Quality Of Industrial Products Research Articles

Kinetic study on the extraction of ethanimidic acid from hibiscus flowers using microwave assisted hydrodistillation

Microwave extraction of active compounds from plants offers an efficient alternative to traditional methods, highlighting the need to investigate its modeling and kinetic mechanisms for comprehensive understanding. This study evaluated kinetic models for the microwave-assisted hydrodistillation (MAHD) of ethanimidic acid from hibiscus flowers. Experimental data, including ethanimidic acid concentration as a function of time, power, and solid-to-liquid ratio, were analyzed using three-parameter empirical models. The second-order model demonstrated superior accuracy (R² = 0.954), outperforming the first-order model, and effectively described the two-stage extraction process: washing and diffusion. The second-order kinetic model ensures precise control of ethanimidic acid extraction, enabling efficient scalability for industrial production and consistent quality for pharmacological applications, emphasizing sustainable and cost-effective manufacturing practices. This model provides a robust framework for advancing MAHD applications in the industrial production of bioactive compounds, promoting innovation in sustainable technologies, and enhancing the economic feasibility of bioactive compound recovery.

Testing the Taguchi method to design and analyze integrated disease management strategies, for the control of late blight (Phytophthora infestans) on potato.

Identifying robust integrated pest management (IPM) strategies requires the testing of multiple factors at the same time and assessing their combined effects e.g., on disease control. This makes field-based experiments large, resource intensive and expensive. Hence, there are limits to the number of treatment combinations that can be practically tested under field conditions. Taguchi approach to design of experiments (DOE) or the Taguchi approach is commonly employed to enhance the quality of industrial products. It uses smaller experiments than classical DOE but its applicability to late blight research, and agricultural research, has not been widely evaluated. Two existing datasets, following the same protocol and investigating the effectiveness of different IPM treatments to control late blight, caused by Phytophthora infestans, on potato, were used to test the Taguchiapproach. Disease severity was quantified as area under the disease progress curve (AUDPC). The method could accurately predict the performance of a cultivar and fungicide-based integrated disease management strategy from a small dataset and identified cultivar as a key factor for disease control. Linear regression demonstrated a strong and statistically significant relationship between AUDPC values collected during the original experiments and the predicted disease severity values generated using the Taguchi method. The Taguchi approach can accurately predict disease severity, with predicted values similar to those collected during the original experiments. Moreover, associated analyses identified the most effective treatment combinations and the factors that exert the greatest influence on disease control. The relevance of this approach when designing and interpreting IPM strategies is discussed. © 2025 Society of Chemical Industry.

Classification of palm oil fruit ripeness based on AlexNet deep Convolutional Neural Network

The palm oil industry faces significant challenges in accurately classifying fruit ripeness, which is crucial for optimizing yield, quality, and profitability. Manual methods are slow and prone to errors, leading to inefficiencies and increased costs. Deep Learning, particularly the AlexNet architecture, has succeeded in image classification tasks and offers a promising solution. This study explores the implementation of AlexNet to improve the efficiency and accuracy of palm oil fruit maturity classification, thereby reducing costs and production time. We employed a dataset of 1500 images of palm oil fruits, meticulously categorized into three classes: raw, ripe, and rotten. The experimental setup involved training AlexNet and comparing its performance with a conventional Convolutional Neural Network (CNN). The results demonstrated that AlexNet significantly outperforms the traditional CNN, achieving a validation loss of 0.0261 and an accuracy of 0.9962, compared to the CNN's validation loss of 0.0377 and accuracy of 0.9925. Furthermore, AlexNet achieved superior precision, recall, and F-1 scores, each reaching 0.99, while the CNN scores were 0.98. These findings suggest that adopting AlexNet can enhance the palm oil industry's operational efficiency and product quality. The improved classification accuracy ensures that fruits are harvested at optimal ripeness, leading to better oil yield and quality. Reducing classification errors and manual labor can also lead to substantial cost savings and increased profitability. This study underscores the potential of advanced deep learning models like AlexNet in revolutionizing agricultural practices and improving industrial outcomes.

Analysis of the Impact of Process Automation and Human Resource Management on Production Completion Time and Product Quality in the Fast Food Industry in West Java

This study investigates the impact of process automation and human resource management (HRM) on production turnaround time and product quality in the fast food industry in West Java. Using a quantitative research design, data were collected from 180 respondents through a structured questionnaire and analyzed using Structural Equation Modeling with Partial Least Squares (SEM-PLS 3). The findings reveal that both process automation and HRM significantly influence production turnaround time and product quality, with automation showing a stronger impact on quality and HRM demonstrating a more substantial effect on turnaround time. The study highlights the synergistic relationship between automation and HRM, emphasizing their combined role in achieving operational efficiency and maintaining high product standards. These results provide actionable insights for fast food businesses to integrate technological advancements with strategic human resource practices, ensuring competitiveness in a dynamic market.

Strengthening MSME Products Through Legal Compliance, Brand Management, and Improved Packaging Design

The Pengabdian kepada Masyarakat (PkM) program is aimed at MSME business actors who receive assistance from the Malang district PDA, particularly those in the Lawang subdistrict and its surroundings. Currently, business actors are in the pre-startup (8.4%) and startup (32.7%) stages. This means that many small micro businesses have only recently begun or restarted operations following the COVID-19 pandemic. MSME actors face a variety of challenges, including difficulties in competing in the market, unfamiliarity with brand management and e-commerce strategies, a lack of MSME product legality permits, and a lackof good packaging label design. As a result, the opportunity for MSMEs to enter online and offline markets is being overlooked because industrial product quality cannot be separated from brand image and legal permits. The service program includes several activities, including dissemination of industrial product legality and standardization, assistance in submitting permits for the standard legality of industrial products, and assistance in creating product labelsand packaging designs.

Industrial product quality assessment using deep learning with defect attributes

Industrial product quality assessment using deep learning with defect attributes

Enhanced multiscale attentional feature fusion model for defect detection on steel surfaces

Surface defect detection has been an important part of controlling the quality of industrial products. Detecting small defects poses a persistent challenge, primarily due to the scarcity of available information. To solve this problem, this paper proposes an improved model Bi-Level Efficient Global YOLO (BEG-YOLO) based on YOLOv8x. The architecture consists of Cross Stage Partial DenseNet Global Feature Pyramid Network (CSP-GFPN), and Bi-Level Efficient Attention (BEA). CSP-GFPN is a feature pyramid network structure that employs enhanced feature fusion across scales. It extends the neck network of BEG-YOLO while enabling information sharing across different spatial scales and potentially non-adjacent semantic layers. BEA is a fusion of attention. It serves to eliminate most of the irrelevant feature key–value pair inputs at the broader feature map level, thus focusing more comprehensively on the few relevant regions that remain. Three public datasets, NEU-DET, GC10-DET, and X-SDD, are used in this experiment. According to the experimental results, better results are achieved in terms of mAP50, mAP50−95, precision, and recall compared to other good YOLO series models. Code is available at https://github.com/xyk1300332643/BEG-YOLO.

Effective natural rubber vulcanization using electron beam irradiation and DFT driven cross-linking agents

This study explores an eco-friendly and efficient method for vulcanizing natural rubber latex using electron beam irradiation, enhanced by cross-linking agents. Unlike conventional sulfur vulcanization, electron beam vulcanization can occur at room temperature, reducing the need for chemical additives and eliminating toxic waste. Both experimental and theoretical studies were performed to evaluate vulcanization efficiency, utilizing ethylene glycol dimethacrylate (EDMA) and 1,6-hexanediol diacrylate (HDDA) as cross-linking agents. Electron beam irradiation is an effective method for generating reactive radical species via homolytic cleavage of covalent bonds in natural rubber. Quantum mechanical calculations, including density functional theory (DFT) and transition state searches using linear and quadratic synchronous transit methods, were employed to examine molecular structures, energies, and reaction pathways in the vulcanization process. The findings reveal that HDDA demonstrates superior cross-linking performance compared to EDMA, making it the preferred choice for enhancing vulcanization efficiency. These results offer valuable insights for optimizing rubber manufacturing processes, potentially improving the quality and efficiency of industrial rubber production.

Comprehensive analysis of bacterial succession and flavor components in Xiguajiang with different watermelon juice supplementation during fermentation

This study investigated the physicochemical factors, bacterial communities, and volatile aromatic components in watermelon soybean pastes (WSP) prepared with different ratios of different watermelon juice to koji during the fermentation process. The results indicated that high concentration of watermelon juice could significantly increase the contents of amino acid nitrogen, reducing sugar, and free amino acids, but decreased bacterial diversity. Enterococcus, Bacillus, Enterobacter, Staphylococcus, Pediococcus, Lactobacillus, and Weissella were identified as the dominant genus bacteria in WSP samples, while Enterococcus and Pediococcus were found to account for more than 50% in WSP-4 samples. In WSP samples, this study identified 142 volatile components, the concentration of volatile components was more abundant in WSP-4 samples compared to WSP-2 and WSP-3 samples, especially in the pyrazine family. Furthermore, E-nose results showed that the influence of watermelon juice on the aroma components of WSP samples was mainly focused on the late fermentation periods, but aroma profiles were relatively similar in all WSP samples regardless of watermelon juice concentration throughout the fermentation period. Therefore, this study discovered that controlling the amount of watermelon juice and fermentation time can affect the aroma profiles of WSP samples to a certain extent, and improve the industrial production and food quality.

High temperature resistant thin film thermocouple prepared based on inkjet printing

Thin film thermocouples (TFTCs) are known for their wide temperature measurement range and high precision, showing significant potential in aerospace and steel metallurgy applications. The lack of effective manufacturing techniques has been a major barrier to their mass production. Inkjet printing, a high-quality thin film additive manufacturing process operating outside vacuum environments, offers a viable solution for large-scale practical applications of TFTCs. We successfully fabricated ITO/In2O3 high-temperature thin film thermocouples on ceramic substrates using inkjet printing technology. We developed a printable ink with excellent fluid characteristics, suitable for constructing the sensitive layer of the thermocouples. The fabricated sensors exhibited outstanding high-temperature stability and reliability, with a maximum measurement temperature exceeding 800 °C, surpassing the upper limits reported in other inkjet-printed temperature sensors. Continuous operation at 800 °C for 8 hours resulted in a low drift rate of 1.53 °C/h. The sensors demonstrated 98.55% high repeatability and 98.7%FS high accuracy across a wide temperature range, with a maximum Seebeck coefficient reaching 111 μV/°C, superior to most other high-temperature thin film sensors reported in literature. These findings show great potential in enhancing the safety of aerospace engineering equipment and ensuring the quality of products in the steel metallurgy industry.

Defect intelligent recognition of membrane product based on deep learning

Defect detection plays a crucial role in the manufacturing industry, ensuring the quality of industrial products. Despite advancements in this field, current defect detection methods face two primary challenges: (1) extracting visually similar features from the background poses difficult and (2) these methods struggle to identify tiny defects in the target objects. We present a feature enhancement module called the SE-CAR, which aims to handle the identified problems effectively. This module is designed to efficiently capture tiny defects in images, prioritize defect information features, and ultimately enhance the model’s predictive performance and accuracy in defect recognition tasks. In addition, Distance-IoU Non-Maximum Suppression is employed as a substitution for the original Non-Maximum Suppression. This enhances the recognition accuracy of bounding boxes, ensuring that the model maintains high detection accuracy even after complex scenarios. Moreover, the proposed methodology exhibits broad applicability across a wide spectrum of prevalent defect detection paradigms. In empirical experiments, we employ the YOLOv7 architecture as the foundation framework, integrating the proposed methodologies for the purpose of detecting defects in the membrane. The empirical evidence demonstrates a notable improvement in the performance of detecting defects in membrane products using the SE-CAR feature enhancement module and Distance-IoU Non-Maximum Suppression algorithm. In comparison to baseline networks, an increase in mAp by 6.29%, precision by 1.63%, and recall by 8.76%.

Temperature correction of near-infrared spectra of raw milk

Accurate milk composition analysis is crucial for improving product quality, economic efficiency, and animal health in the dairy industry. Near-infrared (NIR) spectroscopy can quantify milk composition quickly and nondestructively. However, external factors, such as temperature fluctuations, can alter the molecular vibrations and hydrogen bonding in milk, altering the NIR spectra and leading to errors in predicting key constituents such as fat, protein, and lactose. This study compares the effectiveness of Piecewise Direct Standardization (PDS), Continuous PDS (CPDS), External Parameter Orthogonalization (EPO), and Dynamic Orthogonal Projection (DOP in correcting the impact of temperature-induced variations on predictions in milk long-wave NIR spectra (LW-NIR, 1000–1700 nm).A total of 270 raw milk samples were analyzed, collecting both reflectance and transmittance spectra at five different temperatures (20 °C, 25 °C, 30 °C, 35 °C, and 40 °C). The experimental setup ensured precise temperature control and accurate spectral measurements. PLSR models were calibrated at 30 °C to predict milk fat, protein, and lactose content. The performance of these models was assessed before and after applying the temperature correction methods, with a primary focus on reflectance spectra.Results indicate that EPO and DOP significantly enhance model robustness and prediction accuracy across all temperatures, outperforming PDS and CPDS, especially for lactose prediction. These orthogonalization methods were compared against PLSR models calibrated with spectra from all temperatures. EPO and DOP showed comparable or superior performance, highlighting their effectiveness without requiring extensive temperature-specific calibration data. These findings suggest that orthogonalization methods are particularly suitable for in-line milk quality measurements under farm conditions where temperature control is challenging. This study highlights the potential of advanced chemometric techniques to improve real-time, on-farm milk composition analysis, facilitating better farm management and enhanced dairy product quality.

A Dynamic Event-Triggered Secure Monitoring and Control for a Class of Discrete-Time Markovian Jump Systems: A Plug-and-Play Architecture

In modern industrial applications, production quality, system performance, process reliability, and safety have received considerable attention. This article proposes a dynamic event-triggered attack estimator for Markovian jump stochastic systems susceptible to actuator deception attacks. Utilizing the developed estimator, the presented attack-tolerant control strategy can tolerate the effects of such attacks and ensure the mean-square convergence of the overall closed-loop system. A dynamic event-triggered mechanism is implemented on the sensor side to optimize communication efficiency. To address the potential threat of deception attacks, a plug-and-play (PnP) secure monitoring and control architecture is introduced. This architecture facilitates the seamless integration of the designed attack-tolerant controller with the nominal feedback controller, thereby enhancing system security without requiring significant modifications to the existing control structure. The practicality and effectiveness of the proposed approaches are demonstrated through experimental results on a switched boost converter circuit.

NASPrecision: Neural Architecture Search-Driven Multi-Stage Learning for surface roughness prediction in ultra-precision machining

Accurate surface roughness prediction is critical for ensuring high product quality, especially in sectors such as manufacturing, aerospace, and medical devices, where the smallest imperfections can compromise performance or safety. However, this is very challenging due to complex, non-linear interactions among variables, which is further exacerbated when working with limited and imbalanced datasets. Existing methods leveraging traditional machine learning algorithms require extensive domain knowledge for feature engineering and substantial human intervention for model selection. To address these issues, we propose a Neural Architecture Search (NAS)-Driven Multi-Stage Learning framework, named NASPrecision. This innovative approach autonomously identifies the most suitable features and models for various surface roughness prediction tasks and significantly enhances the performance by multi-stage learning. Our framework operates in three stages: (1) architecture search stage, employing NAS to automatically identify the most effective model architecture; (2) initial training stage, where we train the neural network for initial predictions; (3) refinement stage, where a subsequent model is appended to refine and capture subtle variations overlooked by the initial training stage. In light of limited and imbalanced datasets, we adopt a generative data augmentation technique to balance and generate new data by learning the underlying data distribution. We perform extensive experiments on three distinct real-world datasets, each associated with a different machining technique, and compare with various machine learning algorithms. The experimental results underscore the superiority of our framework, which achieves an average improvement of 25.7%, 22.7%, and 39.1% in terms of Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Standard Deviation of Residuals (STDR), respectively. This significant performance enhancement not only confirms the robustness of our framework but also establishes it as a generic solution for accurate surface roughness prediction. The success of this approach can lead to improved production efficiency and product quality in critical industries while also reducing the need for extensive domain knowledge and human intervention.

Sound quality improvement on industrial products

Even with efforts to reduce the noise level (dB) of industrial products, some people remain dissatisfied with the sound of products due to annoyances remained. Annoyance is closely related to the sound quality (SQ), especially in quiet environments such as hospitals and laboratories, it is essential to improve the sound quality (comfort of listening) of products. We defined a method for evaluating the sound quality of industrial products that we handle using both existing and developed sound quality indices that focus on the high-pitched sound, the prominent peak noise, the rough sound and the beat/moan noise, and improved our actual products based on the method. In this paper, we introduce the evaluation method and the sound quality improvement in actual products, including one of research activities that contributes to the business unit and has the potential to create future customer value.

ECM-YOLO: a real-time detection method of steel surface defects based on multiscale convolution.

Steel surface defects, characterized by multiple types, varied scales, and overlapping occurrences, directly impact the quality, performance, and reliability of industrial products. Proposing a high-precision and high-speed steel surface defect detection algorithm is crucial for ensuring product quality. In this regard, this paper introduces ECM-YOLO, a detection network based on YOLOv8n. First, addressing the insufficient information capture of the C2f module, the C2f enhanced multiscale convolution processing (C2f_EMSCP) module is proposed, enhancing global and local feature capture capabilities through multiscale convolutions. Second, to further enhance the network's robustness and focus on critical information, the channel prior convolutional attention (CPCA) mechanism is integrated between the backbone and neck networks to facilitate more efficient information transmission. Last, a novel, to the best of our knowledge, detection head, i.e.,multiscale simple and efficient anchor matching head (MultiSEAMHead), is proposed to mitigate accuracy issues arising from overlaps between different types of defects. Experimental results demonstrate that ECM-YOLO achieves mAPs of 78.9% and 68.2% on the NEU-DET and GC 10-DET data sets, respectively, outperforming YOLOv8n by 2.5% and 4.4%. Moreover, ECM-YOLO excels in model parameters, computational efficiency, and inference speed compared with other models. These findings highlight the applicability of ECM-YOLO for real-time defect detection in industrial settings.

TAFENet: A Two-Stage Attention-Based Feature-Enhancement Network for Strip Steel Surface Defect Detection

Strip steel serves as a crucial raw material in numerous industries, including aircraft and automobile manufacturing. Surface defects in strip steel can degrade the performance, quality, and appearance of industrial steel products. Detecting surface defects in steel strip products is challenging due to the low contrast between defects and background, small defect targets, as well as significant variations in defect sizes. To address these challenges, a two-stage attention-based feature-enhancement network (TAFENet) is proposed, wherein the first-stage feature-enhancement procedure utilizes an attentional convolutional fusion module with convolution to combine all four-level features and then strengthens the features of different levels via a residual spatial-channel attention connection module (RSC). The second-stage feature-enhancement procedure combines three-level features using an attentional self-attention fusion module and then strengthens the features using a RSC attention module. Experiments on the NEU-DET and GC10-DET datasets demonstrated that the proposed method significantly improved detection accuracy, thereby confirming the effectiveness and generalization capability of the proposed method.

A novel Pickering emulsion gels stabilized by cellulose nanofiber/dihydromyricetin composite particles: Microstructure, rheological behavior and oxidative stability

Particle concentrations (w) and oil content (Φ) are crucial factors influencing the gel stability of Pickering emulsions. To understand the stabilization mechanism comprehensively, we prepared emulsion gels stabilized by CNF/DMY composite particles at various w (0.5–1.5 wt%) and Φ (0.2–0.6, v/v). The microstructure revealed the adsorption of these particles at the oil-water interface, with excess particles forming a three-dimensional network structure in the continuous phase. Rheological studies showed that the network structure of Pickering emulsions was significantly influenced by w and Φ, resulting in improved emulsion gel strength that hindered the movement of oil droplets and oxygen in the continuous phase, thereby enhancing emulsion stability. Three scenarios for the critical strain (γco) were observed: at Φ = 0.2, γco decreased with increasing w, while at Φ = 0.4, γco increased with increasing w. At Φ = 0.6, γco remained relatively constant regardless of w. In conclusion, adjusting particle concentration and oil content enabled the control of microstructure, rheological properties, and antioxidant capacity of emulsion gels. These findings could be a valuable resource for formulating and ensuring the quality of emulsion gel-based products in the food industry.

Application Progress of Robot Precision Compensation Technology in Aviation Manufacturing

With the increasing demand for product quality and precision in aviation manufacturing industry, the application of robot technology in this field is more and more extensive. However, the accuracy of robot operation has always been a key factor restricting its application. This paper summarizes the research progress of robot precision compensation technology in the field of aviation manufacturing in recent years. This paper first introduces the application status of robot technology in aviation manufacturing, and then analyzes the principles of various precision compensation technologies and their application examples in aviation manufacturing. Through the analysis and summary of the research content, through reasonable precision compensation technology, the operation precision of the robot can be significantly improved, thus meeting the high precision requirements of aviation manufacturing. This paper also analyzes the challenges of robot precision compensation technology in aviation manufacturing field, and looks forward to the application prospect of precision compensation technology in aviation manufacturing field, which provides valuable reference for researchers and enterprises in related fields.

Dynamical Adsorption Behavior Prediction of Dried Tobacco Leaf Heterogeneous Interfaces through Simulation and Image Recognition Techniques.

The process of spraying water and flavorings on dry tobacco is an important factor in the industrial environment and product quality. Tobacco as a complex porous fiber material, the interfacial transfer process of water is complex. In this study, machine learning and image recognition techniques were utilized to quickly obtain the structural parameters of the tobacco surface and construct a cellular structure model of the tobacco surface. In situ observation of the droplet impact spreading process was carried out using a high-speed camera to explore the droplet dissipation dynamics on different surfaces. And the competing processes of droplet wetting and evaporation under the influence of surface microstructure were determined by combining experimental studies and finite element simulation calculations. Based on the characteristics of tobacco pore size distribution, the infiltration under gas-liquid two-phase action was transformed into single-phase flow transfer under capillary force, and the continuous droplet infiltration process was simulated. A parallel artificial membrane permeability measurement method of bionic tobacco waxy layer was constructed for the screening of spray dosing copenetrant. This study brings new insights into the wetting of porous fibrous materials and is important for exploring the wetting process and additive development process influenced by the microstructure.