Quality Of Industrial Products Research Articles

The Effect of the Aeration Condition on the Liquid–Solid Material Mixing in a Stirred Tank with a Single-Layer Impeller

In order to increase industrial production quality and efficiency, it is essential to understand how the aeration and no-aeration condition affects liquid and solid material mixing in the stirred tank. Due to complicated shear flows, the related mass-transfer mechanism confronts numerous difficulties. This paper put forward an improved computational fluid dynamics and discrete element method (CFD–DEM) modeling approach to explore the effect mechanism of aeration conditions on liquid–solid material mixing. Firstly, a mass-transfer dynamic model is set up with a volume of fluid and piecewise linear interface construction (VOF–PLIC) coupling strategy to explore flow modes and vorticity evolution trends under aeration control. Then, a self-developed interphase coupling interface is utilized to modify the coupling force and porosity of the porous media model in the DEM module, and random dispersion properties of the particle phase under non-aeration and aeration are obtained. Results show that the aeration and flow-blocking components transform fluid tangential speeds into axial and radial speeds, which can improve the material mixing quality and efficiency. The mixed flow field can reach a greater turbulent process under the impeller rotation, making the particles have an intensive disorder and complex flow patterns. The enhanced motion efficiency of the vortex clusters encourages their nesting courses and improves cross-scale mixed transport. It can serve as some reference for the three-phase flow mixing mechanism, vorticity distribution law, and particle motion solution and has a general significance for battery homogeneous mixing, biopharmaceutical processes, and chemical process extraction.

Supercharging cell-level potential-induced degradation (PID) testing using a salt-enriched hybrid polymer layer

Potential-induced degradation of the shunting type (PID-s) is a well-known problem for photovoltaic modules. However, standard PID testing at the cell or module level takes 96 h due to the time it takes for the Na ions to diffuse from the glass to the solar cell through the ethylene-vinyl acetate (EVA) encapsulant. This is too slow to monitor production quality in the fast-paced solar cell industry. Moreover, it results in the encapsulation of solar cells, significantly restricting characterization options after PID testing. In this work, we replace the EVA in the PID testing setup with a salt-enriched hybrid polymer, which provides the solar cell with direct access to sodium (Na) ions while at the same time avoiding encapsulation during the testing process. Solar cells that underwent PID-s testing with the salt-enriched hybrid polymer showed a maximum degradation after only 12 min, over two orders of magnitude faster than standard PID-s testing using EVA, which took 4,300 min (∼72 h) to reach maximum degradation. Hence, PID-s testing using the salt-enriched hybrid is significantly faster than conventional techniques and avoids encapsulation, thus allowing for more detailed post-PID cell analysis. This new test shows that some solar cells can recover from PID-s, which was corroborated by extending the standard test to 300 h. This important novel insight shines a new light on the PID-s mechanism and the impact of this failure mode in field operation.

Research on the development status and countermeasures of project management Informa ionization

With the continuous development of industry, computer science and electronic information industry, our country complies with The Times and puts forward the goal and route of high-quality development. How to improve the production capacity and quality of industrial products, how to use computer science and electronic information technology for industrial empowerment, has become a part of the need to focus on research. This paper takes Henan Zhi Hui Crane Co., LTD. 's engineering management informatization process as the research object, studies the enterprise' s engineering informatization start, development, effectiveness and make a summary of experience, hoping to be promoted and help the informatization construction of small and medium-sized enterprises.

Multiscale Local and Global Feature Fusion for the Detection of Steel Surface Defects

Steel surface defects have a significant impact on the quality and performance of many industrial products and cause huge economic losses. Therefore, it is meaningful to detect steel surface defects in real time. To improve the detection performance of steel surface defects with variable scales and complex backgrounds, in this paper, a novel method for detecting steel surface defects through a multiscale local and global feature fusion mechanism is proposed. The proposed method uses a convolution operation with a downsampling mechanism in the convolutional neural network model to obtain rough multiscale feature maps. Then, a context-extraction block (CEB) is proposed to adopt self-attention learning on the feature maps extracted by the convolution operation at each scale to obtain multiscale global context information to make up for the shortcomings of convolutional neural networks (CNNs), thus forming a novel multiscale self-attention mechanism. Afterwards, using the feature pyramid structure, multiscale feature maps are fused to improve multiscale object detection. Finally, the channel and spatial attention module and the WIOU (Wise Intersection over Union) loss function are introduced. The model achieved 78.2% and 71.9% mAP respectively on the NEU-DET and GC10-DET dataset. Compared to algorithms such as Faster RCNN and EDDN, this method is effective in improving the detection performance of steel surface defects.

Impacts of industrial production and air quality by remote sensing on nitrogen dioxide concentration and related effects: An econometric approach

The high concentration of nitrogen dioxide (NO2) is to blame for West Java's poor Air Quality Index (AQI). So, this study aims to determine the influence of industrial activity as reflected by the value of its imports and exports, wind speed, and ozone (O3) on the high concentration of tropospheric NO2. The method used is the econometric Vector Error Correction Model (VECM) approach to capture the existence of a short-term and long-term relationship between tropospheric NO2 and its predictor variables. The data used in this study is in the form of monthly time series data for the 2018–2022 period sourced from satellite images (Sentinel-5P and ECMWF Climate Reanalysis) and publications of the Central Bureau of Statistics (BPS-Statistics Indonesia). The results explained that, in the short-term, tropospheric NO2 and O3 influence each other as they would in a photochemical reaction. In the long-term, exports from the industrial sector and wind speed have a significant effect on the concentration of tropospheric NO2. The short-term effect occurs directly in the first month after the shock, while the long-term effect occurs in the second month after the shock. Wind gusts originating from industrial areas cause air conditions to be even more alarming because tropospheric NO2 pollutants spread throughout the region in West Java. Based on the coefficient correlation result, the high number of pneumonia cases is one of the impacts caused by air pollution.

Deep reinforcement learning enables adaptive-image augmentation for automated optical inspection of plant rust.

This study proposes an adaptive image augmentation scheme using deep reinforcement learning (DRL) to improve the performance of a deep learning-based automated optical inspection system. The study addresses the challenge of inconsistency in the performance of single image augmentation methods. It introduces a DRL algorithm, DQN, to select the most suitable augmentation method for each image. The proposed approach extracts geometric and pixel indicators to form states, and uses DeepLab-v3+ model to verify the augmented images and generate rewards. Image augmentation methods are treated as actions, and the DQN algorithm selects the best methods based on the images and segmentation model. The study demonstrates that the proposed framework outperforms any single image augmentation method and achieves better segmentation performance than other semantic segmentation models. The framework has practical implications for developing more accurate and robust automated optical inspection systems, critical for ensuring product quality in various industries. Future research can explore the generalizability and scalability of the proposed framework to other domains and applications. The code for this application is uploaded at https://github.com/lynnkobe/Adaptive-Image-Augmentation.git.

Regulatory and functional divergence among members of Ibβfruct2, a sweet potato vacuolar invertase gene controlling starch and glucose content.

Sweet potato [Ipomoea batatas (L.) Lam.] is an important food and industrial crop. Its storage root is rich in starch, which is present in the form of granules and represents the principal storage carbohydrate in plants. Starch content is an important trait of sweet potato controlling the quality and yield of industrial products. Vacuolar invertase encoding gene Ibβfruct2 was supposed to be a key regulator of starch content in sweet potato, but its function and regulation were unclear. In this study, three Ibβfruct2 gene members were detected. Their promoters displayed differences in sequence, activity, and cis-regulatory elements and might interact with different transcription factors, indicating that the three Ibβfruct2 family members are governed by different regulatory mechanisms at the transcription level. Among them, we found that only Ibβfruct2-1 show a high expression level and promoter activity, and encodes a protein with invertase activity, and the conserved domains and three conserved motifs NDPNG, RDP, and WEC are critical to this activity. Only two and six amino acid residue variations were detected in sequences of proteins encoded by Ibβfruct2-2 and Ibβfruct2-3, respectively, compared with Ibβfruct2-1; although not within key motifs, these variations affected protein structure and affinities for the catalytic substrate, resulting in functional deficiency and low activity. Heterologous expression of Ibβfruct2-1 in Arabidopsis decreased starch content but increased glucose content in leaves, indicating Ibβfruct2-1 was a negative regulator of starch content. These findings represent an important advance in understanding the regulatory and functional divergence among duplicated genes in sweet potato, and provide critical information for functional studies and utilization of these genes in genetic improvement.

Enhancing Industrial Production Quality Through Innovations and System Improvements in Paving Block Manufacturing

The paving block industry in Aceh Besar District has experienced significant growth, leading to intensified competition among industry players. Thus, it is essential to introduce innovations in product development and operational procedures within the paving block industry to address this challenge. This community service initiative empowers partners to enhance their production systems and elevate the quality of their paving block products. The approach includes training on production procedures, guidance on standard paving block manufacturing protocols, and support in refining marketing techniques. As part of this initiative, partners have incorporated glass waste as a partial replacement for natural sand in their paving block products. This community service have demonstrated positive results, including increased sales production and improved knowledge of the paving block manufacturing process among partners. The sales volume of partner products has notably risen, with production reaching 5,720 and 8,920 units in January and May, respectively. Moreover, partners have observed sustained profit growth, experiencing a significant increase of 57.35%. These findings underscore the success of integrating glass waste as an alternative material, enabling partners to reduce production costs while maintaining product quality

Influence of Die Land Length on the Maximum Extrusion Force and Dry Ice Pellets Density in Ram Extrusion Process.

The management of waste materials, particularly non-biodegradable substances such as plastics and composites, is an increasingly pressing issue. Energy efficiency in industrial processes is crucial throughout their life cycle, including the handling of materials such as carbon dioxide (CO2), which has a significant environmental impact. This study focuses on the conversion of solid CO2 into pellets using ram extrusion, a widely used technique. The length of the die land (DL) in this process plays a critical role in determining the maximum extrusion force and the density of dry ice pellets. However, the influence of DL length on the characteristics of dry ice snow, known as compressed carbon dioxide (CCD), remains understudied. To address this research gap, the authors conducted experimental trials using a customized ram extrusion setup, varying the DL length while keeping the other parameters constant. The results demonstrate a substantial correlation between DL length and both the maximum extrusion force and dry ice pellets density. Increasing the DL length leads to a decreased extrusion force and optimized pellet density. These findings provide valuable insights for optimizing the ram extrusion process of dry ice pellets and improving waste management, energy efficiency, and product quality in industries utilizing this technique.

Optimized Placement of Frost-Measuring Sensors in Heat Exchangers via Image Processing of Frost Formation Pattern.

Heat exchangers (HXs) play a critical role in maintaining human thermal comfort and ensuring product safety and quality in various industries. However, the formation of frost on HX surfaces during cooling operations can significantly impact their performance and energy efficiency. Traditional defrosting methods primarily rely on time-based control of heaters or HX operation, overlooking the actual frost formation pattern across the surface. This pattern is influenced by ambient air conditions (humidity and temperature) and surface temperature variations. To address this issue, frost formation sensors can be strategically placed within the HX. However, the non-uniform frost pattern poses challenges in sensor placement. This study proposes an optimized sensor placement approach using computer vision and image processing techniques to analyze the frost formation pattern. Through creating a frost formation map and evaluating various sensor locations, frost detection can be optimized to control defrosting operations with higher accuracy, thereby enhancing the thermal performance and energy efficiency of HXs. The results demonstrate the effectiveness of the proposed method in accurately detecting and monitoring frost formation, providing valuable insights for sensor placement optimization. This approach presents significant potential in enhancing the overall performance and sustainability of the operation of HXs.

Recent research progress on various cooling and lubrication techniques used in sustainable hard machining: A comprehensive review

In recent research, hard machining is an essential manufacturing technology to meet the demand for various industrial product quality and precision. Several key issues like higher heat generation, higher tool wear, higher cutting force and inappropriate surface quality were found in hard machining. Researchers have examined several hard machining-related issues and developed their own suggestions for solutions. It was attempted to research the effects of tool geometry, tool materials and coatings, cutting parameters, and different cooling strategies (coolant and its delivery techniques) on various machinability responses, such as tool wear, tool life, cutting forces, surface roughness, chip morphology etc. In this review article, the different methodologies of cooling and lubrication utilized during hard machining processes have been analysed and their potential application towards sustainable machining is explored. This paper addressed the needs, challenges, and trends towards achieving sustainable hard machining of hard-to-cut materials through the application of dry, minimum quantity lubrication, cryogenic and nanofluids-assisted minimum quantity lubrication cooling strategies. It will be helpful to achieve environmental, economical, ecological and societal benefits to justify sustainable machining. Minimum quantity lubrication hard machining and nano-cutting fluid-assisted minimum quantity lubrication hard machining for sustainability will be immensely beneficial for the replacement of traditional cooling strategies for machining hard-to-cut hardened steel. Furthermore, different computational methods adopted in hard machining for optimization and modelling were also explored to get the optimal solution for industrial applications.

Region‐based fully convolutional networks with deformable convolution and attention fusion for steel surface defect detection in industrial Internet of Things

AbstractNext‐generation 6G networks will fully drive the development of the industrial Internet of Things. Steel surface defect detection as an important application in industrial Internet of Things has recently received increasing attention from the military industry, the aviation industry and other fields, which is closely related to the quality of industrial production products. However, many typical convolutional neural networks‐based methods are insensitive to the problem of unclear boundaries. In this article, the authors develop a region‐based fully convolutional networks with deformable convolution and attention fusion to adaptively learn salient features for steel surface defect detection. Specifically, deformable convolution is applied into selectively replace the standard convolution in the backbone of the region‐based fully convolutional networks, which performs significantly in scenarios with unclear defect boundaries. Moreover, convolutional block attention module is utilised in region proposal network to further enhance detection accuracy. The proposed architecture is demonstrated on two popular steel defect detection benchmarks, including NEU‐DET and GC10‐DET, which can effectively present the performance of steel surface defect detection by abundant experiments. The mean average precision on two datasets reaches 80.9% and 66.2%. The average precision of defect crazing, inclusion, patches, pitted‐surface, rolled‐in scale and scratches on NEU‐DET is 58.2%, 82.3%, 95.7%, 85.6%, 75.9%, and 87.9% respectively.

OPTIMIZATION OF DIAMOND-LIKE CARBON COATINGS FOR MECHANICAL AND TRIBOLOGICAL APPLICATIONS. REVIEW

Diamond-like carbon (DLC) coatings are used to improve the quality of industrial products due to a wide range of exceptional physical, mechanical and tribological properties. Problems that limit the scope of application of hard non-hydrogenated DLC coatings with properties closest to those of diamond are considered. The scientific literature is reviewed devoted to improving the characteristics of DLC-based wear-resistant coatings. The approaches currently used to reduce internal compressive stresses, improve adhesion to the substrate, and increase the strength of coatings are analyzed. Aspects of optimization of deposition parameters, doping, constructing the gradient and multilayer structure are considered. Based on the generalization of the results of the reviewed publications, a conclusion is made about the advantages of using coatings with a multilayer structure. A promising design undoped multilayer DLC coatings with high protective properties is discussed.

DHT: Dynamic Vision Transformer Using Hybrid Window Attention for Industrial Defect Images Classification

Industrial defect detection is gaining importance in the control of industrial product quality. Highly accurate and efficient defect detection with complex and variable industrial defect types is therefore an interesting but challenging problem. Vision transformers have been highly successful in a variety of computer vision tasks, due to their ability to capture global information in images. Nevertheless, simply capturing global information is problematic. On the one hand, because they are incapable of inductive bias as Convolutional Neural Network (CNN), transformers will have difficulty focusing on local features of defects in industrial defect image inspection tasks. On the other hand, using global computation leads to excessive memory and computational cost. To mitigate these issues, we propose a new vision transformer architecture which contains Hybrid Window Attention (HWA) and Dynamic Token Normalization (DTN). HWA, which combines pooling attention and window attention, makes the computational complexity reduced to improve efficiency. DTN enables transformers to focus on both the global information and the local features of defects, thus providing improved accuracy of industrial surface defect detection. Extensive experiments demonstrate that our Dynamic Vision Transformer (DHT) achieves 96.8% and 98.5% classification accuracy on the NEU dataset and the DAGM dataset, respectively, with a low computational complexity.

Defect transformer: An efficient hybrid transformer architecture for surface defect detection

Surface defect detection is an extremely crucial step to ensure the quality of industrial products. Nowadays, convolutional neural networks (CNNs) based on encoder–decoder architecture have achieved tremendous success in various defect detection tasks. However, the intrinsic locality of convolution prevents them from modeling long-range interactions explicitly, making it difficult to distinguish pseudo-defects in cluttered backgrounds. Recent transformers are especially skilled at learning global image dependencies, but with limited local structural information for the refined defect location. To overcome the above limitations, we incorporate CNN and transformer into an efficient hybrid transformer architecture for defect detection, termed Defect Transformer (DefT), to capture local and non-local relationships collaboratively. Specifically, in the encoder module, a convolutional stem block is firstly adopted to retain more spatial details. Then, the patch aggregation blocks are used to generate multi-scale representation with four hierarchies, each of them is followed by a series of DefT blocks, which respectively include a locally position-aware block for local position encoding, a lightweight multi-pooling self-attention to model multi-scale global contextual relationships with good computational efficiency, and a convolutional feed-forward network for feature transformation and further local information learning. Finally, a simple but effective decoder module is constructed to gradually recover spatial details from the skip connections in the encoder. Extensive experiments on three datasets demonstrate the superiority and efficiency of our method compared with other deeper and complex CNN- and transformer-based networks.

New Insights for Revealing the Effect of FDI on Pakistan's Economic Growth: Evidence from the Auto Sector

FDI is one of the important pillar for the development of economic sectors and industries for promoting the high growth of valuable products and improve the quality of useful industrial products. However, as a result of globalization, a country's economic development is no longer completely reliant on its own resources. The foreign direct investment (FDI) made by multinational corporations improves the quality of life for locals by creating employment for them and raising their level of living. FDI may have both positive and negative effects on the expansion of the economy, depending on the factors. South Asia's leading liberal economy is Pakistan. Pakistan permits 100% foreign ownership in its secondary industry due to its open economy and fast-paced private sector. Pakistan's tertiary sector has seen a sharp growth in FDI inflows when compared to other economic sectors. Pakistan's primary sector of the economy is characterised, like other emerging countries, by poor productivity, inefficient labour, energy deficits, and less incentives. However, it seems that the incentives and other variables have only had a modest impact on Pakistan's FDI levels in recent years. The economic development of Pakistan is negatively impacted by foreign direct investment. FDI has a significant influence on Pakistan's short- and long-term economic development.

Edge Intelligence with Light Weight CNN Model for Surface Defect Detection in Manufacturing Industry

Surface defect identification is essential for maintaining and improving the quality of industrial products. However, numerous environmental factors, including reflection, radiance, light, and material, affect the defect detection process, considerably increasing the difficulty of detecting surface defects. Deep Learning, a part of Artificial intelligence, can detect surface defects in the industrial sector. However, conventional deep learning techniques are heavy in terms of expensive GPU requirements to support massive computations during the defect detection process.CondenseNetV2, a Lightweight CNN-based model, which performs well on microscopic defect inspection, and can be operated on low-frequency edge devices, was proposed in this research. It provides sufficient feature extractions with little computational overhead by reusing a set of the existing Sparse Feature Reactivation module. The training data are subjected to data augmentation techniques, and the hyper-parameters of the proposed model are fine-tuned with transfer learning. The model was tested extensively with two real datasets while running on an edge device (NVIDIA Jetson Xavier Nx SOM). The experiment results confirm that the projected model can efficiently detect the faults in the real-world environment while reliably and robustly diagnosing them.

Semantic segmentation of surface defects of smooth parts based on deep convolutional neural networks

Machine vision plays an increasingly important role in industrial product quality detection. During processing, scratches, dents and other defects are inevitable on the surface of a smooth part. Although surface defects do not affect the overall performance of the product, their existence is unacceptable when a perfect product is required. The surface defect detection method based on machine vision and deep convolutional neural networks overcomes, to a certain extent, the problem of low detection efficiency, high false detection and missing detection rates in the traditional detection method. In this paper, a multistream semantic segmentation neural network is proposed to identify defects on smooth parts. Taking a seatbelt buckle as an example, the scratch and crush defects on the surface are classified. The network takes DeepLabV3+ as the framework and three types of image stream as the input of the network. In the backbone feature extraction network, the Xception structure is improved to MobilenetV2 and the convolutional block attention module (CBAM) is introduced into the decoding network, which improves the operational efficiency and accuracy. Compared with other classical networks, this network demonstrates good performance in the image dataset of the seatbelt buckle and realises fast and accurate semantic segmentation and classification of surface defects. The evaluation results of the network model have been significantly improved.

Insight into property, function, and digestion of potato starch modified by phenolic compounds with varying structures.

Influences of structural characteristics of phenolic compounds on the properties of potato starch were investigated, and their effects on the quality, function, and digestion of potato starch noodles were further determined. All testing phenolic compounds (including protocatechuic acid [PA], naringin [NA], and tannic acid [TA]) exhibited the significant capability to modify the thermal properties, rheological properties, and enzymatic hydrolysis of potato starch. The high amount of hydroxyl groups, the presence of glycoside structure, appropriate molecular size, and steric hindrance were beneficial to enhance their effects on potato starch. In addition, by changing the microstructure of starch hydrocolloids, PA, NA, and TA could affect the color, texture properties, and cooking properties of potato starch noodles. Meanwhile, PA and TA could endow potato starch noodles with remarkable antioxidant activity. Furthermore, the in vitro digestion of potato starch noodles was obviously inhibited by phenolic compounds, especially for TA. All present results suggested that structural characteristics of phenolic compounds affected their interaction affinity and combination degree with potato starch molecules, so as to modify the properties of starch and affect the quality, function, and digestion of starchy foods, which showed the valuable applications in food and chemical industries. PRACTICAL APPLICATION: The property, function, and digestion characteristics of potato starch show the significant effects on the quality of potato starch products in food industry. In present study, the thermal properties, rheological properties, and enzymatic hydrolysis of potato starch were modified by different phenolic compounds (including protocatechuic acid, naringin, and tannic acid) in varying degrees, whereas the quality, antioxidant activity, and digestion characteristic of potato starch noodles were modified by phenolic compounds with different structures. All results showed the potential application and interaction regularities of phenolic compounds as natural additives for potato starch processing in food industry.

Comparative Analysis of Hybrid Photovoltaic Thermal (PV/T) Solar Dryer

As the world’s population is increasing, the demand for food is also increasing. Drying techniques increase the life and quality of crop and industrial food products. It also improves the economic condition of farmers. Drying reduces the water stored within the product by evaporation. It can be done by the use of conventional energy and different methods. Sun radiation is used for open sun drying around the globe. Open sun drying has many disadvantages in comparison to other drying techniques. Solar drying is comparatively clean and effective. Solar dryers are of mainly four types: 1) direct solar dryer; 2) indirect solar dryers; 3) mixed mode solar dryer and 4) hybrid solar dryers. Because electric and heat energy demand is increasing day by day worldwide, PV/T solar dryer becomes an interesting and upcoming interest of research nowadays. In this review article basics of different kinds of solar dryers and recent advancements in hybrid PV/T dryers have been presented. Results for drying grapes, medicinal herb, tomato, and wood using PV/T solar dryer are discussed in this study. Variations of drying time, energy consumption, efficiency with different air temperatures, air flow rate and RH are discussed. The use of different solar collectors, solar air heater and heat storage materials with hybrid PV/T dryer have also been reviewed.