Intelligent Production System Research Articles

Modular Intelligent Control System in the Pre-Assembly Stage

This paper presents a novel approach to developing fully automated intelligent control systems for use within production-based organizations, with a specific focus on advancing research into intelligent production systems. This analysis underscores a prevailing deficiency in control operations preceding assembly, where single-purpose control machines are commonly utilized, thus presenting inherent limitations. Conversely, while accurate multipurpose measurement centers exist, they often fail to deliver comprehensive quality control for manufactured parts due to cost and time constraints associated with the measuring process. The primary aim in this study was to develop an intelligent modular control system capable of overseeing the production of diverse components effectively. The modular intelligent control system is designed to meticulously monitor the quality of each module during the pre-assembly phase. By integrating sophisticated sensors, diagnostic tools, and intelligent control mechanisms, this system ensures precise control over module production processes. It facilitates the monitoring of multiple parameters and critical quality features, while integrated sensors and diagnostic methods promptly identify discrepancies and inaccuracies, enabling the swift diagnosis of issues within specific modules. The system’s intelligent control algorithms optimize production processes and ensure synchronization among individual modules, thereby ensuring consistent quality and performance. Notably, the implementation of this solution reduces inspection time by an average of 40 to 60% compared to manual inspection methods. Moreover, the system enables the comprehensive archiving of measurement data, eliminating the substantial error rates introduced by human involvement in the inspection process. Furthermore, the system enhances overall project efficiency, predictability, and safety, while allowing for rapid adjustments in order to meet standards and requirements. This innovative approach represents a significant advancement in intelligent control systems for use in production organizations, offering substantial benefits in terms of efficiency, accuracy, and adaptability.

Theoretical Framework and Research Proposal for Energy Utilization, Conservation, Production, and Intelligent Systems in Tropical Island Zero-Carbon Building

This study aims to theoretically explore the technological systems of tropical island zero-carbon building (TIZCB) to scientifically understand the characteristics of these buildings in terms of energy utilization, energy conservation, energy production, and intelligent system mechanisms. The purpose is to address the inefficiencies and resource wastage caused by the traditional segmented approach to building energy consumption management. Thus, it seeks to achieve a comprehensive understanding and application of the zero-carbon building (ZCB) technology system. This article focuses on the demands for energy-efficient comfort and innovative industrialization in construction. Through an analysis of the characteristics of TIZCB and an explanation of their concepts, it establishes a theoretical framework for examining the system mechanisms of these buildings. Additionally, it delves into the energy utilization, energy conservation, energy production, and intelligent system from macro, meso, and micro perspectives. This approach results in the development of an implementation strategy for studying the mechanisms of energy usage, conservation, and intelligent production systems in TIZCB. The results show that: (1) this study delves into the theoretical underpinnings of TIZCB, emphasizing their evolution from a foundation of low-carbon and near-zero energy consumption. The primary goal is to achieve zero carbon emissions during building operation, with reliance on renewable energy sources. Design considerations prioritize adaptation to high-temperature and high-humidity conditions, integrating regional culture along with the utilization of new materials and technologies. (2) A comprehensive technical framework for TIZCB is proposed, encompassing energy utilization, conservation, production capacity, and intelligent systems. Drawing from systems theory, control theory, and synergy theory, the research employs a macro–meso–micro analytical framework, offering extensive theoretical support for the practical aspects of design and optimization. (3) The research implementation plan establishes parameterized models, unveiling the intricate relationships with building performance. It provides optimized intelligent system design parameters for economically viable zero-carbon operations. This study contributes theoretical and practical support for the sustainable development of TIZCB and aligns with the dual carbon strategy in China and the clean energy free trade zone construction in Hainan.

A Review of Proposed Models for Cutting Force Prediction in Milling Parts with Low Rigidity

Milling parts with low rigidity (thin-walled parts) are increasingly attracting the interest of the academic and industrial environment, due to the applicability of these components in industrial sectors of strategic interest at the international level in the aerospace industry, nuclear industry, defense industry, automotive industry, etc. Their low rigidity and constantly changing strength during machining lead on the one hand to instability of the cutting process and on the other hand to part deformation. Solving both types of problems (dynamic and static) must be preceded by prediction of cutting forces as accurately as possible, as they have a significant meaning for machining condition identification and process performance evaluation. Since there are plenty of papers dealing with this topic in the literature, the current research attempts to summarize the models used for prediction of force in milling of thin-walled parts and to identify which are the trends in addressing this issue from the perspective of intelligent production systems.

Exploring the relationship between Project Cost Deployment and Industry 4.0 through an industrial application in an Engineer-to-Order environment

he literature on Lean Manufacturing has predominantly emphasized mass production and batch systems, leaving a considerable knowledge gap regarding the challenges and considerations specific to implementing Lean principles in Engineer-to-Order (ETO) manufacturing contexts. This paper addresses this gap by exploring the interaction between Project Cost Deployment (PCD), a tailored method for ETO environments, and the digital technologies of Industry 4.0. The research aims to identify the synergies between Lean Manufacturing and Industry 4.0, investigating how their integration can enhance productivity, quality, and competitiveness in the manufacturing industry. While Lean Manufacturing is adept at addressing loss reduction and elimination across various industrial contexts, Industry 4.0 leverages digital technologies to revolutionize manufacturing operations and enable more efficient and intelligent production systems. The paper demonstrates the effectiveness of the PCD method through an industrial application involving an international company specializing in customized paper converting machinery and lines. By utilizing PCD in conjunction with Industry 4.0 technologies, analysts were able to uncover hidden losses and quantify them from an economic perspective, while also evaluating the potential impacts of improvement activities. The results highlight the significance of synergizing Lean Manufacturing and Industry 4.0 for ETO environments, offering valuable insights into how digital technologies can be effectively integrated into Lean principles.

Optimizing smart manufacturing systems using digital twin

Presented paper investigates the application of digital twins for the optimisation of intelligent manufacturing systems and focuses on the comparison between simulation modelling results and real-world production conditions. A digital twin was created in the Simio software environment using a data-driven simulation model derived from a real-world production system. Running the digital twin in real time, which was displayed graphically, facilitated the analysis of key parameters, including the number of finished products, average flow time, workstation utilization and product quality. The discrepancies were attributed to the use of random distributions of input data in the dynamic digital twin, as opposed to the long-term measurements and averages in the real-world system. Despite the limitations in the case study, the results underline the financial justification and predictive capabilities of digital twins for optimising production systems. Real-time operation enables continuous evaluation and tracking of parameters and offers high benefits for intelligent production systems. The study emphasises the importance of accurate selection of input data and warns that even small deviations can lead to inaccurate results. Finally, the paper high-lights the role of digital twins in optimising production systems and argues for careful consideration of input data. It highlights the importance of analysing real-world production systems and creating efficient simulation models as a basis for digital twin solutions. The results encourage extending the research to different types of production, from job shop to mass production, in order to obtain a comprehensive optimisation perspective.

The Management of IoT-Based Organizational and Industrial Digitalization Using Machine Learning Methods

Recently, the widespread adoption of the Internet of Things (IoT) model has led to the development of intelligent and sustainable industries that support the economic security of modern societies. These industries can offer their participants a higher standard of living and working services via digitalization. The IoT also includes ubiquitous technology for extracting context information to deliver valuable services to customers. With the growth of connected things, the related designs often suffer from high latency and network overheads, resulting in unresponsiveness. The continuous transmission of enormous amounts of sensor data from IoT nodes is problematic because IoT-based sensor nodes are highly energy-constrained. Recently, the research community in the field of IoT and digitalization has labored to build efficient platforms using machine learning (ML) algorithms. ML models that run directly on edge devices are intensely interesting in the context of IoT applications. The use of intelligence ML algorithms in the IoT can automate training, learning, and problem-solving while enabling decision-making based on past data. Therefore, the primary aim of this research is to provide a systematic procedure to review the state-of-the-art on this scope and offer a roadmap for future studies; thus, a structure is introduced for industry sustainability, based on ML methods. The publications were reviewed using a systematic approach that divided the papers into four categories: reinforcement learning, semi-supervised learning, unsupervised learning, and supervised learning. The results showed that ML models could manage IoT-enabled industries efficiently and provide better results compared to other models, with significant differences in learning time and performance. The study findings are considered from a variety of angles concerning the industrial sector’s capacity management of the new elements of Industry 4.0 by combining the industry IoT and ML. Additionally, unique and relevant instructions are provided for the designers of expert intelligent production systems in industrial domains.

Purchasing decision of machine tool by exploiting uncertain information in nested probabilistic linguistic model

As the global environment deteriorates further, the decision-makers of enterprises no longer only consider qualitative factors such as yield in the choice of machine tools, but also pay more attention to the green sustainability and intelligent structure. In this study, a two-stage decision-making framework is established and a decision support system that combines quantitative and qualitative analysis is built to handle the machine tool purchasing decision. The first stage focused on quantitative analysis is to propose the mathematical model of the intelligent production system. Two heuristic algorithms that are automatic optimization method and periodic search method are designed to preliminary screen alternatives. The second stage related to qualitative analysis is to propose an improved TOPSIS method with nested probabilistic linguistic term set to obtain the best alternative comprehensively. In the end, we design the production schedule for the best alternative and prove the practicability and validity of the proposed models and algorithms. This study contributes to providing a theoretical perspective of representing uncertain information, as well as a practical scenario for purchasing decisions.

A Synergic Approach of Deep Learning towards Digital Additive Manufacturing: A Review

Deep learning and additive manufacturing have progressed together in the previous couple of decades. Despite being one of the most promising technologies, they have several flaws that a collaborative effort may address. However, digital manufacturing has established itself in the current industrial revolution and it has slowed down quality control and inspection due to the different defects linked with it. Industry 4.0, the most recent industrial revolution, emphasizes the integration of intelligent production systems and current information technologies. As a result, deep learning has received a lot of attention and has been shown to be quite effective at understanding image data. This review aims to provide a cutting-edge deep learning application of the AM approach and application. This article also addresses the current issues of data privacy and security and potential solutions to provide a more significant dimension to future studies.

Improvement Productivity and Quality by Using Lean Six Sigma: A Case Study in Mechanical Manufacturing

Intelligent integrated production systems are always of interest to production planners. However, in order to deploy and improve from a mere processing fac- tory to a processing factory with an integrated intelligent production system, it requires a team of employees, engineers, and managers to always have a spirit of innovation, continuously improving existing semi-automatic equipment into automatic ones, aiming to move towards a smart factory. The result of this research is to reduce waste in the process of assembling mechanical products by applying the DMAIC process (Define, Measure, Analysis, Improve, Con- trol), lean six sigma tools, the test of variance, hypothesis testing and exper- imental design, IBM SPSS 2020, Matlab2019a and Minitab 18 software are used for data analysis and Solid work software is used to design and simulate mechanical parts. This study shows a systematic approach to analysis to find the root cause of defects in the product assembly process, a method of diag- nosing defective products as well as the application of charts to the analysis of waste products, and improving quality by applying basic quality manage- ment tools such as Pareto charts, fishbone diagrams, value stream mapping, man-machine chart, and failure tree analysis (FTA). Clearly identify the types of waste such as components sliding on top of each other without rust, and sur- face roughness of metal products that do not meet the standards. Experimental design models and statistical models, and statistical tests are applied to Lean six sigma’s DMAIC model in the process of analyzing the machining process. The results of analysis and process improvement have improved in a reduc- tion of scrap in the assembly line of mechanical products by 59.66% per year, an increase in assembly-line productivity by 7.8% per year, and a decrease in waste costs incurred by 59.66% per year. The application of the DMAIC cycle to improve the quality of the assembly line of mechanical products, in addition to reducing waste, also reduces the quality cost of the assembly line.

Three-Dimensional Point Cloud Task-Specific Uncertainty Assessment Based on ISO 15530-3 and ISO 15530-4 Technical Specifications and Model-Based Definition Strategy

Data-driven manufacturing in Industry 4.0 demands digital metrology not only to drive the in-process quality assurance of manufactured products but also to supply reliable data to constantly adjust the manufacturing process parameters for zero-defect manufacturing processes. Better quality, improved productivity, and increased flexibility of manufacturing processes are obtained by combining intelligent production systems and advanced information technologies where in-process metrology plays a significant role. While traditional coordinate measurement machines offer strengths in performance, accuracy, and precision, they are not the most appropriate in-process measurement solutions when fast, non-contact and fully automated metrology is needed. In this way, non-contact optical 3D metrology tackles these limitations and offers some additional key advantages to deploying fully integrated 3D metrology capability to collect reliable data for their use in intelligent decision-making. However, the full adoption of 3D optical metrology in the manufacturing process depends on the establishment of metrological traceability. Thus, this article presents a practical approach to the task-specific uncertainty assessment realisation of a dense point cloud data type of measurement. Finally, it introduces an experimental exercise in which data-driven 3D point cloud automatic data acquisition and evaluation are performed through a model-based definition measurement strategy.

Skills Intelligence in the Steel Sector

To implement Intelligent Production Systems, two innovation approaches are to be combined: on the one hand, technological innovations produce intelligent solutions which harvest the potential of digital technologies and huge volumes of available data. On the other hand, social innovation processes are needed which introduce the skills perspective into innovative production systems. Skills Intelligence is a helpful concept that provides a good information base for informed decisions on skill needs and skill development. The article shows that this target can be achieved best through innovative tools which are developed based on an effective integration of different stakeholders and perspectives.

Formalization of requirements for assembly of high-precision units during process design of production of multi-product machining systems

This article considers the improvement of mathematical and methodological support of the implementation of an enlarged block of design procedures of the analysis of requirements of the assembly of high-precision products of the complex of design procedures of the system of accounting requirements for the assembly in the design of manufacturing methods of machining. The proposed modernization will allow to more efficiently perform of the design dimensional analysis of a high-precision assembly unit in an automated fashion and choose rational manufacturing methods of machining of parts in the system of automated sequencing of manufacturing methods during process design of the production of multi-nomenclature machining complexes. In addition, the proposed solutions allow for the expansion of digitalization of designing preproduction and process design and the transition to intelligent production systems. The problem posed in the presented study is relevant, since the proposed solutions allow for the expansion of digitalization of designing preproduction and process design and the transition to intelligent production systems in the future.

Assessing the implementation feasibility of intelligent production systems based on cloud computing, industrial internet of things and business social networks

PurposeThe increasing demands for customized services and frequent market variations have posed challenges to managing and controlling the manufacturing processes. Despite the developments in literature in this area, less consideration has been devoted to the growth of business social networks, cloud computing, industrial Internet of things and intelligent production systems. This study recognizes the primary factors and their implications for intelligent production systems' success. In summary, the role of cloud computing, business social network and the industrial Internet of things on intelligent production systems success has been tested.Design/methodology/approachIntelligent production systems are manufacturing systems capable of integrating the abilities of humans, machines and processes to lead the desired manufacturing goals. Therefore, identifying the factors affecting the success of the implementation of these systems is necessary and vital. On the other hand, cloud computing and the industrial Internet of things have been highly investigated and employed in several domains lately. Therefore, the impact of these two factors on the success of implementing intelligent production systems is examined. The study is descriptive, original and survey-based, depending on the nature of the application, its target and the data collection method. Also, the introduced model and the information collected were analyzed using SMART PLS. Validity has been investigated through AVE and divergent validity. The reliability of the study has been checked out through Cronbach alpha and composite reliability obtained at the standard level for the variables. In addition, the hypotheses were measured by the path coefficients and R2, T-Value and GOF.FindingsThe study identified three variables and 19 sub-indicators from the literature associated that impact improved smart production systems. The results showed that the proposed model could describe 69.5% of the intelligence production systems' success variance. The results indicated that business social networks, cloud computing and the industrial Internet of things affect intelligent production systems. They can provide a novel procedure for intelligent comprehensions and connections, on-demand utilization and effective resource sharing.Research limitations/implicationsStudy limitations are as below. First, this study ignores the interrelationships among the success of cloud computing, business social networks, Internet of things and smart production systems. Future studies can consider it. Second, we only focused on three variables. Future investigations may focus on other variables subjected to the contexts. Ultimately, there are fewer experimental investigations on the impact of underlying business social networks, cloud computing and the Internet of things on intelligent production systems' success.Originality/valueThe research and analysis outcomes are considered from various perspectives on the capacity of the new elements of Industry 4.0 for the manufacturing sector. It proposes a model for the integration of these elements. Also, original and appropriate guidelines are given for intelligent production systems investigators and professionals' designers in industry domains.

Dynamic Lean 4.0 Cyber-Physical System for Polymeric Products Manufacturing in Industry 4.0

In the polymer products industry, the adoption of the intelligent production system offers solutions to increase automation and reduce the complexity of manufacturing processes. Simultaneously with the emergence and development of the Industry 4.0 concept and techniques, numerous approaches have appeared in order to implement Lean principles and tools at the intelligent production system level, with the main purpose of increasing efficiency and eliminating waste. Lean 4.0 architectures rely on the physical and digital resources of Industry 4.0, which can be active (directly involved in the production processes of the product) and passive (which have a supporting role). The implementation of Lean 4.0 principles is based on Lean 4.0 tools, defined at cybernetic level, and on the corresponding physical assets, which intervene in the management of the production flow. The objective of this paperwork is to present the realization and implementation framework of the cyber-physical system Lean 4.0 at the manufacturing cell level into an Industry 4.0 production system.

Recent Advances in Polymer-based 3D Printing for Wastewater Treatment Application: An Overview

The introduction of Industrial Revolution (IR) 4.0 has signalled a transformation of traditional manufacturing landscape to intelligent production systems and advanced technologies. Additive manufacturing (AM) is a vital element in this latest revolution due to its remarkable potential in providing a green fabrication alternative with advanced design flexibility that surpasses the traditional manufacturing methods. At present, polymer-based additive manufacturing has drawn considerable interest in wastewater research due to its notable design freedom in manufacturing of novel, geometrically complex, and porous structures used in membrane modules and spacers, adsorption, advanced oxidation process, bioreactor, and microfluidic technology. In this work, recent advances of additively manufactured polymeric materials and 3D printing techniques used for various wastewater research are reviewed. Furthermore, the challenges in the fabrication techniques are assessed, alongside with the discussion on future perspectives of polymer-based AM technologies to develop a versatile and sustainable wastewater treatment system at commercial scale.

Subsea Electrification: Is the Tide About To Turn?

The concept of a standalone production system on the seabed with automated wellbore construction and production processes has been an industry goal for a long time. Electrification of subsea facilities and of wellbore and reservoir equipment offers many opportunities to improve operational efficiency, reduce life-of-field capital and operating expenses, and reduce carbon footprint, among other benefits. Talk of a subsea electrification revolution being “just around the corner” has been ongoing for more than 20 years. And, millions of dollars in investments and numerous joint industry projects (JIPs) over the past decade have moved the vision closer to fruition (Fig. 1). But the upstream industry continues to lag others in replacing hydraulics with electrics. The reasons echo those for slow uptake of other new technologies and methodologies—fear of change, the unknown, and failure. Now, recent events are stirring up interest and expectations. “Four to five years ago, only a very small percentage of the buying community were making big noises about the future state of the electrified subsea or subsurface,” said John Kerr, subsea production systems and technology director for Baker Hughes, in a recent interview. “During the past 18 months the narrative has increased rapidly with many more operators looking at electrification as the base case for subsea solutions. We’ve seen a groundswell of interest to the point that we now see 3-, 5-, and 7-year lookaheads with electric solutions as the base case design concept,” Kerr said. What has changed? “Electrification of subsea devices has always been a solution to solve specific technical needs,” said Kerr. “The predominant one was extreme long-distance stepouts, where once you get to 250 miles or so, the ability to pump hydraulic fluids through small umbilicals presented so much pressure loss that it became impractical to implement a hydraulic solution, so all-electric became the solution of choice. Now we are seeing much more understanding of what electrification can deliver in the commercial and operational sense. “During the last 2 years, there has also been rapid adoption of dialogue around the aspect of increased carbon credentials and carbon reduction as an advantage,” Kerr continued. “The interest is much more comprehensive, driving different behavior in concept selection for operators.” Has the pandemic played a role? The consensus of participants in a subsea electrification panel at the virtual 2020 SPE Annual Technical Conference and Exhibition (ATCE) was that unless you’re surrounded by a crisis, you’re not encouraged to change. “The moment you put someone in a crisis situation, they understand that they have to change,” said Rory Mackenzie, leader for subsea electrical technologies at Total. “2020—the pandemic, oil price collapse, and environmental issues—this created a crisis. People are now much more open to considering change.” The panelists included Alvaro Arrazola, completions engineer, Chevron, North America Upstream; Glenn-Roar Halvorsen, project manager subsea all-electric, Equinor; Christina Johansen, managing director, Norway, TechnipFMC; Samantha McClean, intelligent wells technical advisor, BP; Rory Mackenzie, head of subsea electrical technologies, Total R&D; and Thomas Scott, global product line director, intelligent production systems and reservoir information, Baker Hughes. Edward O’Malley, director of strategy and portfolio, oilfield services, Baker Hughes, moderated the session.

Research on simulation platform of intelligent production lines

In order to improve the ability of teachers and students to comprehensively apply intelligent production line technology, this paper provides an intelligent production line simulation method and system, which can simulate the operation of intelligent production line, and is easy to control and has lower cost, so as to meet the teaching needs of cultivating intelligent production line application talents. This paper introduces the development technology and implementation method of intelligent production line based on C language of single chip microcomputer.

Smart manufacturing control system based on deep reinforcement learning

The base of smart manufacturing contains two main components: cyber-physical systems and the Internet-of-Things (IoT). But the progress in machine learning, Big Data and deep learning technologies have a great impact on all areas of the modern economy, including information technology, industry. The capabilities of deep learning models associated with the ability of intelligent systems to make decisions significantly transform the key paradigms of intelligent production systems. The gap between the principles of human decision-making and intelligent systems is constantly closing. New approaches to decision-making based on deep models allow bringing the benefits of human control into production processes, and, at the same time, getting rid of errors associated with the human factor. The paper proposes an architecture for building multipurpose manufacturing systems based on deep machine learning models and reinforcement learning technologies.

Study Reviews Advances in Downhole Fiber-Optic Modeling and Analytics

This article, written by JPT Technology Editor Judy Feder, contains highlights of paper SPE 200826, “Recent Advances in Downhole Fiber-Optics Modeling and Analytics: Case Studies,” by Derek S. Bale, SPE, Rajani P. Satti, SPE, and Roberto Failla, SPE, Baker Hughes, et al., prepared for the 2020 SPE Western Regional Meeting, originally scheduled to be held in Bakersfield, California, 27 April–1 May. The paper has not been peer reviewed. The upstream industry has witnessed significant breakthroughs in developing and deploying permanent, on-demand, and distributed temperature and acoustic fiber-optic monitoring systems to optimize well completions and enhance production. Beyond steady advances in hardware, challenges associated with the analysis of distributed optical data are being addressed to enable delivery of value-driven solutions and services. The complete paper discusses a methodology for integrating intelligent completion and production systems with a modeling and analytics framework for efficient development of fiber-optic-based data-interpretation services for complex downhole environments. Introduction During the last 30 years, the industry has found novel ways to apply fiber-optic technology to monitor in-well events, operations, and critical parameters. Recently, applications including the need to maximize hydrocarbon recovery, remotely manage assets for improved cost-efficiency and safety, and reduce carbon footprint have accelerated the adoption of fiber-optic-based systems. Specific to wellbore completions, the confluence of increased durability and reliability of downhole fiber-optic systems, computer processing speed, and the ability to couple fiber sensors to completion and production equipment has led to significant growth in several applications. Fiber-optic techniques such as distributed temperature sensing (DTS) and distributed acoustic sensing (DAS) have proved particularly successful for applications such as injection and production profiling, well-integrity monitoring, leak detection, perforation cluster efficiency, and fracture monitoring. For all the benefits delivered by downhole fiber-optic technology, challenges specific to data transmission and storage remain, in particular with regard to data analysis and interpretation, that must be understood to fully enable delivery of value-generating solutions. These challenges are illustrated in Fig. 1 of the complete paper. Philosophy and Description of Solutions The solutions to the challenges described previously need to be downhole-tool-centric, cost-effective, and time-efficient. The complete paper is focused on presenting a methodology that follows a scientific and pragmatic work flow and demonstrating successful applications using a combination of intelligent downhole hardware and advanced modeling and analytics. The methodology begins with designing and developing intelligent downhole tools capable of providing the necessary data to enhance or optimize production, mitigate risk, and improve operational efficiency. Intelligent downhole tools can include interval control valves, downhole pressure and temperature gauges, connectors, control units, and cables, and are deployed into a complex downhole environment. As these smart tools are run downhole, fiber-optic cables are deployed in tandem to acquire continuous, spatially distributed data (i.e., strain, temperature, or acoustic) along the completion.

Цифрова бізнес-модель промислового підприємства

The formation of a single digital space at the enterprise greatly simplifies its management. Digitalization significantly improves productivity and has already become a top priority for business leaders and organizations around the world. Intelligent production systems fundamentally change the existing image of the industry, there is a qualitative transition to a new industrial order, in which production is managed as a single organism, in which all technological and organizational elements are interconnected.