Field Of Industrial Production Research Articles

Ellipse Detection with Applications of Convolutional Neural Network in Industrial Images

Ellipse detection has a very wide range of applications in the field of industrial production, especially in the geometric detection of metallurgical hinge pins. However, the factors in industrial images, such as small object size and incomplete ellipse in the image boundary, bring challenges to ellipse detection, which cannot be solved by existing methods. This paper proposes a method for ellipse detection in industrial images, which utilizes the extended proposal operation to prevent the loss of ellipse rotation angle features during ellipse regression. Moreover, the Gaussian angle distance conforming to the ellipse axioms is adopted and combined with smooth L1 loss as the ellipse regression loss function to enhance the prediction accuracy of the ellipse rotation angle. The effectiveness of the proposed method is demonstrated on the hinge pins dataset, with experiment results showing an AP* of 80.93% and indicating superior detection performance compared to other methods. It is thus suitable for engineering applications and can provide visual guidance for the precise measurement of ellipse-like mechanical parts.

Laser Ultrasonic Automatic Detection Method for Surface Microcracks on Metallic Cylinders

Metallic cylinders are widely used in various fields of industrial production, and the automatic detection of surface microcracks is of great significance to the subsequent grinding process. In this paper, laser-excited surface acoustic waves (SAW) are used to detect surface microcracks. Due to the dispersion of SAWs on the cylinder surface, the SAWs exhibit different polarities at different positions. In order to improve the consistency of signals and the accuracy of the modeling, the angle at which the polarity is completely reversed is selected as the detection point. A laser ultrasonic automatic detection system is established to obtain signals, and the B-scan image is drawn to determine the location of the microcrack. By comparing the time–frequency diagrams of the reflected SAWs and transmitted SAWs, the transmitted wave is chosen to establish the microcrack depth prediction model. In addition, according to the trajectory of the grinding wheel, a prediction model based on the absolute depth of the microcracks is established, and the influence of the orientation of the microcracks on the signal energy is considered. The method proposed in this paper can provide a reference for the rapid grinding of microcracks on the surface of metallic cylinders; it has the characteristics of visualization and high efficiency, and overcomes the shortcomings of the currently used eddy current testing that provides information on the depth of microcracks with difficulty.

MIMEM: A Hybrid Metaheuristic Method Solving the Resource Constrained Scheduling Problem

The Multi-Skill Resource-Constrained Project Scheduling Problem(MS-RCPSP) is an NP-Hard problem that involves scheduling activities whileaccounting for resource and technical constraints. This paper aims to present anovel hybrid algorithm called MIMEM, which combines the Memetic algorithm with the Inverse method to tackle the MS-RCPSP problem. The proposedalgorithm utilizes the inverse method to identify local extremes and then relocates the population to explore new solution spaces for further evolution. TheMIMEM algorithm is evaluated on the iMOPSE benchmark dataset, and the results demonstrate that it outperforms. The solution of the MS-RCPSP problemusing the MIMEM algorithm is a schedule that can be used for intelligent production planning in various industrial production fields instead of manual planning.

LCA Case Study of Ceramic Tableware: Ecodesign Aspects of Ceramics Production from Ancient Technology to Present Factory

Ceramic tableware, as one of the products of daily use, can be produced in multiple ways. However, they all consume primary raw materials for manufacturing and energy for firing and generate emissions and waste. To assess how different types of production cause an environmental impact, five scenarios were compared using the Life Cycle Assessment (LCA). In the first step, the energy and material flows from the following scenarios were analysed: factory (FAC), slip casting (PSC), pottery wheel (PW), high energy rate formation technique (HERF) and experimental reconstruction of the ancient technique (ANC). The results related to 1 kg of ceramics were interpreted using the perspective of ecodesign to discuss key parameters that affect environmental impacts. Considering the results in the Climate Change category, the FAC production of 1 kg of ceramics reached 3.64 kg CO2 eq. but the highest impact in this category was caused by low energy efficiency in the specific case of the PSC scenario (8.93 kg CO2 eq.). On the one hand, this paper contributes to the discussion of environmentally friendly tableware, but also serves as a case study that describes the implementation of ecodesign in the ceramic tableware industry as an important field of industrial production in the specific context of the Czech Republic, as a state, which has significant deposits of kaolin.

The Role of Innovation in the Economic Development of the Eastern Regions of the Russian Federation: Economic and Statistical Analysis

The article evaluates an innovative component of economic growth in the eastern regions of the Russian Federation using statistical analysis methods. The relevance of the matter under consideration is increasing dramatically due to the introduction of economic sanctions against Russia, which significantly restrict the import of high-tech and innovative products into our country. Based on Rosstat data for 2016–2019, a comparative analysis of the development of several areas of innovation and economic activity in the regions of the Russian Federation as part of the Far East Federal District (Far East – FE) and the Baikal Region (BR) was carried out. According to the authors, currently, in the regions under consideration, there is an imbalance in the individual components of the innovation sphere, significant heterogeneity, and uneven development of innovation and technological processes in general. The effectiveness of innovation activity in the regions under review in comparison with the indicators for Russia as a whole is low. The revealed multidirectional dynamics of innovation activity indicators in the regions and the lag of most of them from the average Russian level are connected, in the authors’ opinion, with the problems of organizational and financial support of innovation activity and insufficient budget funding allocated to stimulate it. Innovations in the FE and the BR regions were carried out mainly at the expense of the organizations' own funds. In the structure of their costs, most of the funds were allocated for the purchase of machinery and equipment, in 2019 the share of research and development costs significantly increased. The export of innovative products was carried out in less than half of the considered group of regions. The intensity of exports of innovative products in the economy as a whole, as well as in the field of industrial production, exceeds the national level and accounts for about half or more of the total export volume. In terms of the effectiveness of innovation activity among the analyzed regions, the Khabarovsk Territory is the undisputed leader, and among the outsiders are the Magadan Region and the Kamchatka Territory in 2016, the Trans-Baikal Territory in 2019. In most regions, there is an increase in the raw materials orientation of economic systems, and a decrease in the share of the manufacturing sector in the gross regional product (GRP). The results of the analysis of the role of innovation activity in the economic development of the eastern regions of the country and interregional comparative statistics, according to the authors, can be used in the development of updated and upgraded programs for the socio-economic development of the subjects of the FE and the BR of the Russian Federation, concerning present-day foreign policy and foreign economic realities.

Algorithms for Vision-Based Quality Control of Circularly Symmetric Components.

Quality inspection in the industrial production field is experiencing a strong technological development that benefits from the combination of vision-based techniques with artificial intelligence algorithms. This paper initially addresses the problem of defect identification for circularly symmetric mechanical components, characterized by the presence of periodic elements. In the specific case of knurled washers, we compare the performances of a standard algorithm for the analysis of grey-scale image with a Deep Learning (DL) approach. The standard algorithm is based on the extraction of pseudo-signals derived from the conversion of the grey scale image of concentric annuli. In the DL approach, the component inspection is shifted from the entire sample to specific areas repeated along the object profile where the defect may occur. The standard algorithm provides better results in terms of accuracy and computational time with respect to the DL approach. Nevertheless, DL reaches accuracy higher than 99% when performance is evaluated targeting the identification of damaged teeth. The possibility of extending the methods and the results to other circularly symmetrical components is analyzed and discussed.

FORMATION OF THE CRISIS MANAGEMENT CONCEPT IN WARTIME CONDITIONS

The relevance of the study is that the concept and specifics of crisis management in wartime, taking place in Ukraine, should be given great attention since war is a tragedy of the modern world society, affecting its future development. The purpose of the study is to identify the specifics and develop the concept of crisis management in wartime. Objectives of the study: 1) to formulate the concept of crisis management in wartime; 2) to develop directions for improving the concept of crisis management in wartime; 3) to reveal the possibilities of eliminating the negative impact of the crisis and improving the mechanism of sustainable development of an enterprise; 4) to find out the main tasks for an industrial enterprise to overcome the crisis situation during wartime. The object of the study is the concept of crisis management as a complex social and economic phenomenon. The subject of the study is the formation of the concept of crisis management in wartime. Methodology of the article – methods of system analysis and synthesis, structure and function, institutional, which contribute to the development of the crisis management concept in wartime, which will help enterprises to overcome the crisis and come to a stable functioning of enterprises. Results of the study: approaches to defining the essence of crisis management, crisis phenomena arising in the activities of companies have been systematized; the basic principles and directions of crisis management have been analyzed; specific environmental factors that arose during the spread of full-scale military aggression in Ukraine have been identified. The author substantiates the need to form a system of crisis management in order to ensure the financial stability and security of enterprises and create prerequisites for further sustainable development. The government should create a concept of crisis management and a mechanism for responding to emergencies in the field of industrial production in wartime. The practical significance of the study is that the development of the concept of crisis management will help Ukrainian enterprises to overcome the crisis and embark on the path of sustainable development.

АНТИКРИЗИСНОЕ УПРАВЛЕНИЕ ПРОМЫШЛЕННЫМ ПРОИЗВОДСТВОМ В УСЛОВИЯХ САНКЦИЙ

This article examines the factors that determine the systemic stability of Russian industry in the context of the imposition of sanctions by foreign countries. The purpose of the article is to identify areas of anti-crisis management of industrial production in modern conditions of sanctions pressure. As a result of the study, the role of anti-crisis management of industrial production in Russia for its restoration was revealed. Overcoming the annual indicators of unprofitable enterprises in Russia since 2016 was not possible until 2023. In addition, the number of bankrupt enterprises in the period 2007–2022. does not decrease. These indicators indicate the significant influence of external factors associated with geopolitical influence on the activities of enterprises. The study identified risks associated with the activities of industrial enterprises: the risk of a global recession, a high probability of increased sanctions pressure, the risk of a reduction in export earnings and the persistence of pro-inflationary risks. The sanctions pressure did not provoke a serious crisis in the regional industry, but created conditions for reducing dependence on imports, localizing production within the region, optimizing the structure and vector of foreign trade activities of industrial enterprises. However, due to the increased sanctions pressure in 2022 in the field of industrial production, it is necessary to formulate additional measures for anti-crisis management. Anti-crisis management under sanctions depends not only on the activities of the enterprises themselves based on internal processes, but also on the activities of the state. To build new supply chains, establish alternative channels for selling products, and actively develop import substitution, large-scale infrastructure investments are required, which depends on significant government participation in the form of investments and the formation of legislation to support the development of domestic industrial production.

О ХАРАКТЕРЕ ЗНАЧИМОСТИ МАЛОГО И СРЕДНЕГО ПРЕДПРИНИМАТЕЛЬСТВА В ЭКОНОМИКЕ ПРОМЫШЛЕННЫХ ОТРАСЛЕЙ РОССИИ

Within the framework of the implementation of various initiatives of small and medium-sized enterprises in the field of industrial production, a clear understanding of the essential characteristics of the industrial economy and the scope of its functioning is required. In addition, it is necessary to consciously approach the objects of studying economic processes and the target orientation of the market vector. The article presents the substantial nature of the economy of the industrial sector, highlights the aspect of the importance of this branch of the economy for public administration. The dynamics of the structure of small and medium-sized enterprises (SMEs) in the period, including by districts, is demonstrated. The causal relationships of the political and economic conjuncture of Russia are noted. Conclusions about the significance and potential of SMEs are formulated for the economic growth of the country.

MEMINV: A hybrid efficient approximation method solving the multi skill-resource constrained project scheduling problem.

The Multi-Skill Resource-Constrained Project Scheduling Problem (MS-RCPSP) is an NP-Hard problem that involves scheduling activities while accounting for resource and technical constraints. This paper aims to present a novel hybrid algorithm called MEMINV, which combines the Memetic algorithm with the Inverse method to tackle the MS-RCPSP problem. The proposed algorithm utilizes the inverse method to identify local extremes and then relocates the population to explore new solution spaces for further evolution. The MEMINV algorithm is evaluated on the iMOPSE benchmark dataset, and the results demonstrate that it outperforms. The solution of the MS-RCPSP problem using the MEMINV algorithm is a schedule that can be used for intelligent production planning in various industrial production fields instead of manual planning.

Research on Application of forklift dispatching intelligence in Industrial intelligence

In the modern information technology industry rapid development, forklift truck as an important content in the field of industrial production and transportation, research scholars in view of the traditional forklift control application problems are analyzed in the experiment, on the basis of consideration of work safety and comfort manipulation, forklift scheduling intelligent control system was put forward, both could regulate the working state of the vehicles and personnel, It can also use sensing technology to sense and analyze, effectively control data cost, and truly achieve the development goal of industrial intelligence. On the basis of understanding the development status of intelligent forklift dispatching, this paper analyzes the intelligent control system of forklift trucks combined with state-aware technology, and makes clear the research direction of intelligent forklift trucks in the future from the perspective of industrial intelligence development, which is also an effective measure to improve the level of social and economic growth in the new era.

Research on Dynamic Obstacle Avoidance Method of Manipulator Based on Binocular Vision

Background: With the development of robotics, more and more robots are being used in industrial production. However, as the production environment surrounding the robots becomes increasingly complex, there is a need for more intelligent industrial robots. For improving the intelligence of industrial robots, the most important thing is to ensure that they can carry out safe industrial production activities in a complex production environment. Therefore, studying the autonomous dynamic obstacle avoidance path planning of industrial robots in complex environments is of great significance for improving the intelligence of industrial robots and the application of human-machine collaboration. Objective: The main purpose of this paper is to improve the traditional artificial potential field method. It aims to improve the disadvantages of the traditional artificial potential field method, such as falling into the local minimum and failing to reach the target. Secondly, the background difference method, which is based on binocular vision and Kalman filtering algorithm, is used, and the environmental map containing the static and dynamic obstacles is obtained. After obtaining the information on the position of static and dynamic obstacles, the robot arm can make good use of the improved artificial potential field method to plan its own trajectory, thus realizing the dynamic obstacle avoidance of the robot arm in a complex environment. Methods: This paper proposes an improved artificial potential field method. First of all, in order to solve the problem of not being able to achieve the goal, the method of modifying the repulsion field function, as proposed by Wang Huili and others, has been cited. In the traditional repulsion function, the relative distance between the robot and the target is introduced. Due to this, the target point is always the minimum point of the potential field. It is necessary to ensure that the robot can reach the target position smoothly. For the local minimum problem, by adding a gravitational increase factor β, the gravitational force received is greater than the repulsive force, thereby breaking the balance of the resultant force. Finally, for the traditional artificial potential field method that does not consider the path planning problem in the presence of dynamic obstacles, the velocity vector of the dynamic obstacle is brought into the potential field function to improve the traditional artificial potential field method. Results: The robot easily falls into the local optimum during path planning, and the improved artificial potential field method overcomes this shortcoming. Therefore, the manipulator can perform autonomous dynamic obstacle avoidance path planning in an environment with dynamic obstacles, finally reaching the target point safely. Conclusions: The industrial production field faces an increasing demand for intelligent industrial robots. This paper improves on the traditional artificial potential field method based on binocular vision so that the mechanical arm can avoid dynamic obstacles autonomously in the presence of dynamic obstacles, thereby improving the degree of intelligence of the robotic arm, enabling the technology to meet the needs of future industrial development and contribute to the development of future industrial robot technology.

Quasi-3D visualization and local parameter measurement of horizontal and nearly horizontal liquid–liquid two-phase flows by using PLIF method

Horizontal and nearly horizontal liquid–liquid two-phase flows are widely encountered in petroleum, chemical, and other important industrial production fields. The three-dimensional (3D) flow visualization and the local parameter measurement are of great significance for understanding the flow dynamics and revealing the flow-pattern transition mechanism. In this paper, experiments of horizontal and nearly horizontal (+5°) liquid–liquid flow are carried out in an acrylic pipe with an inner diameter of 20 mm. Silicone oil (organic phase) and water/glycerol mixture (aqueous phase) are used as the test fluid. A novel planar laser-induced fluorescence (PLIF) system is designed to detect the flow structures. The incident direction of the laser sheet is along the radial section of the pipe, and the PLIF camera is positioned at 60° to the plane of the laser sheet, referred to as PLIF-60. In the experiment, the two-dimensional (2D) flow images at the pipe radial section are captured by the PLIF-60 system. Considering the effect of the angle between the laser sheet and the PLIF camera, the flow images are corrected to remove the horizontal and vertical distortions. The liquid–liquid stratified interface and the entrained droplets are identified in the corrected flow images. A droplet tracking method is proposed to derive the droplet diameter and its quantity distribution. Quasi-3D flow structures are reconstructed upon the accurate detection of the stratified interface and the entrained droplets. The evolution characteristics of the interface structures and the spatial distributions of droplets are studied. In addition, aqueous phase holdup and droplet entrainment ratios at pipe axial and radial sections are extracted based on the quasi-3D flow visualizations. The effects of the flow conditions and the pipe inclinations on the profiles of the aqueous phase holdup and the droplet entrainment ratio are uncovered.

Integration of scheduling and control for the no-wait batch process: A decomposition method

The batch process plays an important role in various fields of industrial production. Among them, the production processes that have specific requirements for operational continuity in each processing stage such as metallurgical, steelmaking, and pharmaceutical, should consider the no-wait constraints to meet the production reality. The batch process with no-wait constraints is a typical NP-hard problem. A new decomposition method with an improved genetic algorithm was proposed to solve the integration problem for the no-wait batch process. The integrated formulation represents a typical mixed-logic dynamic optimization (MLDO) problem which invokes logical disjunctions and operational dynamics. To solve such an MLDO problem, we transform it into a mixed-integer nonlinear program (MINLP) using the Big M reformulation and the simultaneous collocation method. Considering the structure and characteristics of the no-wait problem, an improved algorithm based on the previous GA-GBD algorithm is proposed. By collaboratively optimizing the process scheduling and dynamics, the proposed method substantially improves the overall economic performance of the batch production. At last, specific production instances are tested to demonstrate the feasibility and superiority of the proposed integration model of the no-wait batch process and optimization algorithm.

Formation of a strategy for creating and developing a competitive environment in the field of industrial production

Formation of a strategy for creating and developing a competitive environment in the field of industrial production

3D 프린팅과 구조해석을 이용한 자동용접용 캐리지 레일 설계 및 제작

Recently, 3D printing technology has been used in various industrial production fields such as architecture, automobiles, aviation, medicine, and fashion. 3D printing technology has the advantage of reducing the initial cost of product production and reducing lead time because it can manufacture complex shapes, reduce product weight, and reduce production methods and cutting raw materials through molds. In this study, an integrated manufacturing of standard automatic welding carriage rails used in shipbuilding process that could not be supported by the existing process method using 3D printing technology was attempted. The structure was manufactured using polyamide made of nylon rather than the steel used in the existing welding carriage rails to reduce the weight of the structure and reduce economic costs. Tensile tests and three-point bending tests were performed on specimens and structures made of polyamide to examine the mechanical properties of the material. Thereafter, structural stability was evaluated through structural analysis of the load of the welding robot. Through the experimental results, it was confirmed that the existing automatic welding carriage rail can be replaced with a structure manufactured with 3D printing technology.

Цифровые инновационные технологии в сельском хозяйстве

Abstract. The concept of digital economy is used in almost any sphere of the world economy. Information and communication technologies are actively spreading in economic and economic processes, contributing to the formation and development of new effective management technologies, commercial activities, effective business processes. The development of agro-industrial production is burdened by the influence of climatic, geographical and natural conditions, in connection with which it is poorly protected and has weaknesses. When in the field of industrial production there is an opportunity to provide for possible development options in a timely manner and make changes to the development strategy, then there is no such opportunity in the agricultural sector. The purpose of the study is to analyze the advantages of digital transformation of agriculture. The main research methods are system analysis, general logical research methods (comparison, generalization); economic and statistical analysis of data on the state of agriculture in the regions of Russia. Research objectives: 1) to analyze the expected results of the digital transformation of the agro-industrial complex; 2) to investigate the main stages of the Digital Agriculture project. Results. The digital transformation of the agro-industrial complex will lead to the conduct of smart agriculture – a high-tech complex of solutions that allows for maximum automation of the industry to increase the level of competitiveness and productivity, as well as attract investment in the industry. The scientific novelty of the research lies in the development of a scientific and methodological approach to assessing the integrated level of informatization of agricultural enterprises in the region in the conditions of digitalization. Based on the analysis, an assessment of the use of digital technologies in agricultural enterprises in the modern period is given.

Preparation of Nitrogen-Doped Carbon-Based Bimetallic Copper-Cobalt Catalysis Based on Deep Learning and Its Monitoring Application in Furfural Hydrogenation

In the field of catalysis, the support of the catalyst is often composed of hollow carbon materials. In order to monitor the preparation of nitrogen-doped carbon-based bimetallic copper-cobalt catalysis and its hydrogenation reaction in furfural, using m-aminophenol as the nitrogen source, formaldehyde as a carbon source, and P123 as a template agent, a nitrogen-doped bimetallic copper-cobalt mesoporous carbon catalyst Cu-Co@N-MPC-500 was synthesized by the hydrothermal method. The morphology, structure, and chemical composition of the catalyst were analyzed by means of TEM, XRD, BET, and XPS, respectively. The results show that the nitrogen-doped mesoporous carbon has a stable structure, uniform pore size distribution, and the nano-copper-cobalt particles are uniformly dispersed in the mesoporous carbon surface. Through furfural hydrogenation, the catalyst selectivity and cycle stability were discussed. Under the furfural conversion rate of 96.1%, the yield of cyclopentanone could reach 76.2%. After 5 cycles, the catalytic efficiency of the catalyst did not decrease significantly. It shows that Cu-Co@N-MPC-500 has excellent application prospects in the field of industrial production.

The Review of Electromagnetic Field Modeling Methods for Permanent-Magnet Linear Motors

Permanent-magnet linear motors (PMLMs) are widely used in various fields of industrial production, and the optimization design of the PMLM is increasingly attracting attention in order to improve the comprehensive performance of the motor. The primary problem of PMLM optimization design is the establishment of a motor model, and this paper summarizes the modeling of the PMLM electromagnetic field. First, PMLM parametric modeling methods (model-driven methods) such as the equivalent circuit method, analytical method, and finite element method, are introduced, and then non-parametric modeling methods (data-driven methods) such as the surrogate model and machine learning are introduced. Non-parametric modeling methods have the characteristics of higher accuracy and faster computation, and are the mainstream approach to motor modeling at present. However, surrogate models and traditional machine learning models such as support vector machine (SVM) and extreme learning machine (ELM) approaches have shortcomings in dealing with the high-dimensional data of motors, and some machine learning methods such as random forest (RF) require a large number of samples to obtain better modeling accuracy. Considering the modeling problem in the case of the high-dimensional electromagnetic field of the motor under the condition of a limited number of samples, this paper introduces the generative adversarial network (GAN) model and the application of the GAN in the electromagnetic field modeling of PMLM, and compares it with the mainstream machine learning models. Finally, the development of motor modeling that combines model-driven and data-driven methods is proposed.

De novo biosynthesis of p-coumaric acid and caffeic acid from carboxymethyl-cellulose by microbial co-culture strategy

BackgroundAromatic compounds, such as p-coumaric acid (p-CA) and caffeic acid, are secondary metabolites of various plants, and are widely used in diet and industry for their biological activities. In addition to expensive and unsustainable methods of plant extraction and chemical synthesis, the strategy for heterologous synthesis of aromatic compounds in microorganisms has received much attention. As the most abundant renewable resource in the world, lignocellulose is an economical and environmentally friendly alternative to edible, high-cost carbon sources such as glucose.ResultsIn the present study, carboxymethyl-cellulose (CMC) was utilized as the sole carbon source, and a metabolically engineered Saccharomyces cerevisiae strain SK10-3 was co-cultured with other recombinant S. cerevisiae strains to achieve the bioconversion of value-added products from CMC. By optimizing the inoculation ratio, interval time, and carbon source content, the final titer of p-CA in 30 g/L CMC medium was increased to 71.71 mg/L, which was 155.9-fold higher than that achieved in mono-culture. The de novo biosynthesis of caffeic acid in the CMC medium was also achieved through a three-strain co-cultivation. Caffeic acid production was up to 16.91 mg/L after optimizing the inoculation ratio of these strains.ConclusionDe novo biosynthesis of p-CA and caffeic acid from lignocellulose through a co-cultivation strategy was achieved for the first time. This study provides favorable support for the biosynthesis of more high value-added products from economical substrates. In addition, the multi-strain co-culture strategy can effectively improve the final titer of the target products, which has high application potential in the field of industrial production.