Low Processing Efficiency Research Articles

Design and implementation of an extensive-process method for improving the processing efficiency and capacity of the production logistics system

ABSTRACT Aiming at the low processing efficiency, low daily output, and long processing time of production logistics systems in small and medium-sized automobile enterprises, this study designed a novel production logistics system based on an extensive process. After improving the processing procedures, an extensive process method is adopted to classify product materials and number the process flow. Next, an automatic identification method for single and multi-process process changes is designed, and real-time control and feedback of the production process are realized. Finally, the design and implementation of the system structure and function are completed. The problem of chaotic and disorderly production in the workshop is solved, and the processing efficiency and daily output are improved. The efficiencies of the SVW Limit Arm (SVW) and FAW Limit Arm (FAW) increased by 37.33% and 39.85%, respectively. The daily output of the two products increased by 55.65% and 32.04%.

Mechanism study of ultrasonic-assisted screening of fine powder materials

The strong adsorption characteristics of fine power materials give rise to problems such as material conglomeration, blockage of sieve mesh and low process efficiency during screening, thus necessitating studies on processes like ultrasonic-assisted powder materials screening. In this study, based on the Derjaguin-Landau-Verwey-Overbeek theory, an energy balance model that takes into account the viscous force and ultrasonic vibration energy was first established; secondly, the force balance model was established as the breaking up of aggregates due to collisions between them during screening depends on the balance of forces on the aggregates; thirdly, the process of particle agglomeration and dispersion was studied via EDEM simulation; finally, the results of the experiment were used to verify the mechanism by which the ultrasonic auxiliary prevents material agglomeration and adsorption. The results showed that ultrasonic vibration can effectively scatter the particles, and improve the screening efficiency.

Porous carbon/Fe composites from waste fabric for high-efficiency electromagnetic wave absorption

• Waste fabrics are up-recycled into high-performance microwave absorbers. • Tannic acid-Fe 2+ coating catalyzes the polymer fiber into graphitized carbon fiber. • The recycling method is simple and environmentally friendly. • The minimum RL of −50.5 dB at 1.7 mm can be attained. Nowadays, a large amount of waste fabrics has brought huge environmental and resource problems, while the traditional recycling routes are downcycling with low efficiency and complex separation processes. Herein, we demonstrate a green and facile route to up-recycle waste fabrics (cotton-polyester blending or cotton fabrics) into a high-performance microwave absorber. In this design, an assistant coating was deposited onto the fiber surface of the waste fabric through the assembly of tannic acid-Fe 2+ (TA-Fe 2+ ), which effectively catalyzed the polymer fiber into highly graphitized porous carbon fiber at high temperatures. Meanwhile, the Fe nanoparticles from the reduction of Fe 2+ ions were evenly embedded on the surface of porous carbon fibers. Consequently, the resultant porous carbon/Fe composites with hierarchical microstructures displayed excellent microwave absorption performances. Typically, the microwave absorber from waste polyester-cotton blended fabric could reach a minimum reflection loss ( RL ) of -50.5 dB at a thin coating thickness of 1.7 mm, and the minimum RL from waste cotton fabric absorber reached -47.1 dB at a thickness of 2.5 mm. This work provides a new idea to convert waste fabrics into high value-added microwave absorbing materials in a simple and environmentally friendly way, which will help reuse resources and protect the environment.

Recovery of Fluoride-Rich and Silica-Rich Wastewaters as Valuable Resources: A Resource Capture Ultrafiltration-Bipolar Membrane Electrodialysis-Based Closed-Loop Process.

Traditional technologies such as precipitation and coagulation have been adopted for fluoride-rich and silica-rich wastewater treatment, respectively, but waste solid generation and low wastewater processing efficiency are still the looming concern. Efficient resource recovery technologies for different wastewater treatments are scarce for environment and industry sustainability. Herein, a resource capture ultrafiltration-bipolar membrane electrodialysis (RCUF-BMED) system was designed into a closed-loop process for simultaneous capture and recovery of fluoride and silica as sodium silicofluoride (Na2SiF6) from mixed fluoride-rich and silica-rich wastewaters, as well as achieving zero liquid discharge. This RCUF-BMED system comprised two key parts: (1) capture of fluoride and silica from two wastewaters using acid, and recovery of the Na2SiF6 using base by UF and (2) UF permeate conversion for acid/base and freshwater generation by BMED. With the optimized RCUF-BMED system, fluoride and silica can be selectively captured from wastewater with removal efficiencies higher than 99%. The Na2SiF6 recovery was around 72% with a high purity of 99.1%. The aging and cyclic experiments demonstrated the high stability and recyclability of the RCUF-BMED system. This RCUF-BMED system has successfully achieved the conversion of toxic fluoride and silica into valuable Na2SiF6 from mixed wastewaters, which shows great application potential in the industry-resource-environment nexus.

Directional High-Value Regeneration of Lithium, Iron, and Phosphorus from Spent Lithium Iron Phosphate Batteries

As lithium-ion batteries (LIBs) are undergoing unprecedented development in electric vehicles (EVs) and renewable grids, recycling spent battery disposal is becoming the dominating issue considering the urgent demand for sustainable resources and eco-friendly development. However, existing recovery methods for spent LIBs still suffer from complex processes and low processing efficiency. Herein, an effective pyroprocessing-based strategy was proposed to recycle spent lithium iron phosphate (LFP) materials, featuring full element regeneration and conversion of high-value products. Specifically, over 99% Li was extracted and converted into high purity lithium carbonate (>99%), while Fe and P were further converted into value-added Fe2P2O7 and Na4P2O7, respectively. Due to the benefits of high efficiency of metal extraction and the reuse of all valuable elements, the pyroprocessing-based strategy potentially generates the profit of 1.44 $ kg–1 of LFP batteries, over three times more than that of the conventional hydrometallurgical process, while the discharges of wastewater and residue are reduced by 66.3 and 93.9%, respectively. This study provides a new pyroprocessing-based approach to the green recovery of all elements of LFP materials in spent LFP batteries.

Sulfuric acid modified magnetorheological finishing of polycrystalline magnesium aluminate spinel

Polycrystalline magnesium aluminate spinel has excellent optical, mechanical, and chemical properties; making it an ideal material for critical optical parts under extreme conditions, and it has broad application prospects in aerospace. The high hardness and polycrystalline nature of the material bring significant challenges to high-precision spinel processing, especially in magnetorheological finishing (MRF), where low processing efficiency, polishing ripples and grain effect appear during polishing, leading to degradation of processing accuracy, surface quality and optical performance. To achieve high-efficiency and high-quality MRF of spinel, a new method of surface modification of spinel with sulfuric acid followed by MRF is proposed. The effect of sulfuric acid modification on the removal efficiency and surface roughness of spinel MRF was studied. Furthermore, the material properties of the spinel surface before and after modification were analyzed by nanoindentation, transmission electron microscopy (TEM) and X-ray photoelectron spectroscopy (XPS). Additionally, the mechanism of sulfuric acid etching assisted MRF was investigated. The sulfuric acid modification softens the spinel surface and removes the surface damage layer, and the modified layer can be used as a sacrificial layer to suppress the grain effect. The results showed that the sulfuric acid modification improved the MRF efficiency by 2.46 times, and decreased the peak-valley values of the grain effect and the average residual stress by 69.6% and 54.5%, respectively.

CO2 precooled dual phase expander refrigeration cycles for offshore and small-scale LNG production: Energy, exergy, and economic evaluation

Small-scale liquefied natural gas (LNG) processes have gained considerable attention owing to the development of offshore natural gas (NG) reserves. Low energy efficiency and high process costs are the major challenges associated with small-scale LNG processes. In this study, to reduce energy consumption and improve process economics, a dual-phase expander-based process assisted by carbon dioxide (CO2) precooling has been proposed. Three proposed cases were developed using two precooling refrigerants (propane (C3) and CO2) and dual-phase expander-based liquefaction cycles utilizing a mixture of high-boiling (ethane (C2) and propane (C3)) and low-boiling refrigerant (nitrogen (N2)). These processes were optimized with the multivariate Coggins algorithm to exploit the energy-saving opportunities. The results show that proposed case II (using CO2 in precooling and C2/N2 in liquefaction) is the most energy-efficient, with a specific energy consumption of 0.3790 kWh/kgNG. Proposed case II is 1.73% and 16.6% more energy-efficient than proposed cases I and III, respectively. The exergy analysis results show that proposed case II has the lowest exergy destruction, while the economic evaluation shows the lowest capital investment ($44.4 MM) compared to the other proposed cases. These results show the superior performance of case II among the proposed cases.

Identification and Area Information Extraction of Oat Pasture Based on GEE—A Case Study in the Shandan Racecourse (China)

Forage grass is very important for food security. The development of artificial grassland is the key to solving the shortage of forage grass. Understanding the spatial distribution of forage grass in alpine regions is of great importance for guiding animal husbandry and the rational selection of forage grass management measures. With its powerful computing power and complete image data storage, Google Earth Engine (GEE) has become a new method to address remote sensing data collection difficulties and low processing efficiency. High-resolution mapping of pasture distributions on the Tibetan Plateau (China) is still a difficult problem due to cloud disturbance and mixed planting of forage grass. Based on the GEE platform, Sentinel-2 data and three classifiers, this study successfully mapped the oat pasture area of the Shandan Racecourse (China) on the eastern Tibetan Plateau over 3 years from 2019 to 2021 at a resolution of 10 m based on cultivated land identification. In this study, the key phenology windows were determined by analysing the time series differences in vegetation indices between oat pasture and other forage grasses in the Shandan Racecourse, and monthly scale features were selected as features for oat pasture identification. The results show that the mean Overall Accuracy (OA) of Random Forest (RF) classifier, Support Vector Machine (SVM) classifier, and Classification and Regression Trees (CART) classifier are 0.80, 0.69, and 0.72 in cultivated land identification, respectively, with corresponding the Kappa coefficients of 0.74, 0.58, and 0.62. The RF classifier far outperforms the other two classifiers. In oat pasture identification, the RF, SVM and CART classifiers have high OAs of 0.98, 0.97, and 0.97 and high Kappa values of 0.95, 0.94, and 0.95, respectively. Overall, the RF classifier is more suitable for our research. The oat pasture areas in 2019, 2020 and 2021 were 347.77 km2 (15.87%), 306.19 km2 (13.97%) and 318.94 km2 (14.55%), respectively, with little change (1.9%) from year to year. The purpose of this study was to explore the identification model of forage grass area in alpine regions with a high spatial resolution, and to provide technical and methodological support for information extraction of the forage grass distribution status on the Tibetan Plateau.

Research on Distributed Calculation of Flight Parameters Based on Hadoop

With the improvement of launch vehicle technology and the increase of launch missions, under the intensive launch tasks, the contradiction between the large-scale calculation demand of flight parameters of launch vehicle and the traditional standalone calculation mode is increasingly prominent, which is mainly reflected in the slow calculation speed, low processing efficiency, limited bandwidth bottleneck, and single point fault. Big data processing architecture Hadoop’s distributed computing framework MapReduce, running in low cost cluster, is innovative applied in the large-scale calculation of launch vehicle’s flight parameters, relying on its distributed storage, coordination and load balancing mechanism. The method effectively improves computing efficiency, breakthroughs performance bottleneck and avoids single point failure. Compared with the traditional standalone deployment and pseudo-distributed cluster deployment based on Hadoop, the fully distributed cluster deployment based on Hadoop is the optimal deployment for calculating flight parameters. The results show that the parallel calculation of flight parameters based on Hadoop could save time used by 51% with the consistency of calculation results, which is the same as the standalone deployment.

Direct fabrication of single-phase multiferroic BiFe0.95Co0.05O3 films on polyimide substrates for flexible memory

• Low-temperature prepared BiFe 0.95 Co 0.05 O 3 films exhibited excellent characteristics. • A solution-based seeded photocatalytic precursor method has been developed. • The physical mechanism of the low-temperature prepared films is explained. Multiferroic memory exhibits unusual physical properties due to the electrical writing and magnetic reading of data to achieve low power consumption and efficient data processing. The flexibility of the multiferroic memory can realize its application in portable and wearable smart electronic products. The main challenge of integrating multiferroic oxides onto flexible substrates is to lower their processing temperature below the degradation temperature of plastic substrates. In this paper, the bendable inorganic BiFe 0.95 Co 0.05 O 3 polycrystalline thin films were prepared directly on flexible polyimide substrates with Pt electrode at only 350 ℃ by solution-based seeded photocatalytic precursor method. The flexible multiferroic element could bend safely to a small bending radius of 3 mm and exhibited excellent ferroelectricity, ferromagnetic, and magnetoelectric coupling characteristics at room temperature. Its remanent polarization was as high as 36 μC/cm 2 , remanent magnetization up to 4110 A/m, and magnetoelectric coupling coefficient up to 0.0135 V/A. The polarization did not deteriorate significantly during 10 3 bending-release cycles, 10 7 fatigue cycles, and 10 4 s data retention.These results prove the concept of integrating single-phase multiferroic thin films and flexible plastic substrates with low temperature by seeded photocatalytic precursor method and demonstrate the application potential of single-phase multiferroic films prepared at low temperature in the next generation of flexible multiferroic memory.

Efficiency Optimization of the Electroerosive Process in µ-WEDM of Steel MS1 Sintered Using DMLS Technology.

Although the application of mathematical optimization methods for controlling machining processes has been the subject of much research, the situation is different for µ-WEDM. This fact has prompted us to fill the gap in this field in conjunction with investigating µ-WEDM’s very low productivity and overall process efficiency, since the current trend is oriented towards achieving high quality of the machined area at a high manufacturing productivity. This paper discusses in detail the application of non-linear programming (NLP) methods using MATLAB to maximize the process performance of µ-WEDM maraging steel MS1 sintered using direct metal laser sintering (DMLS) technology. The novelty of the solution lies mainly in the selection of efficient approaches to determine the optimization maximum on the basis of a solution strategy based on multi-factor analysis. The main contribution of this paper is the obtained mathematical-statistical computational (MSC) model for predicting high productivity and quality of the machined area with respect to the the optimal efficiency of the electrical discharge process in the µ-WEDM of maraging steel MS1 material. During the experimental research and subsequent statistical processing of the measured data, a local maximum of 0.159 mm3·min−1 for the MRR parameter and a local minimum of 1.051 µm for the Rz parameter were identified simultaneously during µ-WEDM maraging steel MS1, which was in the range of the predicted optimal settings of the main technological parameters (MTP).

Advanced exergy analysis of the turbojet engine main components considering mexogenous, endogenous, exegenous, avoidable and unavoidable exergy destructions

In this study, exergy dynamic and advanced exergy analyses are applied to the turbojet engine to assess its mexogenous, endogenous, exogenous, avoidable and unavoidable exergies under the environment conditions of 15 °C temperature and 1 bar pressure. The maximum exergy point in the turbojet engine is found for the combustor in which C11H23 (Jet-A1) fuel is combusted with air, while the minimum one is determined for the air compressor head where the free air enters. The combustion chamber has the maximum fuel, product and irreversibility rates and the air compressor has the minimum fuel and product exergy values, while the minimum irreversibility is found for the turbine. Maximum improvement potential rate is found for the combustion chamber (5141.27 kW), while minimum rate is determined for the turbine of system (6.95 kW). Also, the turbine component has the highest exergy efficiency (97.20%) due to its expansion process, while combustion chamber component has the lowest exergy efficiency (55.39%) due to low efficient combustion process of the fuel. Furthermore, the mexogenous exergy destructions from maximum to minimum are found for the combustion chamber, air compressor and gas turbine units, respectively. Considering exergy dynamic analysis, the mexogenous exergy destruction rates of the combustion chamber, air compressor and gas turbine are found as 184.4 kW, 103.97 kW and 9.99 kW, respectively. Considering all results, the combustion chamber is the primer component to be handled for better efficiency and improvement.

Research on the application of enterprise internal control and risk management under the background of digital intelligence

In the era of big data, the accelerated development of digital technology has become the leading force leading a new round of scientific and technological revolution. Manufacturing enterprises have also embarked on the journey of digital transformation. Under the digital background, the traditional risk management and control mode of enterprises is facing great challenges. As the external business environment and internal business process of the enterprise are becoming more and more complex, in the process of implementing internal control and risk management, there will be related problems such as inadequate data acquisition, low information processing efficiency and lagging information transmission. Therefore, how to find a way for enterprises to survive and develop in the context of big data is particularly important for modern enterprises to effectively avoid risks and achieve high-quality sustainable development. While enjoying the convenience brought by the big data system, enterprises also face many uncertainties. Based on the theory of internal control and risk management, this paper analyzes the defects of internal control and risk management of most enterprises in the market at present, looks for the causes of risk, and puts forward specific solutions to help enterprises improve their ability to resist risks and ensure high-quality and sustainable development.

Characterization of Fenton reaction-based material removal on single crystal diamond surface

Single Crystal Diamond (SCD), a new generation semiconductor material, is used in biochip substrates, sensors, and solid-state quantum emitters for quantum communication due to its excellent optoelectronic properties. However, its low processing efficiency arises from its extremely high hardness and chemical inertness. For efficient processing of SCD, an intensive analysis of its material removal mechanism is required. The material removal characteristics of the SCD surface under different processing conditions were studied using the method of wear scratch variation on SCD surface. The results show that the Fenton reaction could induce oxidation on the SCD surface and improve its material removal, which was more suitable for SCD processing than other reaction solution environments. The addition of diamond abrasive to the Fenton reaction solution resulted in a maximum SCD surface material removal amount of 586.43 μm2, which was higher than the Fenton reaction condition alone by a factor of 1.56, higher than the effect of diamond abrasive alone by a factor of 0.81, and higher than the combination of the two by a factor of 0.06. Among those factors that influence the Fenton reaction assisted SCD material removal, the mechanical removals of abrasive and tool rod accounted for 47.2 % and 8 %, respectively, while the synergy of chemical reaction with mechanical action by the abrasive and tool rod accounted for 44.8 %. The synergistic effect of chemical reaction and mechanical removal can improve the processing efficiency of SCD.

The Power of Digitalization in the Life Sciences and Diagnostics Sectors with art image enhancement

The traditional image enhancement processing method is limited by the contrast between image background and enhancement object when processing, which leads to poor image enhancement processing effect and low processing efficiency. With the development of technology, the application of digital electronics is expanding. In order to solve the above problems, the application of digital electronics in image enhancement is explored by studying the method of Chinese character art image enhancement based on digital electronics. Digital coding and Chinese character information processing of Chinese character art images using digital electronics. Retinex enhancement algorithm is improved by using PLIP model to realize Chinese character art image enhancement process. The simulation results demonstrate that the studied image enhancement method can effectively improve the processing efficiency by about 50%, and the image enhancement process is more effective.

Rapid and Multimaterial 4D Printing of Shape-Morphing Micromachines for Narrow Micronetworks Traversing.

Micromachines with high environmental adaptability have the potential to deliver targeted drugs in complex biological networks, such as digestive, neural, and vascular networks. However, the low processing efficiency and single processing material of current 4D printing methods often limit the development and application of shape-morphing micromachines (SMMs). Here, two 4D printing strategies are proposed to fabricate SMMs with pH-responsive hydrogels for complex micro-networks traversing. On the one hand, the 3Dvortex light single exposure technique can rapidly fabricate a tubular SMM with controllable size and geometry within 0.1 s. On the other hand, the asymmetric multimaterial direct laser writing (DLW)method is used to fabricate SMMs with designable 3D structures composed of hydrogel and platinum nanoparticles (Pt NPs). Based on the presence of ferroferric oxide (Fe3 O4 ) and Pt NPs in the SMMs, efficient magnetic, bubble, and hybrid propulsion modes are achieved. Finally, it is demonstrated that the spatial shape conversion capabilities of these SMMs can be used for narrow micronetworks traversing, which will find potential applications in targeted cargo delivery in microcapillaries.

Representation for action recognition with motion vector termed as: SDQIO

The active human action motion characteristics from moving areas within a particular spectrum are conveyed by the information extraction domain of video data. Meanwhile, most of the video data frequencies are encoded with silent information that has significant redundancy, resulting in low processing efficiency in conventional video models that accept raw frames as input. Human action recognition remains challenging for the computer vision domain in videos. To address this issue, this study introduces SDQIO, a novel video content extractor that focuses on obtaining multiscale and multidimensional motion information for fast action detection. The ultimate goal of our SDQIO is to develop an effective action recognition module (SDQIO) by identifying five distinct types of action capture operators and extensively assessing their effects on movement prediction over short and long periods. Our SDQIO uses a multi-differentiation modeling approach to gather motion-type information over the whole clip. Extensive experiments revealed that our technique outperforms all other action recognition algorithms by a significant margin on standard action datasets, attaining state-of-the-art and equivalent outcomes.

Cognitive processing features of elementary school children with mathematical anxiety: Attentional control theory-based explanation

A growing body of research suggests that mathematical anxiety (MA) seriously affects an individual’s math achievement. However, few studies have focused on the cognitive mechanisms of MA in elementary school children. Based on attention control theory (ACT), this research aimed to explore the cognitive mechanism of MA in elementary school children using two studies. In Study 1, the dual-task paradigm of number memory and computation span was used to investigate the difference in processing efficiency between the high-mathematical anxiety (HMA) group and the low-mathematical anxiety (LMA) group. In total, 59 students with HMA and 54 students with LMA participated in Study 1. The results showed that students with HMA had lower processing efficiency in dealing with high-load math tasks. To further investigate the underlying mechanism of low processing efficiency for students with HMA, Study 2 explored the attention bias toward math-related stimuli of students with HMA using the Posner paradigm. In total, 48 students with HMA and 49 students with LMA participated in Study 2. The results showed that math trials put children with HMA in a state of heightened vigilance in general, which might be related to the low processing efficiency in dealing with high-load math tasks. These findings support the ACT and further reveal the mechanism of MA in elementary school children from a cognitive perspective.

Heat flux distribution model and transient temperature field analysis in grinding of helical raceway

To solve the problem of low processing efficiency of screw helical raceway by using high-efficiency grinding technology, this paper carried out theoretical, simulation, and experimental analyses on the grinding temperature which mainly affects the selection of high-efficiency grinding process parameters. First, based on the distribution characteristics of the grinding allowance of the screw helical raceway, the analysis shows that the geometry of the grinding contact area is trapezoidal. Then, based on the existing triangular heat flux distribution model of the rectangular contact area, two heat flux distribution models of the triangle and the concave triangle are proposed according to the characteristics of the trapezoidal contact area. Finally, the plane grinding experiment was carried out to correct the simulation parameters, and the modified simulation parameters were used to predict the grinding temperature of the screw raceway, so as to achieve the maximum removal efficiency and avoid burns. The analysis results show that the highest temperature in the grinding area is on the lowest point of the helical raceway, and the temperature and temperature gradient in the grinding area obtained by using the concave triangular heat flux distribution model are the highest. By comparing the process parameters for the onset of grinding burns predicted by simulation and experimentally, the temperature predicted by the concave triangle heat flux distribution model in the trapezoidal contact area is higher than the actual temperature, while the temperature predicted by the triangle heat flux distribution model in the trapezoidal contact area is lower than the actual temperature.

Real-Time Quality Control in Thin Glass Forming Using Infrared Thermography and Deep Learning

Towards the growing trends in lightweight, flexible, and optical advantages, thin glasses become key components in numerous applications such as consumer electronics like foldable smartphones, or automotive interiors. Nonisothermal glass molding promises a viable technology for the cost-efficient production of precision glass components. In the existing production, the quality of the glass products can only be accessed at the end of the hot forming process. Due to high rates of product failures often appeared in the precision glass molding processes, the current quality control of the produced optical products suffers low process efficiency. This work introduces an enabling approach for monitoring the product quality in real-time using thermography and machine learning. Specifically, the acquisition of the temperature fields of the glass components during the hot forming stage enabled by an infrared thermographic camera allows machine learning to predict the final shape of the molded components at the end of the forming process. Several transfer learning models have been investigated to demonstrate the proposed method. To further enhance the prediction performance, self-built convolutional neural network models were developed using different types of image data. By incorporating the time-series image data as an input to the learning models, the prediction performance was achieved. The model built in the present work demonstrates an excellent prediction accuracy where the difference between the measured and predicted shapes of the glass products can be kept at low double-digit micrometers. Such accuracy achieved by our self-developed machine learning model promisingly satisfy the quality control in serial productions of numerous precision optical glass components in automotive and consumer electronics sectors.