Industrial Equipment Research Articles

Bulk self-healing behaviour based on water-excited chemically bonded phosphate ceramic coating and its anti-corrosion resistance

Since industrial metal equipment is subjected to rigorous corrosion and wear, traditional inorganic coatings lack rapid self-healing to limit application. A chemically bonded phosphate ceramic (CBxPA) coating was successfully fabricated by two-component spraying, which exhibited significant corrosion protection and water-excited bulk self-healing ability. Results confirmed that MgKPO4·6H2O crystal stacking formed the denser and uniform coating, the unreacted ions were stored in the coating waiting for the water to excite to generate rapidly bulk self-healing “Whisker Clouds” layer at room temperature. The coating possessed a significant anti-corrosion self-healing efficiency value of 68.61 % in less than 36 h and showed rust-free after 500 h of exposure to a salt solution. Based on these observations, it provides a facile strategy to construct self-healing and anti-corrosion coating.

Numerical simulation of granular heat transfer within lifter rotating drum using smoothed particle hydrodynamics (SPH)

Abstract The rotating drums are typical industrial thermal processing equipment, which is widely used to heat or cool material particles in the circle fluid bed (CFB) systems. The traditional cylindrical rotating drum cannot significantly promote the heat transfer process of granules in operation, largely limiting further application. In this research, using smoothed particle hydrodynamics (SPH), two different types of lifters are employed to investigate the effects of shape, size, and number of lifters on the coefficient of heat transfer within the rotating drums by combining the flow and mixing characteristics of particles, the stress distribution, the transient velocity and temperature distribution, and the trend of the Peclet number.

Effect of TiC-CNT precipitates on the microstructure and hardness of coatings welded by the GTAW process

Carbon nanotubes (CNT)-reinforced metal matrix nanocomposites balance toughness and ductility and, therefore, can reduce maintenance costs of industrial equipment. In this sense, flux-cored made of low carbon steel filled with CNT/Ti6Al4V/CaF2 were used as filler metal to coat an A131 grade A steel sheet via GTAW process. SEM and Raman spectroscopy analyses evidence the partial phase transformation between Ti and CNT in TiC-CNT precipitates, which can act as barriers to dislocation movements in a ferritic matrix. Consequently, the hardnesses of the coatings were up to 33.9–77.4 % higher than the base metal. Therefore, incorporating TiC-CNT precipitate-based reinforcements via arc welding can improve the hardness of conventional steels.

Superhydrophobic carbon fiber composite coatings based on TC4 titanium alloy for improving corrosion resistance

Seawater corrosion poses significant challenges to marine industrial equipment, leading to a reduced lifespan and potential safety hazards. To enhance the corrosion resistance and reliability of titanium alloys in marine environments, a superhydrophobic TiO₂ nanotube-carbon fiber composite coating (SCF-TAD20) was developed through anodic oxidation, spraying, and chemical modification. SCF-TAD20 achieves a contact angle of 153°, which facilitates excellent corrosion protection due to its unique micro-nano structure, gas barrier properties, and chemical resistance. Electrochemical tests demonstrate that SCF-TAD20 increases the corrosion potential from -0.482 V to 0.0612 V and reduces the corrosion current density from 377.8 × 10-8 A/cm2 to 0.615 × 10-8 A/cm2, a four-order decrease. Its charge transfer resistance (∼8.81E6 Ω·cm2) is significantly higher than that of the substrate (∼3.03E5 Ω·cm2). The coating also performs well in seawater with varying pH levels, showing the lowest corrosion current density and a significant increase in resistive arc radius. This research presents a novel approach to enhancing TC4 titanium alloys for marine applications.

Efficient Deep Q-Learning for Industrial Equipment Calibration in Elevator Manufacturing

Efficient Deep Q-Learning for Industrial Equipment Calibration in Elevator Manufacturing

ПРИМЕНЕНИЕ ИНСТРУМЕНТОВ ПРЕДИКТИВНОЙ АНАЛИТИКИ ДЛЯ ПРОГНОЗА ВОССТАНОВЛЕНИЯ КРУПНОГАБАРИТНОГО ОБОРУДОВАНИЯ

The article presents the results of a study in the field of predictive analytics for the repair of largesized equipment in the building materials industry. New data processing techniques and the use of artificial intelligence,especially machine learning, can improve the prediction of the future state of equipment. The stages of predictive analytics application are presented, the possibility of predicting the residual resource is described and the effective use of predictive analytics is demonstrated using the example of restoring the trunnion of a ball mill. The data obtained are analyzed, on the basis of which it is concluded that repair work is necessary to increase the inter-repair life of the equipment and reduce economic losses. In conclusion, the validity of the use of predictive analytics for the repair of large-sized equipment and making optimal decisions is emphasized. The use of predictive analytics makes it possible to increase the efficiency of repair of large-sized equipment by increasing the repair life, reducing the number of downtime and preventing unplanned breakdowns. This approach is a promising and practical solution for enterprises in the building materials industry.

РАЗРАБОТКА ПРОГРАММЫ РАСЧЕТА ПАРАМЕТРОВ СХЕМЫ ЗАМЕЩЕНИЯ ТРЕХФАЗНОГО АСИНХРОННОГО ЭЛЕКТРОДВИГАТЕЛЯ

The article is devoted to developing a program for calculating the parameters of a T-shaped equivalent circuit of three-phase asynchronous electric motors. The relevance of this work is due to the need to obtain reliable results of computer simulation modelling of electric drive systems in software packages. The aim of the study is to implement in a convenient software interface an analytical technique for calculating the parameters of an asynchronous motor equivalent circuit with minimal deviations from the catalogue data. For the software calculation of the equivalent circuit parameters the paper considers analytical approximation and iterative counting techniques, since they do not require experiments on industrial electrical equipment. A comparative analysis of such techniques is performed using the example of a three-phase asynchronous motor brand BA280S4 with a power of 110 kW. The results of the compar-ative analysis show that the highest reliability of the mechanical and electromechanical characteristics is achieved in the case of using the iterative counting technique with minimizing the objective function of the sum of the weighted squares of the deviations of the control point values from the catalogue data. The authors implement the program for calculating the equivalent circuit parameters according to the selected calculation method in the Embarcadero Delphi environment. The reliable values of the electric motor parameters calculated by the program will allow for computer to model electric drive systems that best matches real objects.

Spectrophotometric Determination of Norepinephrine in Industrial Equipment Cleaning Rinse Water by Its Own Absorbance and The Tollens’ Reagent

A literature review of the available spectrophotometric methods of determination of catecholamines is presented. Based on this review, two simple spectrophotometric methods for the determination of norepinephrine bitartrate in pharmaceutical formulations and industrial equipment cleaning rinse waters were chosen and validated. The first method for the determination of norepinephrine at ppm levels utilizes the absorbance band of the solution of norepinephrine bitartrate at 279 nm. The second method for the determination of norepinephrine at ppb levels is based on the reaction of norepinephrine with the Tollens’ reagent in the presence of the cationic surfactant cetylpyridinium chloride and the colorimetric determination of the formed colloidal silver nanoparticles. The calibration graphs are linear in the range from 1 to 150 mg/l of norepinephrine for the first method and 25 µg/l to 10 mg/l for the second method. The molar attenuation coefficients are 388 and 7560 m2/mol, respectively, the limits of detection are 1.1 mg/l and 3.1 µg/l, and the limits of quantification are 3.3 mg/l and 9.4 µg/l, respectively. Both methods are selective concerning the common excipients, show good accuracy (the relative uncertainties do not exceed 4 and 7%, respectively) and precision (the relative standard deviations do not exceed 4 and 7%, respectively), and do not require lengthy sample preparation and sophisticated laboratory equipment.

High-Precision Chromatic Confocal Technologies: A Review

Chromatic confocal technology is widely used for precise, steady, and efficient displacement measurement in many industrial fields. It employs the confocal and dispersion principles to encode axial positions with the wavelengths of the reflected broad spectrum. The typical chromatic confocal sensor includes a light source, a dispersion objective, conjugate pinholes, and a spectral detection device. This study offers an overview of the current research on chromatic confocal technology. Because of its good performance in displacement detection, chromatic confocal technology has been widely used in contour measurement, biomedical imaging, and thickness measurements, as part of global and professional research. Due to its structural flexibility, it is also easily integrated into industrial equipment for in-machine and online profile measurements. It holds significant potential for future applications in industrial manufacturing and scientific research. However, there are also some challenges to be explored in terms of the broadband light source, dispersive optics design, and the balance between speed and accuracy in signal processing.

Research on hydrodynamics of the thermal agent flow for the beet pulp filtration drying

The reuse and recycling of secondary raw materials is essential to ensure sustainable development and the rational use of natural resources. One of the most important types of secondary raw materials is plant-based food waste, which is generated in large quantities at food processing plants, including sugar beet pulp, which is a valuable resource for further use. One of the main problems with using beet pulp is its high moisture content, which is ≈ 80 % wt. It is proposed to dry beet pulp by filtration drying, which is highly efficient. The calculation of industrial equipment for filtration drying should take into account the costs of overcoming the hydraulic resistance of a stationary layer of material. The aim of the article was to study the hydrodynamics of the flow of a thermal agent through a stationary layer of beet pulp of different heights and to perform computer modelling of this process. The obtained results will allow predicting the hydraulic resistance of a material layer of different heights and the required pressure drop to ensure the course of the drying process under various possible technological modes. For computer modelling of the flow of a thermal agent through a stationary layer of beet pulp, the porous media method was used in the ANSYS Fluent 2022 R2 software package. The process was simulated on the basis of the Navier-Stokes system of differential equations and the flow continuity equation with the additional use of the Darcy equation to determine the value of the hydraulic resistance of the layer ΔP. Computer simulations of the hydrodynamics of the thermal agent flow through a stationary layer of beet pulp were performed for the range of stationary layer heights H = 90÷110 mm with an interval of 5 mm. The obtained data showed a relative average deviation of 2.19 % from the experimental data, which indicates a high accuracy of calculations in this range. The study for the extended range of layer heights H = 80÷120 mm with an interval of 10 mm showed an increase in the relative average deviation to 4.09 % from the experimental data, but the results are still sufficiently accurate for practical use in the calculation of drying equipment. The obtained dependencies may be used to determine the value of the hydraulic resistance of a stationary layer of material using the filtration drying method and for practical calculations of drying equipment. Together with the results of the kinetic regularities of filtration drying, the obtained experimental data and computer modelling data on the hydrodynamics of the flow of a heat transfer agent through a material layer will allow us to calculate the optimal parameters of filtration drying for beet pulp and to assess the efficiency of filtration drying for drying beet pulp in comparison with other methods of dehydration.

System for the Acquisition and Monitoring of Energy Consumption in the Context of Industry 4.0

This paper examines methodologies for monitoring energy consumption and operational parameters of an industrial assembly line, with a particular emphasis on the utilization of virtual tools, augmented reality, and real-time data visualization. The research examines the integration of an interface and the implementation of a data acquisition system, employing augmented reality to facilitate interaction with the collected data. The proposed system employs the Node-RED interface to facilitate the establishment of connections between constituent elements. The data is stored in a database, which provides support for decision-making in the analysis of energy consumption and operational parameters of the industrial assembly line. The results demonstrate that the solution is effective in enhancing energy resource management, identifying inefficiencies, and optimizing the performance of industrial equipment. The findings indicate that the implementation of such a system can markedly enhance the industrial process.

Research of heat conductivity of quartz material

In the article the results of a study of heat conductivity and thermal resistance of quartz-containing materials were presented. The object of the study was a quartz mineral from the Aktas deposit, located in the Ulytau district of the Karaganda region. The initial size was a piece of quartz mineral measuring 5–10 mm, which was disassembled mechanically. To prepare the samples under study, quartz powder 0.2 mm thick, crushed by the electric pulse method, was used. Electropulse technology is one of the methods based on the influence of Yutkin, accompanied by the formation of a specially formed high-voltage pulsed electrical discharge inside a volume of liquid. The pulse discharge frequency of the electrohydropulse installation for grinding the mineral to the required fraction was f = 3 Hz, the pulse capacitor capacitance C = 0.4 μF, the pulse discharge voltage U = 18–37 kV. The samples under study were made in the following dimensions: width 100 mm, length 100 mm, height 25 mm. To evaluate the thermophysical properties of manufactured samples in a stationary mode, the ITP-МG4 “100” installation was used, which determines the heat conductivity and thermal resistance of materials for thermal insulation of industrial equipment and pipelines in accordance with GOST 7076, based on the thermoelectric method, and GOST 30256, based on the thermal probe method. As a result of the study, a graph of the temperature dependence of heat conductivity and thermal resistance of quartz-containing materials was obtained. Based on the results obtained, it was established that the thermophysical parameters of the sample containing quartz are lower compared to the sample consisting of cement.

WAYS TO IMPROVE THE EFFICIENCY OF TECHNOLOGICAL EQUIPMENT

Increasing the productivity of production systems and equipment in the meat industry is inextricably linked with maintenance activities. The article analyzes the influence of manufacturability, hygiene, reliability, simplicity of equipment design on the main indicators of the quality of meat products. It is shown that the Total Productive Maintenance (TRM) and Lean production strategy is an important tool for evaluating the Overall Equipment Effectiveness (OEE) indicator, which is used to evaluate the main types of losses that reduce the efficiency of equipment. The relationship of structural and mechanical characteristics of food masses, humidity of raw materials, temperature and viscosity, processing pressure, density on the optimal operating modes of the equipment is analyzed. Using the OEE concept, the time losses associated with the functioning of the equipment are determined. The types of losses are classified: downtime and changeover (availability or readiness) of equipment, short-term shutdown of equipment and reduction of processing speed (equipment performance), defective products and product losses at startup (quality) of equipment. The application of this method of studying the overall efficiency of the equipment can be applied to any technological equipment used in the meat industry and will eliminate many problems that arise during the operation of technological equipment, improve its technical and economic indicators, develop a system of measures to improve its maintenance and repair. Using this concept, it is possible to systematize the factors that reduce the efficiency of the equipment. At the same time, it is proved that information about the factors affecting the loss of equipment operation time allows you to create a system for maintenance and repair of equipment.

Bearing RUL prediction and fault diagnosis system based on parallel multi-scale MIMT lightweight model

Abstract In actual industrial production, the importance of safety production is increasingly prominent, and the degradation and failure of machinery and equipment are potential sources of safety hazards. Therefore, there is a growing trend towards real-time monitoring, prediction, and diagnosis of industrial equipment to prevent unpredictable impacts on life and property safety caused by sudden failures. To address this issue, this paper proposes a real-time degradation anomaly detection based on parallel multiscale autoencoders and a lightweight model of parallel multiscale multi-input multi-task for bearing Remaining Useful Life (RUL) prediction and fault diagnosis systems. Firstly, the multiscale autoencoder method is used to simulate actual working conditions and reconstruct the original vibration signals to build abnormal degradation detection intervals. The [0, $\mu$ +3$\sigma$] interval is utilized to judge abnormal degradation based on reconstruction errors, and the First Predict Timepoint (FPT) is determined adaptively. Secondly, a method for constructing dimensionless auxiliary datasets is proposed, which adopts a multi-input form based on deep separable convolution for feature extraction of original vibration signals, kurtosis, and peak values to improve the prediction and diagnosis performance of the lightweight model. Finally, a multi-task output method combining clustering and regression is employed to achieve RUL prediction and fault diagnosis of bearings. The proposed method overcomes the problems existing in traditional bearing RUL prediction and diagnosis methods and possesses theoretical innovation and engineering practicality. Validation on two bearing datasets confirms the effectiveness of the proposed method.

Comparative Analysis of Financial Ratios of Selected Semiconductor Equipment Firms During 2020-2023

As a matter of fact, the semiconductor equipment industry has been developing rapidly. On this basis, this research provides a comparative analysis of the financial ratios of four selected semiconductor equipment firms, i.e., Axcelis Technologies (ACLS), ACM Research (ACMR), Onto Innovation (ONTO), and Veeco Instruments (VECO), over the period from 2020 to 2023. With this in mind, the study aims to evaluate the financial health, operational efficiency, as well as market performance of these firms in order to identify the optimal investment target for value investing. According to the analysis, a specific investing target is selected. Overall, this research is useful in comparing assets and liabilities, gross revenue, stocks, bonds, return on investment, and other specifications of each of these companies that bring out their strong and weak areas. Based on the analysis, ACLS emerges as the most promising candidate due to its robust financial performance and stable market valuation.

Remaining useful life prognostics of bearings based on convolution attention networks and enhanced transformer

Rolling bearings are critical components of industrial equipment, and predicting their remaining useful life (RUL) is a challenging task. This article proposes a new end-to-end model (MDSCT) to achieve efficient and accurate prediction of bearing RUL. MDSCT uses the raw vibration signals collected by sensors for prediction, without relying on a large amount of prior knowledge. The feature extraction backbone of this model is composed of an MDSC attention module that integrates multiple parallel depth-wise separable convolutions and an efficient attention mechanism, combined with a tansformer encoder (PPSformer) optimized using patch embedding and probesparse self attention techniques. They are respectively used to capture local subtle features and global dependent features in degraded signals. This article also proposes an improved adaptive activation function AdaptH_Swish to enhance the model's ability to model nonlinear relationships. To comprehensively verify the comprehensive performance of the model, this paper conducted detailed ablation and comparative experiments using two standard datasets, PHM2012 and XJTU-SY. The experimental results not only confirm the rationality and efficiency of the model structure design, but also demonstrate significant advantages in three evaluation indicators compared to various existing methods, fully demonstrating the high generalization and strong robustness of the MDSCT model in bearing RUL prediction tasks.

Recent Progress of Corrosion Prevention Method of Magnesium Alloy

Magnesium (Mg) and its alloys have received much attention in the aerospace, transportation, automotive industry, and military equipment fields due to their unique chemical and physical properties, such as their low density and high specific strength, particularly as the lightest structural metal materials, with the opportunity to achieve the design of lighter engineering systems. With the continuous improvement of processing technology, the application scope of magnesium alloy is rapidly expanding, and market demand is increasing. However, because of its significant electronegativity (2.37 V) and loose naturally formed oxide coating, magnesium has low corrosion resistance in comparison to other structural metal elements, severely limiting its large‐scale use. This review summarizes several typical anticorrosion methods for magnesium alloys, including chemical conversion coating treatment, anodic oxide film treatment, micro‐arc oxidation treatment, laser surface treatment, ion implantation, physical vapor deposition, and superhydrophobic coating. In most cases, the corrosion resistance of magnesium and its alloys has improved, but it has a certain degree of environmental damage. It is hoped that this review will contribute to further developing magnesium alloy materials in the field of preservative coating.

Energy-Efficient Industrial Internet of Things in Green 6G Networks

The research problem of this systematic review was whether green 6G networks can integrate energy-efficient Industrial Internet of Things (IIoT) in terms of distributed artificial intelligence, green 6G pervasive edge computing communication networks and big-data-based intelligent decision algorithms. We show that sensor data fusion can be carried out in energy-efficient IoT smart industrial urban environments by cooperative perception and inference tasks. Our analyses debate on 6G wireless communication, vehicular IoT intelligent and autonomous networks, and energy-efficient algorithm and green computing technologies in smart industrial equipment and manufacturing environments. Mobile edge and cloud computing task processing capabilities of decentralized network control and power grid system monitoring were thereby analyzed. Our results and contributions clarify that sustainable energy efficiency and green power generation together with IoT decision support and smart environmental systems operate efficiently in distributed artificial intelligence 6G pervasive edge computing communication networks. PRISMA was used, and with its web-based Shiny app flow design, the search outcomes and screening procedures were integrated. A quantitative literature review was performed in July 2024 on original and review research published between 2019 and 2024. Study screening, evidence map visualization, and data extraction and reporting tools, machine learning classifiers, and reference management software were harnessed for qualitative and quantitative data, collection, management, and analysis in research synthesis. Dimensions and VOSviewer were deployed for data visualization and analysis.

Recent Advancements in Fabrication of Metal Matrix Composites: A Systematic Review.

Metal matrix composites (MMCs) are essential materials in various industries due to superior properties, such as high strength-to-weight ratios, better corrosion resistance, improved wear resistance and adaptability, developed by continuous improvements in their fabrication methods. This helps to meet the growing demand for high-performance and sustainable products. The industries that stand to gain the most are automotive and aerospace, where MMCs are used for car parts, airplane frames, and jet engines that need to be strong and lightweight. Furthermore, MMCs are being extensively used in the biomedical industry for implants and medical equipment because of their suitable mechanical integrity and corrosion resistance. Applications in heavy construction, defense, and even space exploration are noteworthy. The advancements in fabrication of MMCs have revolutionized the composite industry with their improved mechanical, tribological, and metallurgical properties. This review article offers an introduction and thorough examination of the most recent advancements (mostly within the last five years) in fabrication methods of MMCs. The novelty and modernization in the traditional processes and advanced processes are covered, along with discussing the process parameters' effects on the microstructure and properties of the composites. The review focuses on features and prospective applications of MMCs that have been greatly improved and extended due to such advancements. The most recent methods for developing MMCs, including friction stir processing (FSP), ultrasonic-assisted stir casting, and additive manufacturing, are discussed. Artificial intelligence and machine learning interventions for composite manufacturing are also included in this review. This article aims to assist researchers and scholars and encourage them to conduct future research and pursue innovations to establish the field further.

Intelligent Fault Diagnosis Method for Constant Pressure Variable Pump Based on Mel-MobileViT Lightweight Network

The sound signals of hydraulic pumps contain abundant key information reflecting their internal mechanical states. In environments characterized by high temperatures or high-speed rotation, or where sensor deployment is challenging, acoustic sensors offer non-contact and flexible arrangement features. Therefore, this study aims to develop an intelligent fault diagnosis method for hydraulic pumps based on acoustic signals. Initially, the Adaptive Chirp Mode Decomposition (ACMD) method is employed to remove environmental noise from the acoustic signals, enhancing the feature signals. Subsequently, the Mel spectrum is extracted as the acoustic fingerprint features of various fault states of the hydraulic pump, and these features are used to train the MobileViT network, achieving accurate identification of the different fault modes. The results indicate that the proposed Mel-MobileViT model effectively identifies and classifies various faults in constant pressure variable pumps, outperforming other models. This study not only provides an efficient and reliable intelligent method for the fault diagnosis of critical industrial equipment such as hydraulic pumps, but also offers new perspectives on the application of deep learning in acoustic pattern analysis.