Defect Production Research Articles

Metabolic rewiring controlled by HIF-1α tunes IgA-producing B-cell differentiation and intestinal inflammation.

Germinal centers where B cells undergo clonal expansion and antibody affinity maturation are hypoxic microenvironments. However, the function of hypoxia-inducible factor (HIF)-1α in immunoglobulin production remains incompletely characterized. Here, we demonstrated that B cells lacking HIF-1α exhibited significantly lower glycolytic metabolism and impaired IgA production. Loss of HIF-1α in B cells affects IgA-producing B-cell differentiation and exacerbates dextran sodium sulfate (DSS)-induced colitis. Conversely, promoting HIF-1α stabilization via a PHD inhibitor roxadustatenhances IgA class switching and alleviates intestinal inflammation. Mechanistically, HIF-1α facilitates IgA class switching through acetyl-coenzyme A (acetyl-CoA) accumulation, which is essential for histone H3K27 acetylation at the Sα region. Consequently, supplementation with acetyl-CoA improved defective IgA production in Hif1a-deficient B cells and limited experimental colitis. Collectively, these findings highlight the critical importance of HIF-1α in IgA class switching and the potential for targeting the HIF-1α-dependent metabolic‒epigenetic axis to treat inflammatory bowel diseases and other inflammatory disorders.

Evaluation and Application of Machine Learning Techniques for Quality Improvement in Metal Product Manufacturing

This article presents a discussion of the application of machine learning methods to enhance the quality of drive shaft production, with a particular focus on the identification of critical quality issues, including cracks, scratches, and dimensional deviations, which have been observed in the final stages of machining. A variety of classification algorithms, including neural networks (NNs), bagged trees (BT), and support vector machines (SVMs), were employed to efficiently analyse and predict defects. The results show that neural networks achieved the highest accuracy (94.7%) and the fastest prediction time, thereby underscoring their efficiency in processing complex production data. The BT model demonstrated stability in its predictions with a slower prediction time, while the SVM model exhibited superior training speed, though with slightly lower accuracy. This article proposes that optimising key process parameters, such as temperature, machining speed, and the type of coolant used, can markedly reduce the prevalence of production defects. It also recommends integrating machine learning with traditional quality management techniques to create a more flexible and adaptive control system, which could help reduce production losses and enhance customer satisfaction.

An Integrated Fuzzy Delphi and Fuzzy AHP Model for Evaluating Factors Affecting Human Errors in Manual Assembly Processes

Human errors (HEs) are prevalent issues in manual assembly, leading to product defects and increased costs. Understanding and knowing the factors influencing human errors in manual assembly processes is essential for improving product quality and efficiency. This study aims to determine and rank factors influencing HEs in manual assembly processes based on expert judgments. To achieve this objective, an integrated model was developed using two multi-criteria decision-making (MCDM) techniques—specifically, the fuzzy Delphi Method (FDM) and the fuzzy Analytic Hierarchy Process (FAHP). Firstly, two rounds of the FDM were conducted to identify and categorize the primary factors contributing to HEs in manual assembly. Expert consensus with at least 75% agreement determined that 27 factors with influence scores of 0.7 or higher significantly impact HEs in these processes. After that, the priorities of the 27 influencing factors in assembly HEs were determined using a third round of the FAHP method. Data analysis was performed using SPSS 22.0 to evaluate the reliability and normality of the survey responses. This study has divided the affecting factors on assembly HEs into two levels: level 1, called main factors, and level 2, called sub-factors. Based on the final measured weights for level 1, the proposed model estimation results revealed that the most influential factors on HEs in a manual assembly are the individual factor, followed by the tool factor and the task factor. For level 2, the model results showed a lack of experience, poor instructions and procedures, and misunderstanding as the most critical factors influencing manual assembly errors. Sensitivity analysis was performed to determine how changes in model inputs or parameters affect final decisions to ensure reliable and practical results. The findings of this study provide valuable insights to help organizations develop effective strategies for reducing worker errors in manual assembly. Identifying the key and root factors contributing to assembly errors, this research offers a solid foundation for enhancing the overall quality of final products.

Business Process Clustering in Small and Medium Manufacturing Industries Based on Enterprise Resource Planning Concepts: A Systematic Literature Review

Abstract Business process management has become increasingly important in the manufacturing sector, playing a vital role in fostering productivity and facilitating organizational adaptability to technological advancements. Although each company’s business process models vary in uniqueness and complexity, certain similarities can be identified from these differences. This study employs a systematic literature review to aggregate and summarize findings on business process models, modeling languages, clustering techniques, and foundational clustering principles. Results demonstrate the dearth of research on the grouping of business process models in the manufacturing industry. Several studies have focused on sectors such as services, trading, and insurance. Research specifically addressing clustering in the manufacturing sector is limited. Existing clustering efforts in manufacturing revolve around groupings related to product defects, industrial locations, business ecosystems, and similar factors. Analysis of the methods, scope, and criteria used in grouping business process models in the manufacturing industry indicates that most approaches rely on structural or graphic similarities. Follow-up research is lacking once these business process clusters are identified. This study proposes a novel approach to grouping, integrating business process modeling with the implementation of a management information system. Business process management relies on integrating departments within the company through an enterprise resource planning (ERP) system. The next step involves proposing a conceptual framework to categorize business processes and assess the comparability of models in the manufacturing sector. Future research directions are also delineated.

Research on automated defect detection system based on computer vision

Abstract. With the intensification of market competition and consumer demand for product quality enhancement, people's development of industrialization is also more and more in-depth, the production scale and complexity of a variety of products is also increasing. However, in the process of automated production, product defects are unavoidable due to various factors such as equipment, environment, and human influence. The traditional human inspection method is inefficient, costly, and subjective, making it difficult to meet the needs of modern industrial production. Therefore, the need for an efficient, accurate and automated inspection system is urgent. Based on the current social situation, this paper will introduce the automated defect detection system based on computer vision, which is widely used in many fields, and has a great guarantee for people in the production of products with high quality and low cost, based on the needs in these areas, this paper will firstly introduce the relevant algorithms and their main contributions to the application scenarios of computer vision technology, and then introduce the key technologies of computer vision in defect detection in the key technology.

LEM-Detector: An Efficient Detector for Photovoltaic Panel Defect Detection

Photovoltaic panel defect detection presents significant challenges due to the wide range of defect scales, diverse defect types, and severe background interference, often leading to a high rate of false positives and missed detections. To address these challenges, this paper proposes the LEM-Detector, an efficient end-to-end photovoltaic panel defect detector based on the transformer architecture. To address the low detection accuracy for Crack and Star crack defects and the imbalanced dataset, a novel data augmentation method, the Linear Feature Augmentation (LFA) module, specifically designed for linear features, is introduced. LFA effectively improves model training performance and robustness. Furthermore, the Efficient Feature Enhancement Module (EFEM) is presented to enhance the receptive field, suppress redundant information, and emphasize meaningful features. To handle defects of varying scales, complementary semantic information from different feature layers is leveraged for enhanced feature fusion. A Multi-Scale Multi-Feature Pyramid Network (MMFPN) is employed to selectively aggregate boundary and category information, thereby improving the accuracy of multi-scale target recognition. Experimental results on a large-scale photovoltaic panel dataset demonstrate that the LEM-Detector achieves a detection accuracy of 94.7% for multi-scale defects, outperforming several state-of-the-art methods. This approach effectively addresses the challenges of photovoltaic panel defect detection, paving the way for more reliable and accurate defect identification systems. This research will contribute to the automatic detection of surface defects in industrial production, ultimately enhancing production efficiency.

Digital Twin Used in Real-Time Monitoring of Operations Performed on CNC Technological Equipment

This article presents the development and implementation of a real-time monitoring solution designed for CNC machines, specifically applied to 150 industrial printing machines, leveraging Digital Twin (DT) technology. The system integrates an SQL database with Android and .NET interfaces, ensuring seamless data synchronization across all machines and optimizing production processes. The real-time monitoring enables immediate reflection of operational changes, enhancing predictive maintenance and reducing machine downtime. A notable feature of the system is its 1 s average data synchronization rate per machine, managing 150 resources distributed over a 10,000 mp area. This fast synchronization improves workflow coordination, reducing production time by approximately 10%, and minimizing operator delays caused by material issues, machine malfunctions, or product defects. The integration of advanced analytics further supports real-time decision-making, predictive maintenance, and performance optimization, aligning the solution with the objectives of Industry 4.0 and Industry 5.0 initiatives. This version reflects the specific results of the research, including the 1 s synchronization rate, the 10% reduction in production time, and the scalability of the system for 150 resources.

Epitope Hierarchy in Type 1 Diabetes Pathogenesis.

Type 1 diabetes (T1D) is an autoimmune disease mediated by T cells destroying insulin-producing β cells. Identifying the antigenic epitopes targeted by autoreactive T cells is crucial for understanding pathogenesis, detecting biomarkers, and developing immunotherapies. This paper covers T-cell epitopes in T1D, focusing on pre-proinsulin and hybrid insulin peptides (HIPs) as major autoantigens. Substantial evidence highlights epitopes in the insulin B-chain and C-peptide as dominant targets for pathogenic CD4 and CD8 T cells infiltrating the islets. HIPs, formed by proinsulin fragments ligated to other peptides, constitute a novel class of epitopes detected in human and mouse islets. In addition, the paper also examines neoepitopes arising from posttranslational modifications, splice variants, and defective ribosomal products. A key challenge is differentiating genuinely pathogenic epitopes driving disease from nonpathogenic mimotopes. Identifying any essential, indispensable epitopes among this array could enable the development of antigen-specific immunotherapies targeting the root causative factors underlying T1D.

Penerapan Pengendalian Kalitas Produk Kaleng Kemasan Crab Meat 800ml Menggunakan Metode SIX Sigma Pendekatan DMAIC

Packaging can manufacturers experience a quality problem, namely with product defects / defects in every production that has not reached zero defects, especially in 800 ml crab meat packaging cans which most often experience product defects. Six Sigma can be defined as a methodology that functions for process improvement that aims to minimize defect processes and improve production quality using the DMAIC (Define, Measure, Analize, Improve, Control) approach. So that it can be analyzed from the known problems to obtain improvement recommendations. From the results of the measurement of the data obtained that for (CTQ) Critical to Quality, the key is based on the pareto diagram, that 85% of the highest defects are in the breaking of welding. From the analysis of the causal diagram, the factors that cause product defects / defects come from machine, human, raw material, and environmental factors. Thus, recommendations can be made for appropriate corrective actions, especially providing training and direction for employees on how to work according to SOPs for checking raw materials, improving the quality standards of raw materials received, and adding appropriate lighting during the production process so that defects / production defects can be minimized.

Identifikasi Pemborosan dalam Proses Produksi Pakan Ternak Ayam di PT.XYZ dengan Menggunakan Metode Value Stream Analysis Tools (VALSAT)

This research aims to identify various types of waste in the chicken feed production process at PT. XYZ and provide improvement recommendations to reduce production costs. The method used is the Value Stream Analysis Tools (VALSAT) to map and analyze the value stream in the production process. This research includes direct observation and data analysis to find the main wastes. The results show seven main types of waste in the chicken feed production process: waiting time, unnecessary movements, product defects, excess inventory, inefficient processes, overproduction, and transportation. However, the research encountered several challenges, such as data limitations and difficulties in collecting accurate data. The largest waste found was excess inventory, with an average score of 9.13. This research provides clear insights into areas in the production process that require improvement. The implementation of the improvement recommendations is expected to increase the company's efficiency and productivity. Despite facing some challenges, the use of VALSAT proved effective in identifying and analyzing waste. It is hoped that the provided improvement recommendations can reduce waste and enhance operational efficiency at PT. XYZ.

Effects of Cold Rolling–Induced Defects on Proton Irradiation Response in Nb-1Zr-0.1C Alloy

This work highlights the effect of proton irradiation in the presence of preexisting defects in the Nb-1Zr-0.1C alloy. To introduce the initial defects, the Nb-1Zr-0.1C alloy was subjected to cold rolling (10% and 20%) prior to irradiation. Characterization of the irradiated material was performed using detailed X-ray diffraction line profile analyses. The results, in terms of various microstructural parameters, revealed that the preexisting defects in these materials act as effective sinks for irradiation-induced defects during the initial irradiation. Electron backscattered diffraction analyses suggested that the fraction of low-angle grain boundaries plays a crucial role during irradiation and determines the outcome of the competing process between defect annihilation and defect production. Transmission electron microscopy analyses revealed that the black dots were coexisting with carbide precipitate in the cold-rolled Nb alloy after irradiation. Mechanical properties were also assessed by measuring microhardness to correlate with the changes in microstructure after irradiation. The change in yield strength calculated from the change in the microhardness value as a function of dose indicated that the 20% cold-rolled Nb alloy was more radiation resistant than the 10% cold-rolled Nb alloy at ambient temperature and a low-dose regime. To see the effect of alloying elements, similar studies were also carried out on pure Nb, and the results were compared with that of the Nb-1Zr-0.1C alloy. All these findings help to develop a better understanding of the behavior of the proton-irradiated Nb-1Zr-0.1C alloy in the presence of preexisting defects introduced by means of cold rolling prior to irradiation.

A novel multi-information fusion CNN for defect detection in laser soldering of SAC305

Identification of laser soldering of lead-free solder Sn-3.0Ag-0.5Cu (SAC305) in electronic packaging was still an enormous challenge. It was difficult to detect defects in large-scale production. This work proposed an identification model based on multi-information fusion convolutional neural network (MIFCNN) for inspecting laser soldering process. In this method, the forty images in chronological order and the temperature data were combined as the input to be utilized in detecting defects. The results demonstrated that MIFCNN had best accuracy for three types of joints with accuracy of 98.28 % due to the combination of images and temperature information. The ICNN and TCNN had poor recognition accuracy for the warpage defect with 73.9 % and the poor wetting defect with 66.5 %, respectively. This was because the images and temperature information were the key to identifying the poor wetting defects and warpage defects, respectively. The poor wetting defect could be recognized by difference of contact angle, while the warpage defect could be significantly detected by maximum temperature. This work could help detecting defects of laser soldering in the actual production and widen the application of MIFCNN in the field of laser soldering.

Dynamic deformation and fracture of brass: Experiments and dislocation-based model

In this work, we perform a comprehensive study of the dynamic deformation and fracture of brass, including Taylor tests with classical and profiled cylinders and ball throwing experiments reaching the strain rates of about (0.1−1)/μs, as well as atomistic and continuum-level numerical modeling. Molecular dynamics (MD) simulations are used to construct the equation of state (EOS) of brass and to study its fracture characteristics at shear deformation under negative pressure. An original model of fracture under combined tensile-shear loading is formulated, which takes into account both the accumulation of empty volume in the process of lattice loosening due to the lattice defect production in the course of plastic deformation and further mechanical growth of voids controlled by the dislocation plasticity. This atomic-scale model is transmitted to the macroscopic experiment-scale level and embedded into 3D dislocation plasticity model to describe the dynamic deformation and fracture of brass using the numerical scheme of smoothed particle hydrodynamics (SPH). A part of experimental data is used to find the optimal parameters of the dislocation plasticity model by means of the Bayesian global optimization method accelerated with the help of artificial-neural-network (ANN)-based emulator of the 3D model. Another part of experimental data is used to fit the fracture model parameter. The remaining experimental data, which are not used in the parameterization, are applied to verify the parameterized model. The developed physical-based model provides correct and meaningful description of the dynamic deformation and fracture of brass, while the developed formalized approach to its parameterization opens a way to wider use of this type of models in the engineering applications, including studies on dynamic performance and high-speed processing technologies.

FORMATION OF STRATEGIC DIRECTIONS FOR THE USE OF ARTIFICIAL INTELLIGENCE IN THE ENTERPRISE TO ACHIEVE THE GOALS OF SUSTAINABLE DEVELOPMENT

Rapid artificial intelligence (AI) development opens new opportunities for enterprises to achieve sustainable development goals. This article summarizes the research results of applying AI to optimize production processes, reduce environmental impact, increase productivity, and strengthen social responsibility. The article aims to highlight the potential of AI for achieving enterprises' sustainable development goals and identify effective strategies for its application to increase economic efficiency, social responsibility, and environmental sustainability. A qualitative analysis of statistical data, official regulatory documents, books, and articles from accredited journals was used to achieve the goal. The obtained results showed that AI could be used to analyze production processes and forecast the demand for raw materials, optimize supply chains and reduce energy consumption, identify potential sustainability risks and develop strategies to prevent them, identify defects in production and control product quality in real-time, create personalized services and products for customers. The study highlights the importance of AI's ethical and safe use to ensure its positive impact on society and the environment. The practical value of the article lies in the systematic review of studies on the effects of AI on the sustainable development of enterprises. The obtained results can be used to improve production processes, increase enterprises' competitiveness, and promote sustainable development.

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%.

Effect of nozzle clogging on vortexing during continuous casting processes: results of an experimental simulation

ABSTRACT Exogenous oxide inclusions in continuously cast steel primarily arise from the entrapment of slag during various stages of steelmaking, including in furnaces, ladles, and molds. To maintain steel cleanliness, operators often pause ladle and tundish processes before ‘slag vortexing’ begins, since this phenomenon (slag vortexing) can adversely affect product quality. Previous studies indicate that sub-entry nozzle clogging can lead to productivity issues and quality defects in cast products. Clogging can cause non-uniform reductions in nozzle diameter along the molten metal flow direction. This study examines the impact of drain port taper (a representation of nozzle clogging) on air core vortex formation during water drainage (simulating molten metal) using a parameter, ‘Characteristic Volume of Air Core.’ Results show that clogged drain ports are prone to vigorous vortexing, significantly affecting efflux velocity and static pressure at drain outlets. The current study also analyzes the dynamic behavior of air core vortexing due to nozzle clogging, revealing that clogging can induce fluctuations in liquid discharge, adversely affecting the casting process. These findings have practical implications for continuous casting processes in steel and other metals.

Causes of quality defects in cassava-based food production: an ishikawa diagram analysis

Causes of quality defects in cassava-based food production: an ishikawa diagram analysis

Free Vibration Analysis for Polyester/Graphene Nanocomposites Multilayer Functionally Graded Plates

In the current work, an FGM nanocomposite made of multi-layers of graphene nanoparticles and a polyester-based matrix was constructed using the molds with a hand lay-up technique to attain an accurate shape and reduce defects in the final product. The desired models have four, six, and 11 layers and different volume fractions of nanoparticles (0.5, 1, 2, 3, 4, and 5%). This study conducted various experimental tests to analyze the free vibration characteristics of functionally graded composite sandwich rectangular structures with simply supported boundary conditions to evaluate the significance of hole number and cutout location in fundamental frequencies. For holes, three types are used (2, 4, and 6) holes with a 10 mm diameter, while for cutouts, three geometrical designs are used (circular, rectangular, and triangular) with various aspect ratios (r = 1, 1.5, 2, and 2.5). A numerical study was carried out to validate the experimental solution, employing modal analysis and finite element analysis (FEA) using ABAQUS software tools. From the findings, the experimental findings and numerical calculations exhibit a satisfactory level of concurrence, displaying a maximum discrepancy of 9.5%. The results show that the fundamental frequency decreases by increasing the cutouts' aspect ratio (r=a/b). There is minimal variation between r =1 and r= 1.5, but a noticeable decrease is observed at an aspect ratio of r= 2.5. This difference is primarily influenced by the type of material gradient and the number of holes, specifically for a given thickness of the functionally graded (FG) plates.

Intelligent inspection and quality control of table olives

Quality is a key factor of product-marketing in the field of agriculture and food industry. A number of researches have suggested the use of vision computer systems and machine learning (ML) techniques essentially to inspect imperfection surfaces and defects in fruit products. Following the trend, this paper still relies on the use of vision computer system by assuring an intelligent detection of table olive defect, with a certainty of its quality control. The specifically exploited computer vision system consists of automatically extracting the texture, colour, and shape features mainly from the global thresholding segmented image. With reference to the extracted features, the newly introduced system has the capacities of distinguishing between the defected and the healthy olive fruits rapidly and effectively at the same time. Subsequently, it is capable of identifying and specifying the diseased table olive. This system is also helpful for estimating the table olive size automatically. The experimental findings are indicative of the high accuracy of the binary classification algorithm, reaching 99, 32%, the average processing time for just one olive is about 0.4 s, which could meet the requirements of the real-time applications, and the error in estimating the size of the table olives does not exceed 10%.

An integrated approach of zero defect manufacturing and process mining to avoid defect occurrence in production and improve sustainability

Purpose This paper aims to integrate zero defect manufacturing (ZDM) with process mining (PM) to avoid defect occurrence during production and improve sustainability. Design/methodology/approach The method is developed based on literature review in ZDM and PM. It uses PM for process discovery as an initial strategy in priority to predict-prevent strategies of ZDM. Findings It presents the applicability of the proposed approach in observing manufacturing process behavior, identifying dynamic causes of defects during production, predicting the time of defect occurrence and preventing defective products. It also identifies, explains and measures criteria for environmental, social and economic pillars of sustainability affected by defects and presents the impacts of the proposed approach on sustainability improvement. Originality/value The extended view of this research, as well as its analytical approach, helps practitioners to develop their ZDM and PM approaches more holistically. The practical application of this research is illustrated through implementing it in a real-life manufacturing case, where the outcomes prove its applicability in avoiding defect occurrence and improving all three pillars of sustainability.