Artificial Intelligence in Manufacturing

The main articles are:

• Artificial intelligence (AI, Artificial intelligence, AI)
• Information Technology in Industry

2024

Why it is difficult for industry in Russia to introduce artificial intelligence - TA opinions

The use of artificial intelligence in industry has great prospects, but enterprises in Russia face several key barriers that significantly limit the development of these technologies and their integration. TAdviser discussed the issue with market participants and experts in November 2024.

Director for Digital Transformation of NPP Istok named after Shokin Holding "Roselectronics" (part of the state corporation "Rostec") Vitaly Alexandrov connects difficulties with the introduction of AI solutions in the domestic industry with a lack of computing power, a shortage of highly qualified specialists, as well as an insufficient level of development of Russian AI solutions. In addition, according to him, there are subjective factors, such as a low level of confidence in the results of the use of AI and the non-obvious economic effect of their implementation.

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Freepik

The press service of MTS AI considers the lack of video cards to be the main problem. It is difficult and expensive for large industrial companies to buy them in the current conditions - on average, 30% more expensive than in other countries, the company said.

And this is very important, because most of these companies want an on-premium solution, which is deployed in the loop, and not in the cloud, respectively, it should be deployed on their computing power. This barrier is difficult to overcome - unless it is worth starting with solutions that will quickly bring results - such solutions are in every area, including industry, - added to MTS AI.

In Severstal, the main restraining factors in the development of AI include, first of all, the insufficient level of automation of production and digital maturity of some business processes. As explained in the press service of the mining and metallurgical company, in order to build a working model, it is necessary that the data be collected digitally with a certain frequency, be complete and reliable. The development of some systems requires not just retrofitting with sensors, but already the technical re-equipment of production lines. The successful introduction of digital tools and economic effects depends on the readiness of the business to transform business processes, openness to new technologies, the press service of Severstal emphasized.

Zyfra The head of the GC "" (engaged in digitalization projects in the industrial sector) Michael Aronson says that by November 2024 there are no vivid examples of the introduction of AI in industry and a proven commercial effect, so companies cannot fully assess the effects of the use of artificial intelligence. In addition, IT infrastructure many factories are not ready to deploy AI solutions, and the industry is still wary of using AI due to questions cyber security and data privacy, the source added. TAdviser

Olesya Kolosovskaya, head of generative AI at NLMK Group, agrees that the spread of artificial intelligence in industry is constrained by the need to use powerful equipment and highly qualified personnel. Also, as Kolosovskaya noted, any new technology requires adaptation to current production processes, which takes time and requires a clear strategy.

Russian Railways told TAdviser that the factor that directly affects the result of the introduction of AI is risk management. Companies may face technological risks (such as failure or errors in the operation of AI algorithms), operational risks (insufficient adaptation of employees to new technologies), as well as security risks.

It is important to assess the likelihood of each risk and its potential impact on business processes. This step is necessary to understand where immediate action is required, and in which cases it is necessary to monitor and manage risks in the long term. The systematization of risks by the degree of their influence and the likelihood of occurrence allows you to prioritize and direct resources to the most critical areas, according to Russian Railways.

Mikhail Krasilnikov, Director of the Department for Development and Implementation of Artificial Intelligence Systems at BIA Technologies (Integrator of IT Solutions for Business), draws attention to the fact that each industrial organization usually has specifics and special data. Often, common ready-made models do not work well enough, since they are not tuned to the specifics of the task. In turn, to develop a new AI product that takes into account the nuances of the enterprise, not only highly qualified engineers and programmers are needed, but serious financial resources, Krasilnikov specified.

Vitaly Alexandrov from NPP "Istok" named after Shokin, speaking about measures to overcome the barriers to the development of AI in the industry, pointed out the need to create incubators for AI prospectors, which, in his opinion, will help develop domestic AI solutions that will be adapted to the specifics of Russian enterprises and will be able to compete more effectively with foreign counterparts. An increase in the number of courses and curricula on AI in universities and specialized educational institutions will help overcome the personnel deficit, the director of digital transformation of NPP "Istok" is sure.

Market participants surveyed by TAdviser see in artificial intelligence the potential for economic effects and the benefits for the development of companies in the market. They agree that there are barriers, they can be overcome, but it will take some time.

The AI standard in the machine tool industry has been approved in Russia

In early November 2024, Rosstandart approved the preliminary national standard (PNST) 964-2024 - "Artificial intelligence technologies in the machine tool industry. Use Cases. " The document was developed by the Association "Digital Innovations in Mechanical Engineering" (ACIM). Read more here

The standard of AI in mechanical engineering has been adopted in Russia

On October 11, 2024, the Federal Agency for Technical Regulation and Metrology (Rosstandart) approved the preliminary national standard (PNST) 955-2024 "Artificial Intelligence in Mechanical Engineering. Use Cases. " The document was developed by the Federal State Budgetary Institution "Russian Institute of Standardization." Read more here

How Russian enterprises are introducing machine vision

Russian industrial enterprises are actively mastering machine vision technologies, integrating them with artificial intelligence to increase production efficiency. The growing interest in this area became known in October 2024 from the results of a study conducted by the Institute for Statistical Research and Knowledge Economics of the Higher School of Economics. Read more here

80% of AI solutions used in Russian production are created in the Russian Federation

At the end of 2023, approximately 80.9% of AI solutions used in Russian production were created in the Russian Federation or significantly modified by domestic developers. For comparison, in 2020, the indicator slightly exceeded 73%. Such data are given in the review of the Institute for Statistical Research and Economics of Knowledge of the Higher School of Economics, published on July 1, 2024.

It is noted that a narrow circle of organizations is engaged in the creation of advanced production technologies of artificial intelligence (PPT AI) in Russia: from 2020 to 2023, their number increased from 35 to 74, which is about 7.5% of the total number of PPT developers. At the same time, the intensity of using AI systems in the Russian Federation is increasing. Moreover, the use of technologies created by organizations independently is growing at the highest pace: in three years (by the end of 2023), their number has almost tripled.

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HSE

During 2023, 88 AI PPTs were developed in Russia, which is approximately 3.2% of the total number of PPTs created in the country. Ten of these products are fundamentally new systems that have no global analogues. In the total volume of new AI solutions, 31 products were developed by organizations operating in the field of information and communications. Another 26 solutions were released by developers from the field of higher education, 19 from the scientific field.

The review also says that in Russia the practice of using AI PPTs as an independent technology in the production of products is limited: in 2023, only 634 organizations applied such solutions in their activities. Of these, 372 enterprises (almost 60%) work in the field of information and communications. The number of AI BSA used since 2020 has increased 1.8 times (from 582 to 1030), but their specific weight in the total number of BSA used in production does not exceed 0.5%. And more than 60% of AI solutions used were implemented by organizations during 2021-2023^[1]

2023

20 areas of application of AI in industrial production in Russia

According to the results of 2023, 25.8% of industrial companies in Russia used technologies based on artificial intelligence (14th place among all industries in the direction of "Using AI"). Moreover, 54.2% of them rely exclusively on domestic AI-based solutions. Such data are given in a study, the results of which TAdviser got acquainted with in mid-December 2024.

The report said that 28.4% of companies that have implemented AI estimate the economic effect of such decisions as significant or multiple, and 97% of organizations have funding for an AI development action plan. In about 15% of enterprises, a senior manager responsible for the development of AI has been appointed.

According to the published data, the share of systems using computer vision in the total mass of AI-based solutions in industry in Russia is about 35%. Approximately 9% is sound processing, 8% is robotization. Natural language processing shows an indicator of 7%.

It is noted that in 2022-2024, AI technologies in industry and industrial enterprises accelerated development and began to be actively introduced into production processes. AI has become an important tool for automating various operations, improving efficiency and reducing costs. One of the key trends is the use of AI to analyze data in real time, which allows enterprises to quickly respond to changes in production chains and make more informed decisions. An important aspect is also the integration of AI with industrial robotics. The development of smart machines and robots capable of performing complex operations on their own reduces dependence on human labor and increases productivity. This approach is especially in demand in the field of precision production and in enterprises with a high degree of technological maturity.

Another trend is related to the introduction of AI to improve asset and infrastructure management. Predicting breakdowns and maintaining equipment with AI helps minimize downtime, reduce operating costs, and improve overall system reliability. AI-based automatic quality control platforms are actively developing: they allow detecting defects in the early stages of production, which reduces the amount of scrap and increases the overall efficiency of processes. The authors of the study name 20 key areas of application of AI in industrial production in Russia:

1. Production - 55 cases;
2. Data Management - 28;
3. Workflow and document management - 18;
4. Maintenance and repair (MR) - 16;
5. Safety and Security - 13;
6. Internal audit and control - 13;
7. Quality Management - 10;
8. Logistics - 9;
9. Personnel Management - 6;
10. Strategic Management - 6;
11. Information and communication infrastructure management - 6;
12. Administrative and Economic Support (ACS) - 5;
13. Business Planning - 5;
14. Accounting - 4;
15. Legal support - 3;
16. Procurement of raw materials - 2;
17. Sales and Marketing - 2;
18. Provision of personnel - 2;
19. Laboratory tests and R&D - 1;
20. Inventory Management - 1.

In general, as stated in the review, the development of AI in industry and industrial enterprises contributes to significant changes in the way production is organized and operations are managed. Companies actively implementing AI gain competitive advantages by improving efficiency, reducing costs and improving product quality.^[2]

AI in heavy industry: prospects and directions of use in Russia

According to the annual report, in Center for the Development of Artificial Intelligence under the Government of the Russian Federation 2023 artificial intelligence , 25% of companies operating in used technology. industries About 30% of organizations announced their intentions to use these technologies in the next three years. artificial intelligence Egor Sachko, extractive industries an expert on artificial intelligence, who implemented large projects of operational transformation using AI for a number of companies in heavy industry in and abroad, spoke Russia about how production management changes, and what trends and difficulties exist in this process. More. here

Created an open database for designing materials with specified properties using AI

On May 10, 2023, Innopolis University reported that a team of researchers from Russia and Singapore formed an open database for designing materials with specified properties using artificial intelligence tools.

The project was attended by the rector and employees of Innopolis University, experts from the National University of Singapore and the Higher School of Economics, as well as Nobel Prize winner in physics Konstantin Novosyolov. The published library of several thousand two-dimensional materials contains information about the structure and properties of single-layer materials with point defects.

Scientists from Russia and Singapore have created a database for designing materials with specified properties using AI

Scientists note that the development of solar panels, photocatalysts and biochemical sensors requires two-dimensional materials designed with the addition of impurities and defects. However, such compounds are difficult to find using classical calculation methods using quantum chemistry. The solution to the problem, according to the authors of the work, may be the introduction of machine learning tools.

To get a material with certain properties, you need to know the relationship between the structure and the property of the defects that need to be added. This is a difficult task, given the huge number of possible starting materials and configurations of defects. Machine learning methods make it possible to speed up the study of materials, namely, to reduce the number of experiments hundreds of times and generate the necessary structures for given properties, "said Ruslan Lukin, head of the Laboratory of Artificial Intelligence in New Materials at Innopolis University.

In the developed database, the datasets will be divided into two groups: defects with low and high densities. The information array mainly presents replacement defects, vacancies and their combinations. The published library includes approximately 3,000 calculated materials and 7,000 high-density defects. In the future, it is planned to develop machine learning models for more accurate and effective forecasting of material properties.^[3]

2022: Artificial intelligence created in Chelyabinsk to improve the quality of ultra-high-strength steel welding

In Chelyabinsk, an artificial intelligence was created that will increase the quality of ultra-high-strength steel welding. We are talking about the project of a scientist from South Ural State University Zhao Dawei, who received a grant to implement his idea. The development became known in September 2022.

The university explains the technology in complex scientific language. But the development attracts for its uniqueness. Heavy-duty steel is used in mechanical engineering, helps to reduce the weight of cars and make it safer. But at the same time, science by September 2022 does not fully know the mechanisms of electrode wear during spot welding. They are planned to be discovered in his research by a scientist and his team.

Zhao Dawei

With computer simulations and experiments, we investigate the effect of aluminum-silicon coating on the mechanism of electrode wear in the spot welding process. We plan to create a database that includes variable welding parameters of welding modes, microstructure, stress field, temperature field and electrode deformation field during the process of its wear, Zhao Dawei said.

According to him, the project is aimed at the development of industry in the Chelyabinsk region. Using artificial intelligence, a program was created to monitor the change in the state of the electrode during spot welding. The results of this project will lead to a significant increase in the competitiveness and efficiency of enterprises in comparison with their competitors, he is sure.

For the first time, the project proposes a large-scale study aimed at finding relationships between an aluminum-silicon coating and an electrode wear mechanism along with predicting electrode life, which will determine the optimal approach to processing electrode wear parameters to its maximum performance. This study will help identify the diffusion of chemical elements between the aluminum-silicon coating and the electrode during the welding process.^[4]

2020: Siemens: AI-based decisions will make key decisions and help make production safe

Over half of industry leaders believe that over the next five years, the world will transfer management of assets of great value to solutions based on artificial intelligence - in particular, factories, equipment and machines. This global trend was identified in a joint study between Siemens and Longitude Research. More than 500 top managers from the energy, production, infrastructure, transport sectors, as well as from the heavy industry sector took part in the survey on the development and implementation of AI, Siemens reported on October 26, 2020.

During the study, respondents were asked the following questions: what if you could automate a number of everyday operating solutions in your organization so that employees could focus on strategic projects such as developing new product lines or expanding your business? How good should the AI model be before you're ready to hand it control? Should its performance be at the level of engineers, or should it exceed it? What if a mistake can lead to serious financial losses or even injuries? These and other scenarios were proposed to 515 top managers of the industrial sector (including in the fields of power, production, heavy industry, infrastructure and transport).

The study showed that the level of confidence in AI is already very high for 2020:56% of respondents prefer to implement an ideal AI model instead of finding an experienced employee (44%). That means the other 44% likely have more confidence in decisions made by people, even if the evidence suggests in favor of AI.

In addition, the study pays attention to the types of contextual data that, according to leaders, can be considered the most useful at the time of the survey. Most of the votes (71%) on the issue of the most important and insignificant advantages were given by the participants in favor of data from equipment manufacturers. They are followed by internal data from other departments, regions or departments (70%), supplier data (70%) and performance indicators of products sold when used by customers (68%).

In these industries, many use cases assume the possibility of using AI in order to avoid accidents and make workplaces safer. In this regard, it is worth noting that, according to 44% of respondents, over the next five years, AI-based systems will autonomously monitor machine equipment, the operation of which carries potential risks of injury or death of personnel. Even more respondents - 54% - believe that in the same time frame AI will autonomously control individual assets of great value of their companies. But in order to transfer such responsibility to industrial AI, it, according to the survey participants, must reach the next level. In most cases, new approaches to data management, collection, mapping and sharing will contribute to this.

These include, for example, knowledge graphs that reflect the relationships between objects and connections in different data sets, or digital twins that allow you to create detailed digital models and simulate the behavior of real systems, assets or processes. Using industrial knowledge graphs to improve AI models by combining different datasets has very high potential.

"Knowledge graphs add context to the data being analyzed," said Norbert Gaus, head of scientific research in the field of digitalization and automation at Siemens. - For example, the technical characteristics of the machine can be analyzed in the context of design data, including the tasks for which the machine is intended, the temperatures at which it should operate, key parameters built into components, etc. Add to this the service history of similar machines, including malfunctions, reviews and expected results of inspections over the entire life of such a machine. Knowledge graphs make it much easier to link the industrial data needed to train AI models and add valuable contextual information. "

2017: AI helps Carlsberg create new beers

In December 2017, Carlsberg announced the use of artificial intelligence, which helps the Danish company create new beers. Read more here.

Robotics

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