Knowledge-Driven Based Performance Analysis of Robotic Manufacturing Cell for Design Improvement

Since 2020, the epidemic of Corona Virus Disease 2019 has swept the world, which has had a great impact on people's life. Especially for college campus life. This paper presents an emergency management system for college students. We use Java Web, big data processing and other technologies to develop, implement and test the system. The system integrates daily management and emergency management, which can meet the basic needs of teachers and students on campus. We hope to provide convenience for students and teachers in learning and management.

Artificial Engineering Intelligence Proxy Modelling for Numerical Reservoir Simulation of CO2 Injection Mohammad Altahow This study uses Artificial Intelligence (AI) and Machine Learning (ML) to improve the speed and efficiency of reservoir simulations for CO₂ storage applications. Numerical reservoir simulators, such as CMG, provide accurate predictions but demand extensive computational time and resources, making them impractical for rapid scenario analyses. To tackle this challenge, I developed a Smart Proxy Model using Artificial Neural Networks (ANNs) within the Improve software platform to replicate the results of numerical reservoir simulations conducted with CMG. The framework predicts key reservoir performance indicators, including pressure distribution and gas saturation evolution. The model was trained and validated using real static and dynamic data from a CO₂ injection field with three injection wells. To capture reservoir variability, 10 unique numerical simulation scenarios were generated, each with a different injection rate profile. Eight scenarios were used for training, while two blind test cases (Run 4 and Run 8) were for validation. The ANN-based smart proxy model has demonstrated excellent agreement with the blind simulation results, accurately reproducing both the spatial and temporal patterns of reservoir pressure and gas saturation. These outcomes confirm that AI-driven modeling can significantly reduce computational time while maintaining high accuracy. More importantly, this innovative approach empowers us to perform rapid scenario evaluations, quantify uncertainty, and make data-driven decisions in carbon capture and storage (CCS) operations.

Justified in the article is the demand for automation of technological processes at industrial enterprises in Russia. This article identifies the possibilities of using artificial intelligence and implementing smart manufacturing in the industry. Successful practices of IoT technologies at foreign enterprises are analyzed. The priorities of the leading Russian industrial companies in the field of digitalization have been revealed: expanding the use of cloud technologies; predictive analysis; IaaS services (virtual data centers and storage systems); supervisory control and data acquisition (SCADA) etc.Targets for priority manufacturing technologies within the federal project Digital Technologies have been identified: mathematical modelling, Smart design and PLM (product lifecycle management), Smart Manufacturing, manipulators and manipulation technologies; the technology of building a comprehensive architecture and strategy for the use of smart manufacturing has been specified. Smart Design provides the implementation of the concept of digital smart manufacturing through Digital Twin technology based on mathematical models of different levels of complexity and adequacy. Smart Manufacturing involves the preparation and implementation of a production process with minimal human participation based on PLM-system data, operational management of technological processes, production, and enterprise. Manipulation technologies include mathematical modelling techniques for robotic systems and the development of appropriate software.The main possibilities of forming the smart potential of industrial production have been revealed: productivity growth and improved product quality; ensuring the reliability and quality of decision-making; infrastructure security guarantees.Results: 1. The practical need to use artificial intelligence and to implement actively smart manufacturing at russian enterprises is justified. 2. The targets for the period up to 2024 on the main technological trends of the development of smart productions in Russia are specified. 3. Opportunities for the formation of smart potential and its strategic use for the development of industrial production are identified.KeywordsAutomationIndustrial enterpriseArtificial intelligenceIoT technologyDigital technologiesDgital designMathematical modellingSmart manufacturingManipulatorsManipulation technologiesArchitectureJEL CodeO10O20O30

Predictive maintenance (PdM) is one of the enabling technologies of smart manufacturing in which artificial intelligence (AI) is used to predict equipment troubles, or even better anticipate the failure of equipment, based on sensor data, machine learning algorithms, and digital twins. The traditional techniques of maintenance such as the reactive maintenance technique and the preventive maintenance technique will most probably introduce undue downtimes, wastages or redundancy. In comparison, AI-oriented predictive maintenance is a combination of real-time data analytics, industrial internet (IIoT), and existing deep learning technology will allow equipment to operate 24/7, minimize the risk of operations, and make decisions. It is a review article on the use of AI-based predictive maintenance in smart manufacturing. It talks about the establishment of PdM, how AI can develop real-time faults and use machine learning, deep learning and deep reinforcement learning procedures. Providing the advantages of PdM systems, the list below states with comment of automatic saving in case of downtime, of low cost and sustainability of aerospace case, case of automotive and case of energy. The issue of the heterogeneity of the data, non-standardization, threat to cybersecurity, and explainable AI is solved. Predictive maintenance and its combination with Industry 4.0, such as digital twins, edge computing, and blockchain, is another item on the list of the ways to make manufacturing systems more autonomous and resilient. The paper ends with a definition of future directions that include hybrid AI model, federated learning of collaborative PdM, and explainable AI model of trust and adoption. Smart manufacturing ecosystems can enable all this by adding intelligence to maintenance to move to more sustainable, reliable, and adaptable operations as defined in Industry 5.0.

The infrastructure required for collecting and distributing open data plays a vital role in the digital economy, serving as a valuable resource for fostering innovation through big data processing and artificial intelligence tech nologies. Unfortunately, several sectors, including the energy industry, lack a well-established ecosystem for implementing such innovations effectively. This paper addresses key challenges associated with the aggregation and utilization of open data in the electric power sector, while exploring potential solutions. Additionally, the paper provides a concise analysis of existing literature on organizing open data in the energy industry and examines the architectural aspects of foreign open data platforms designed for the electric power sector. The paper highlights the considerable focus on open data collection, storage, and provision, as well as the active resolution of both technical and regulatory issues in foreign contexts. Sources of open data in the Russian electric power industry and challenges associated with extracting data from heterogeneous sources are enumerated. The ecosystem approach to organizing an open data platform and the basic data monetization principles are discussed. The paper presents the key stakeholders of the platform and outlines the mechanisms for motivating their involvement. A platform architecture is proposed to meet the needs of stakeholders and ensure accelerated development of innovations in the energy sector. The use of a data lakehouse is recommended for efficient data storage in the platform. An example application on the basis of the open data platform is presented that forecasts peak load hours in regional power systems using machine learning. This application can be integrated into consumer energy management systems. Proposals are stated to refine the organizational and information support of open data of the electric power industry in order to increase the efficiency of their collection, storage, and processing on the basis of the digital platform.

At present as well as in long-term perspective, artificial intelligence systems and big data processing technologies able to have drastic influence on economic and social aspects of the society are of critical importance for the development of scientific, engineering and economic potential of any country. In this respect, assessment of the current state and the development outlooks of artificial intelligence systems is a topical issue. This article provides the results of the study of publications as well as research and development works conducted by Russian researchers between 2012 and 2017 in the ‘Artificial Intelligence’ line of research. This article proposes an analytical method for the semantic study of related terms and phrases in textual descriptions of the documents that are forming topical trends of research and their liaisons. Shifts in the subject fields, appearance of new applications for artificial intelligence methods in the period of time under consideration, and a change in the accent of the research works from fundamental (theoretical) to application-oriented kinds of scientific research are demonstrated. This approach combines both qualitative and quantitative methods of analysis and makes it possible to predict prospective areas of study. The results so obtained may be used for the analysis of development of the entire subject field as a whole as well as for individual topical trends and may be of interest for the management of scientific research and development.

With the rapid growth of quantity of data in various domains, the necessity for resourceful processing systems has become dominant. This paper investigates the Artificial Intelligence (AI) for solving real word problems using big data processing. AI sub fields such as computer vision, machine learning and natural language processing are heavily used in the field of big data processing. By using these emerging technologies, mining complex data and fetching useful information become an easy task. After pandemic, all sectors including medical and health care used the AI technologies for data processing in order to get vaccine fast. This paper also investigates how all sector planned their action to deal with new challenges and opportunities. To enhance the precision, scalability and accuracy in big data processing and manipulating tasks, the integration of multiple new technologies required like Hadoop Spark and distributed computing. This work also covers these integration and innovation so that we can take more advantage of existing things. Moreover, there exist real world case studies from a different sector such as healthcare, finance, business etc which shows the revolution in big data processing.

Software developers often meet the problem of creating new components of an application that someone probably has already produced. Without having effective reuse tools usually it is more natural to create new components from scratch than to seek for useful elements in other programmes and/or systems.In the field of simulation of Flexible Manufacturing Systems (FMS) this issue often occurs, when different systems with some similar features have to be managed. FMSs are highly automated production systems with minimal human control. FMS usually consist Flexible Manufacturing Cells (FMCs). An FMC is considered as a group of one or more machine tools, robots, adjacent mechanical elements, tool and fixture supporting workstations and an internal transport system.The components of different FMSs and FMCs are the same type of machine tools, transfer equipment, etc. In the relevant aspects they usually differ from each other only in their amounts and working parameters. This fact itself breeds the idea of reuse of FMS and FMC simulation model elements.Simulation can be used for analysing the behaviour of an existing or would-be system. It is useful for calculating utilisation statistics, finding bottlenecks, pointing out scheduling errors, and even for creating manufacturing schedules. Simulation is a very effective technique for dynamic analysis, but it is short of optimising ability. A satisfactory solution can be obtained through modelling and simulating alternative approaches with iteration.There are many concepts, object classes, and lines of source code created, that could be reused in the new applications. Usage of reuse methodology and practice will reduce our efforts in developing new systems.In this paper the reuse of the components of simulation models in a hybrid simulation and scheduling system for new simulation applications will be introduced. This simulation-scheduler system has been implemented as a combination of a traditional simulation language (SIMAN/Cinema) and an intelligent expert environment (G2). The design methodology used for model development is based on the object-oriented Rational Rose CASE tool and on a use-case model.

The article explores the possibilities of using modern digital technologies to solve the problems of ensuring an adequate level of shipping safety. In particular, the use of automatic vessel identification systems, river information services, geographic information systems, big data processing technologies, and Industry 4.0 tools is considered. Attention is focused on the need to integrate digital technologies to create a single information environment that allows for the full exchange of navigational, hydrographic and meteorological information between all participants in the transportation process. The study classifies the information needs of users of ship monitoring systems depending on the types of information (dynamic, semi-dynamic and static), and defines the functional requirements for digital ship tracking and tracking systems. It is established that to increase the efficiency of such systems, it is necessary to ensure a high speed of updating information in real time, compatibility of data exchange formats, integration with existing shipping management systems, and the possibility of using predictive models to make optimal management decisions. The results of the study confirm the feasibility of introducing integrated digital platforms that combine the capabilities of modern information technologies to improve the quality of navigation management on inland waterways. The article formulates recommendations for the further development of intelligent systems for monitoring and tracking ships, which include the integration of digital solutions based on artificial intelligence and big data processing technologies, the development of a unified digital architecture for ship traffic management, and the introduction of modern methods for forecasting and analyzing navigation risks. The results obtained are of practical importance for the design and implementation of modern information systems in the field of inland waterway transport, contributing to the competitiveness and sustainable development of the transport industry in Ukraine and the European Union

The necessity for decreasing the negative impact of the manufacturing industry has recently increased. This is getting recognized as a global challenge due to the rapid increase in life quality standards, demand, and the decrease in available resources. Thus, manufacturing, as a core of the product provision system and a fundamental pillar of civilized existence, is significantly influenced by sustainability issues. Furthermore, current manufacturing modeling and assessment criteria require intensive revisions and upgrades to keep up with these new challenges. Nearly all current manufacturing models are based on the old paradigm, which was proven to be inadequate. Therefore, manufacturing technology, along with culture and economy, are held responsible for providing new tools and opportunities for building novel resolutions towards a sustainable manufacturing concept. One of such tools is sustainability assessment measures. Revising and updating such tools is a core responsibility of the manufacturing sector to efficiently evaluate and enhance sustainable manufacturing performance. These measures should be adequate to respond to the growing sustainability concerns in pursuit of an integrated sustainability concept. The triple bottom line (TBL) that includes environment, economic, and social dimensions has usually been used to evaluate sustainability. However, there is a lack of standard sets of sustainable manufacturing performance measures. In addition to the sustainability concept, a new concept of smart manufacturing is emerging. The smart manufacturing concept takes advantage of the recent technological leap in Artificial Intelligent (AI), Cloud Computing (CC), and the Internet of Things (IoT). Although this concept offers an important step to boost the current production capabilities to meet the growing need, it is still not clear whether the two concepts of smart manufacturing and sustainability will constructively or destructively interact. Therefore, the current study aims to integrate the sustainable smart manufacturing performance by incorporating sustainable manufacturing measures and discussing current and future challenges that are faced by the manufacturing sector. In addition, the opportunities for future research incorporating sustainable smart manufacturing are also presented.

The manufacturing segment is undergoing a rapid transformation as manufacturers integrate artificial intelligence (AI) and machine learning (ML). These technologies increasingly rely on data-driven architectures, which enable manufacturers to manage large volumes of data from machines, sensors, and other sources. As a result, they optimize operations, increase productivity, and reduce costs. This paper examines the role of AI in manufacturing through the lens of data-driven architecture. It focuses on the key components, challenges, and opportunities involved in implementing these systems. The paper explores various data types and architecture models that support AI-driven manufacturing, with an emphasis on real-time analytics. It highlights key use cases in manufacturing, including predictive maintenance, quality control, and supply chain optimization, and identifies the essential components required to implement AI successfully in smart manufacturing. The paper emphasizes the critical importance of data governance, security, and scalability in developing resilient and future-proof AI systems. Finally, it reviews a data-centric framework with essential components for manufacturers aiming to leverage these technologies to drive sustained growth and innovation.

Real-time big data processing for anomaly detection: A Survey

This paper describes the ways of using program tools for design and simulation of flexible manufacturing systems (FMS) for the purpose of teaching the undergraduate course Flexible Manufacturing Systems. The concept of the course is based on the synergism of theoretical knowledge and practical hands-on experience gained by studying numerous problem cases, using common tools for description of FMS (Petri nets, max-plus algebra and matrix modeling) and by working with more specialized program tools for FMS design and simulation. An emphasis is given to the description o the program tool FlexMan that enables elegant setting of an FMS configuration, supports automatic generation of a matrix-based FMS description and consequently, enables effective off-site design and simulation of the FMS by using virtual reality models and Internet-related technologies.

This study examines the principal factors influencing organizational management utilizing Artificial Intelligence (AI) in the modern era. The primary emphasis is on the issues and developments impacting contemporary organizations worldwide after the emergence of AI. Initially, the critical elements that influence internal and external management were explored while assessing the ramifications of these factors on management. Then, the impact of numerous factors on organizational management strategies was thoroughly studied alongside adequate contemporary AI models that conceptualized these tactics and led to a competitive advantage stage. Although AI has tremendous advantages for contemporary business and management, it also has disadvantages. The human-feeling process is a fundamental practical sense that AI is limited. Recent studies demonstrated that the AI era lacks human-like creativity and empathy, a proven fact of human brains’ vitality in making intelligent decisions. Therefore, organizations’ members can be complemented by AI for better, more intelligent decision-making that will elevate the related businesses. Conversely, AI can result in ethical concerns about bias and privacy. This issue will prevent modern organizations from considering corrective actions since their decisions might not lead to the anticipated business outcomes, including but not limited to the set Key Performance Indicators (KPIs). Another side-effect of AI is the inadequate data for making the required decision without contemplating empathy. Thus, the AI shall be tackled from 360 degrees to ensure that the AI-driven decision-making system will optimize human interference while minimizing the probable impacts of the related risks, biases, and hallucination. The paper employs genuine case studies and empirical research findings to critically and analytically examine the management concerns presented by applying AI-driven decision-making practice. By harnessing AI for smarter decisions, a practical case study about the Electrical Submersible Pump (ESP) and its related technologies to extract crude oil will be demonstrated using the components and elements of the Contemporary Management Module in the AI age for a smarter-driven decision-making process. This methodology will boost modern organizations’ performances while fostering the employees’ recitals, yielding a successful business journey and evident productivity.

Application of the attention model hybrid building based on the artificial intelligence in the course of the intelligent architecture history is studied in this article. A Hadoop distributed architecture using big data processing technology which combines basic building information with the building energy consumption data for the data mining research methods, and conduct a preliminary design of a Hadoop-based public building energy consumption data mining system. The principles of the proposed model were summarized. At first, the intelligent firewall processes the decision data faster, when the harmful information invades. The intelligent firewall can monitor and also intercept the harmful information in a timelier manner. Secondly, develop a problem data processing plan, delete and identify different types of problem data, and supplement the deleted problem data according to the rules obtained by data mining. The experimental results have reflected the efficiency of the proposed model.