the future 60 days in advance: how the market will behave in two months, how much oil will need to be processed to satisfy the demand for gasoline at the current moment in time, what condition the equipment will be in, whether the installations will be able to cope with the upcoming load and whether they need repairs. At the same time, in the next two years, the Center must reach 50% capacity and begin to monitor, analyze and forecast the amount of petroleum product reserves at all oil depots and fueling complexes of the company; V automatic mode monitor more than 90% of production parameters; analyze the reliability of more than 40% of process equipment and develop measures to prevent losses of petroleum products and a decrease in their quality.

By 2020, Gazprom Neft sets a goal to reach 100% of the capabilities of the Performance Management Center. Among the stated indicators are analysis of the reliability of all equipment, prevention of losses in the quality and quantity of products, and predictive management of technological deviations.

Daria Kozlova, senior consultant at VYGON Consulting:“In general, integrated solutions bring significant economic benefits to the industry. For example, according to Accenture estimates, the economic effect of digitalization could amount to more than $1 trillion. Therefore, when we are talking about large vertically integrated companies, the implementation of integrated solutions is very justified. But it is also justified for small companies, since increasing efficiency can free up additional funds for them by reducing costs, increase the efficiency of working capital management, etc.”

Discuss 0

From the editor's website: At the end of May, the Siemens PLM Connection forum was held in Moscow, the main topics of which were the creation of a digital twin, 3D printing, the Internet of things and increasing the competitiveness of Russian products.

Note that the term digital twin in Russian-language publications is translated both as “digital twin” and “digital twin”.

The hall could hardly accommodate everyone

Five steps to building a digital enterprise

“Once you create and optimize these processes, you can integrate them, connect your suppliers, and have one holistic approach to building your business. Moreover, it will provide the opportunity to create a digital twin of your enterprise, which will allow you to simulate its operation in order to proactively identify bottlenecks, for example, where surpluses are created or where delays are expected,” said Jean Luca Sacco, director of marketing for Siemens PLM Software in the EMEA region. – This sounds like science fiction, but it is already quite feasible. Just take five steps and a digital twin can help your company.”

The first step is product development, Jean Luca Sacco illustrated in real example one of the products created by Siemens itself, reusing its previous generations as much as possible and subject to subsequent testing without creating a physical prototype of all its properties, including heating, cooling and electromagnetic protection. “Our specialty is developing products based on a systematic approach based on an information-rich digital twin of the product, which is stored in the Teamcenter collaboration environment so that all development participants have access to it,” he said.

The second step, the development of production technology, involves modeling not the product itself, but production operations. “Using the Plant Simulation system, we simulate all production operations before creating a workplace in order to anticipate all difficulties in advance. Moreover, this applies not only to one workplace, but to the entire plant as a whole. This will make it possible to optimize material flows, energy consumption and simulate production processes long before the start of investment in building a workshop,” said Jean Luca Sacco and presented an example showing how the model can be used to avoid dangerous curvature of the worker’s spine during assembly.

The third step, preparation and launch of production, involves the use of another digital twin, this time for technical processes and equipment. According to Jean Luca Sacco, Siemens is the only company in the world that can offer an integrated computer engineering system that allows the creation of a complete digital twin, including all disciplines such as mechanics, electrical and software, to test everything before production begins. He emphasized the importance of integrating all components of such a double: “After all, in life everything is interconnected. We design a product, on this basis we develop a process, and the features of the technical process impose requirements for product development.”

The fourth step, production of the product, is also implemented using a digital twin. After all, without it it is impossible to create a real work schedule in order, for example, to determine time losses and optimize production processes. Traditionally, this required a lot of paper instructions, which was inefficient and error-prone, but digital modeling makes it possible to create the perfect set of instructions for the production and assembly of a product. Jean Luca Sacco explained that such a solution is comprehensive, it covers all the resources of the enterprise, such as people, materials, equipment, machines, and with the help of a digital twin allows you to manage production. Electronic information transmitted to the operator at that moment. when he needs her. At the workplace, he can use augmented reality technology and better understand what he needs to do with the incoming workpiece and thereby minimize errors during assembly. But even if errors occur, comparing the real product with its digital twin will eliminate them. “This approach removes the walls that have always existed between designers and workers, and thus makes it possible to significantly improve product quality,” said Jean Luca Sacco.

The fifth stage, maintenance, will become more efficient if you use a solution that allows you to collect and analyze the information that the product generates during its operation.

To implement these five steps, Siemens offers a Digital Enterprise Software Suite, including Teamcenter, NX, Tecnomatix and others, which takes into account production chain processes for various industries. According to Jean Luca Sacco, this solution shows the state of the product at all stages - from the initial idea to the consumer's use, all in a single environment. At the same time, at each stage, people use the work of their colleagues, benefiting from the fact that they have data not only about the current stage, but also about all previous and subsequent ones.

Russian realities

Despite all the difficulties, our companies create complex innovative products, solving problems that have not been solved before. As an example, Viktor Bespalov cited several developments. Thus, when creating a new transport aircraft Il-76, a digital model was built and a single information space, covering the parent organization - Design Bureau named after. Ilyushin, and suppliers.

When developing the new KamAZ-5490 tractor, modeling of almost all assembly processes was carried out before the start of production, which corresponds to the Siemens concept, and when creating the new PD-14 engine, which is now being tested, its full digital model was developed, used not only in production, but in technology services.

At the same time, Viktor Bespalov emphasized, Russian enterprises have to solve many problems. Thus, due to the increasing complexity of products, traditional methods of product decomposition cease to work. Therefore, requirements management and compliance with certification standards must be addressed at the earliest stages.

Making changes during development and beyond remains a challenge. The use of digital modeling and various methods calculation, however, the complexity of this task suggests that there is still work to be done. There are resource management issues associated with the interaction between PLM and ERP.

Victor Bespalov: “Despite all the difficulties, the majority of our Russian customers
plans to expand the use of Siemens PLM Software products."

There are also national problems. Our companies operate not only locally, they enter global markets, as it is impossible otherwise. Viktor Bespalov cited data obtained from one Russian aviation holding company and its foreign competitors, which show that our company spends almost twice as much time on fine-tuning production as they do. In his opinion, this is an alarming signal that Western companies are bringing products to market much faster, and Russian manufacturers it is necessary to try to reduce these losses.

To do this, our companies must use technologies that make them competitive. In this regard, Viktor Bespalov believes that it is necessary to carefully consider the choice of technologies: “I categorically disagree with the statements of some Russian developers that have appeared recently in connection with the import substitution policy, which emphasize that Russian PLM systems are 80% meet the requirements of our enterprises. What to do with the remaining 20%? How will our domestic companies be able to compete in such a situation? How to deal with global players who are already equipped with modern technologies?

As an answer to these rhetorical questions, Viktor Bespalov cited the results of a survey of Russian customers, which show. that despite all the difficulties, most of them plan to expand the use of Siemens PLM Software products.

Apparently, the attention that the Russian office pays to customer requirements plays an important role in this. Moreover, today we are no longer talking about the design of drawings, but about functional requirements. At the last conference, taking into account the requirements of the Design Bureau named after them was mentioned. Sukhoi and ASTC named after. Antonov in the NX CAD system.

This work continues for other products, in particular, the integration of the Sinumetrik CNC system and NX CAM has been strengthened to combine the real and virtual worlds, the integration of NX and Fibersim for aviation programs has been improved, the Product Cost Management system has been adapted to Russian cost calculation methodologies, and the Teamcenter and Test systems have been integrated. Lab for end-to-end requirements verification process.

This topic worries Russian users. So Michael Rebruch, NX Development Director, was asked from the audience a question about how you can convey your problems to NX developers and influence development. To which he replied that the company continues to cooperate with customers in Russia, listening to their wishes and taking them into account: “It is important for us to understand how they work, where they experience difficulties, and then we will try to help.” For his part, Viktor Bespalov promised that immediately after the forum he would continue to work with customers to define requirements and create a plan to meet them in future versions of products.

Attention is also paid to the topic of creating a prototype of a standard solution. “PLM is not a cheap technology, so customers are interested in getting value quickly. In this regard, over the past four years, our efforts have been focused on reducing implementation times,” said Viktor Bespalov.

Special pre-configured data models, NX templates to support unified storage systems, templates for change management processes, libraries for standard parts, materials, technological resources, etc. have already been created, a methodology has been developed quick launch into operation. According to Siemens estimates and data from pilot projects, implementation time can be halved due to the fact that almost 80% of the work is covered standard solution, and only 20-30% falls on taking into account the specifics of the customer.

In addition, as part of the implementation of the industrial approach announced several years ago, Siemens is promoting in Russia a set of industry pre-configured Catalyst solutions, which includes best practices and basic processes for various industries, such as shipbuilding, automotive, mechanical engineering, electronics, energy, etc. . According to Victor Bespalov, these solutions make it possible to introduce new solutions into existing processes in such a way as to reduce the gap between advanced technologies and what the enterprise actually uses.

The presentations of Russian customers showed how we implement the listed Siemens technologies. Thus, Vasily Skvorchuk, head of the IT department of Ural Locomotives LLC, said that when launching the new production of Lastochka electric trains, it was decided to create a comprehensive automation system at the enterprise, including Teamcenter, NX CAD/CAM/CAE from Siemens, Russian Belarusian ERP system Omega (Russian-Belarusian) and “1C: Manufacturing Enterprise Management”.

Vasily Skvorchuk: “Now in an integrated corporate system employs about 1,100 people"

Ural Locomotives LLC, a joint venture with Siemens, was created in 2010. “From that moment, the rapid development of information technology began at our plant,” said Vasily Skvorchuk and added that about 1,100 people now work in the integrated corporate system, and management can monitor the progress of work on the manager's panel, which receives all the basic information. Thanks to this system, all departments have access to a single source of up-to-date information necessary to produce high-quality equipment for Lastochka.

The company plans to use a three-dimensional electronic model of the product for parts processed on a CNC machine. A pilot project has already been carried out.

The transition to an electronic prototype of the product is also underway at the Ulan-Ude Aviation Plant, which develops and produces Mi-8 helicopters. The plant's IT director, Maxim Lobanov, spoke about two projects to organize a digital process for technological preparation of production based on the original design documentation in the form of an electronic layout.

First, for the new helicopter model, the “End Beam” project was implemented, during which the equipment and the beam itself were created, and then the “Cargo Floor” project, manufactured entirely using paperless technology. As part of this project, the tooling assembly process was refined, which made it possible to increase assembly accuracy and reduce time.

According to Maxim Lobanov, in connection with the transition to paperless technologies, there was a need to integrate the Teamcenter PLM system with the planning system used at the plant, as well as create a modern information system to bring the digital layout to each workplace.

Foreign examples

“Production and service are an interesting combination; to get the maximum effect, you need to bring these elements together. We have about half a million pieces of equipment in service and digitalization is very important here,” explained Matti Leto, Director Product & Engineering Process at Konecranes.

He said the process was first defined, and then the search began for a solution to support those processes so that the systems would continue to function well into the future for many years to come. A list of platforms was compiled, including ERP, CRM, etc., but the company considers the PLM system to be the most important from the point of view of long-term sustainability, since it contains information about products. The choice fell on Teamcenter.

At the moment, some of the systems have been implemented, the rest are being implemented. Meanwhile, Konecranes is moving to the next level of digitalization by using IoT technology to automate equipment maintenance and optimize other processes. For this purpose, a portal has been created for the exchange of information between the company, partners and customers.

The Internet of Things project at Konecranes is off to a successful start. More than 10 thousand pieces of equipment are connected to the network. “The PLM system significantly increases the value of the Internet of Things, because Product data together with equipment monitoring data allows you to quickly make informed decisions,” Matti Leto shared his experience. “We believe that the Internet of Things is a new business model that is the future.”

Digital twin as the basis for future production

“Creating a digital twin for lifecycle management of the entire production system allows enterprises to reach a new level of innovation,” said Robert Meschel, senior director of Siemens PLM Software strategy for Manufacturing Engineering Software, and said that by acting in this direction, the company is developing the areas of manufacturing engineering and digital production. “Several new products we are working on now bridge the gap between design and production,” said Robert Meschel.

In addition, there is an increasing use of robots, which are now much more flexible than before. 3D printing, which until recently was considered only suitable for prototyping, is beginning to be used in real production. As evidence, Robert Meschel cited specific examples from the aerospace, shipbuilding, mechanical engineering and automotive industries that show that this provides radical acceleration: “We are updating our products to provide customers with the opportunity to use this technology.”

Another promising advanced approach is virtual commissioning using an integrated hardware and software package. According to Robert Meschel, all this indicates that the basis of future production will be the simulation of reality, and an important prerequisite for this is a digital twin - a model with a high degree of detail.

It is also important that the use of a digital twin allows you to integrate calculations and full-scale tests, as well as models and data. According to Wouter Dehandschutter, technical director of product, Siemens PLM Software, the challenge here is to make the most of the information created at different stages and link it together, but there are now a number of stages in which engineering information is produced in isolation.

Wouter Dehandschutter: “The use of a digital twin allows the integration of calculations and full-scale testing”

He showed that this problem can be solved using a digital twin, analyzing the product at the earliest stages through virtual testing, controlling the twin and increasing its level of detail and accuracy so that full-scale testing focuses on meeting requirements rather than finding solutions.

As an example, Wouter Dehandschutter cited the Irkut Corporation, which applied this approach when designing the MC-21 aircraft, using the products LMS Imagin.Lab and LMS Amesim to calculate the behavior of the system. At the same time, not only individual parts were modeled, but the overall interaction of systems, which made it possible to check at the design stage how the entire aircraft would behave and, according to Irkut, to reduce the creation of the most complex models by five times compared to the previously used solution.

What's new in NX 11

However, one new product is already available. It's free mobile app Catchbook designed for development. “By drawing a freehand sketch on a tablet, the result of which is converted into geometry, we can add dimensions and control the positioning of the sketches. You can also take a photo using your mobile phone and use this system to explore the possibilities of this project,” explained Michael Rebruch.

Michael Rebruch talks about what's new in NX 11

Coming with NX 11 is a new Converging Model product that allows you to combine precise geometry and edge-based cellular representation in a single model. According to Michael Rebruch, customers who have already met him say he has changed the way work is done, so this model can be used in design, testing and new methods such as 3D printing and hybrid manufacturing.

NX 11 will also include the new Lightworks Iray+ solution, based on Nvidia's Iray technology, which is designed for creating photorealistic images and includes a library of materials and scenes.

Additionally, NX 11 will allow you to scan, load, and interact with massive point clouds just like in the real world to design in the context of your physical environment.

NX 11.01 will feature new technology topology optimization, designed to create surfaces of complex shapes, optimize the shape, weight, materials used, dimensions and topology of structures while maintaining the functioning of the part. This is expected to improve interoperability with additive manufacturing. -->

There is a better way. Identification of ways to improve the efficiency of engineering and technological design processes

Aaron Frenkel, Jan Larssen

Manufacturing a product is undoubtedly the most important part of all life cycle processes. At this stage, ideas turn into reality. Moreover, without coordinated design and manufacturing processes to ensure successful assembly of the product on the shop floor, ideas will remain just beautiful drawings or will not be fully realized. For many years, the methods of designing and developing technological processes remained unchanged, maintaining all the traditional shortcomings that lead to increased costs and deadlines. Considering that today innovation has become vital for the survival of machine-building enterprises, Siemens PLM Software analyzed pre-production processes in order to identify ways to further optimize them. In this article, Aaron Frankel, Senior Director of Marketing for Mechanical Engineering Solutions, and Jan Larsson, Senior Director of Marketing for Europe, Middle East and Africa at Siemens PLM Software, discuss what sources of inefficiency need to be eliminate to introduce the concept of a “digital twin of a product” and how this will affect the way products are manufactured.

A beautiful symphony

If you find yourself in a modern enterprise, you will see an amazing symphony of labor of people, robots and machines, the movement of materials and parts - and all this is done with precision to the second in order to stay on schedule. The picture turns out simply fantastic.

But behind the scenes we will see outdated processes of design and technological preparation of production. We are not going to criticize anyone. Developing a product design is no small achievement in itself. Designing can be a very challenging task. In some cases, a product consists of millions of parts, and thousands of employees and partners work on its creation, often around the world. Moreover, in such critical industries as the electronics industry (more fast processors, miniaturization), the automotive industry (environmental issues and emissions reduction) and the aerospace industry (environmental friendliness and the introduction of composite materials), there is a constant desire to optimize and accelerate the processes of creating new products. Taking into account the high complexity of the problems being solved, the reluctance to deviate from practice-tested pre-production processes is quite understandable. However, our customers report common problems in product design and manufacturing, which in some cases lead to costly delays.

Common problems

One of the biggest challenges we see is that designers and technologists use different systems. In practice, this leads to the fact that designers transfer their developments to technologists who are trying to create technological processes V computer systems, to which they are accustomed. In this scenario - and it occurs very often - information desynchronizes, which makes it difficult to control the situation. In addition, the likelihood of errors increases.

Problems regularly arise during the development of workshop layouts. The reason for this is that floor plans are usually created in the form of two-dimensional floor plans and paper drawings. This is a long and labor-intensive process. 2D drawings are an important part of the process, but they don't have the flexibility you need. It often happens that the rearrangement of equipment in a workshop is not recorded on the drawing. The problem is particularly acute when operating in rapidly changing markets (e.g. consumer electronics) when continuous expansion and modernization of production systems is required. Why? Because two-dimensional layouts lack intelligence and associativity. They prevent technologists from knowing what exactly is happening on the shop floor and making smart decisions quickly.

After creating the layout, a technological route is developed. As a rule, it then goes through a control stage. Here lies another significant obstacle to increased efficiency. Technologists usually have to wait until the equipment is installed to evaluate the performance of the equipment. Moreover, if the characteristics turn out to be lower than expected, then it may be too late to develop an alternative technology. Our experience is that this situation results in significant delays.

Finally, customers report two additional problems occurring late in the pre-production cycle. This is an assessment of the performance of individual operations and the entire technological process as a whole.

Due to the high complexity of modern production and the frequent lack of coordination between various systems In process design, identifying which specific operations or production areas are causing delays across an entire line is challenging. And when it comes to the actual manufacturing of the product, customers report that it is usually extremely difficult to evaluate the performance and degree to which actual processes correspond to planned ones. Once again, the problem lies in the high complexity, as well as the lack of feedback between production, designers and technologists.

Digital twin

A digital twin is a virtual copy of a real object that behaves in the same way as the real object. Without getting into the technical details of our products here, suffice it to say that our Product Lifecycle Management (PLM) tools provide a complete digital platform. It supports the use of digital twins that accurately model end-to-end product design and manufacturing processes.

What does all this mean in practice? Let's take a look at the above steps again and show the main capabilities provided by the new approach.

Construction

NX (and other CAD systems) creates a model of the product and transfers it to Teamcenter in 3D JT format. In a matter of seconds, the application creates thousands of different virtual versions of the product that exactly match the real product. At the same time, to identify potential problems, big data processing technologies, design and technological information (PMI) contained in models (tolerances, fits, connections between parts and assemblies), as well as a basic description of the technological process are used. This approach has already been tested in practice when creating electronic products manufactured by our company. For example, we were able to immediately determine that the screw holes on the video output connector did not line up exactly with the screw holes on the PCB. If the error had gone undetected, it would have resulted in warranty claims from customers: the connector could have become separated from the printed circuit board. Identifying design errors at an early stage saves significant time and money, both during technology development and during production.

Process design

The digital twin allows you to improve the collaboration of designers and technologists, optimize the choice of location and manufacturing technology, as well as the allocation of the necessary resources. Let's look at an example of making changes to the build process. Using our software, process engineers add new operations to a working 3D process model based on the new design specification. You can simulate any production system while being anywhere in the world: say, technologists in Paris are preparing production at a factory in Rio. Having time information for each added operation, technologists check whether the new process route meets the specified performance indicators. If this is not the case, then the technological operations are replaced or rearranged. Numerical simulations are then performed again until the selected process route satisfies the requirements. The new workflow is immediately available to all developers for approval. If any problems are identified, designers and technologists work together to eliminate them.

Workshop layouts

When working on layouts, we recommend creating a digital twin containing mechanical equipment, automation systems and resources, clearly connected with the entire “ecosystem” of design and technological pre-production. Using a set of PLM tools, process steps can be swapped using drag and drop. It is just as easy to place equipment and personnel on a production line and simulate its operation. It is very simple, but at the same time exceptional effective method creating and editing technological processes. When design changes are made that require the use of a new industrial robot, numerical simulation specialists check, for example, whether it is possible to install a robot of this size without hitting the conveyor. The workshop layout developer makes the necessary amendments and prepares a notice of changes, on the basis of which the purchasing department purchases new equipment. This analysis of the consequences of changes makes it possible to avoid errors and, if necessary, immediately notify suppliers.

Control of technological design solutions

During the inspection phase, the digital twin is used to virtually verify the assembly process. Virtual simulation and quantitative analysis can evaluate all the factors associated with manual labor in assembly and identify problems such as awkward worker posture. This makes it possible to avoid fatigue and work-related injuries. Based on the simulation results, training videos and instructions are created.

Performance optimization

The digital twin is used for statistical modeling and evaluation of the designed technological system. It makes it easy to determine whether manual labor, robots, or a combination of robots and workers should be used. Numerical simulations of all processes can be carried out, right down to the energy consumption of an individual machine, in order to optimize the technology as much as possible. The analysis shows how many parts are produced in each operation. This ensures that the performance of the actual production line will match the target.

Manufacturing of the product

The digital twin provides feedback between the real and virtual world, which allows optimization of product manufacturing processes. Technological instructions are transmitted directly to the workshop, where equipment operators receive them along with videos. Operators provide designers with production data (such as whether there is a gap between two screws holding a panel in place), while others automated systems collect performance information. Then a comparison is made between the design design and the actual manufactured product, and deviations are identified and eliminated.

New approaches to work

The use of a digital twin, which is an exact copy of a real product, helps to quickly identify potential problems, speeds up production preparation and reduces costs. In addition, the presence of a digital twin guarantees the possibility of manufacturing a product designed by the designers; all technological processes are maintained in an up-to-date and synchronized state; the developed technologies turn out to be operational, and production functions exactly as planned. The digital twin allows you to test how new technologies can be integrated into existing production lines. This eliminates the risks arising during the purchase and installation of equipment.

Mechanical engineering is one of the most advanced branches of global industry, where proven, but outdated approaches to technological preparation of production have long been used. It's time to bring a spirit of innovation that opens the door to success in product development and manufacturing. It's time to try something new!