Defect Production Research Articles

TEHNOLOGIA BLOCKCHAIN PENTRU ÎMBUNĂTĂȚIREA TRASABILITĂȚII ȘI SUSTENABILITĂȚII ECHIPAMENTELOR INDIVIDUALE DE PROTECȚIE ÎN AGRICULTURA DIN ROMÂNIA

Blockchain technology provides a secure and transparent method to improve the traceability and sustainability of personal protective equipment (PPE) in Romanian agriculture. By enabling comprehensive monitoring of PPE across its lifecycle, blockchain addresses gaps in safety and compliance. It ensures adherence to safety standards while reducing risks of counterfeit or defective products. However, several challenges hinder adoption. High costs, limited digital infrastructure, and low digital literacy among farmers create significant barriers. Overcoming these requires targeted investments in rural digital infrastructure and training programs designed to enhance stakeholders’ skills. Public-private partnerships and pilot projects are essential to demonstrate the technology’s benefits and build trust among users. Gradual implementation, supported by collaboration between public and private sectors, can transform Romanian agriculture. Blockchain’s integration can lead to better productivity, improved safety, and greater sustainability. This study examines blockchain’s potential in PPE management, offering insights into its applications, benefits, and necessary steps for successful adoption in the agricultural sector. Through strategic action, Romanian agriculture can leverage blockchain to achieve safer practices and long-term sustainability.

Analisis Yuridis tentang Perlindungan Konsumen pada E-Commerce di Indonesia: Pendekatan Yuridis-Normatif

This study focuses on the juridical analysis of consumer protection in e-commerce transactions in Indonesia, considering the sector's rapid growth, which reached a total transaction value of IDR 631 trillion in 2023. On the other hand, consumer complaints have continued to rise, involving issues such as fraud, defective products, and data privacy violations. This study aims to evaluate the effectiveness of existing regulations, such as Law No. 8 of 1999 and its supporting regulations, in protecting consumer rights. Using a normative-juridical approach, the study examines the legal substance, implementation, and gaps between national regulations and international standards like the European Union's General Data Protection Regulation (GDPR). The findings indicate that although national regulations provide basic protections, their effectiveness is considered low, particularly in the aspect of personal data protection, which only achieves 40% effectiveness, significantly lagging the GDPR at 95%. Additionally, inefficient complaint mechanisms and weak coordination among government agencies are major obstacles to resolving consumer disputes. Low digital literacy among consumers further exacerbates the ineffectiveness of the protection provided by existing regulations. The study concludes that national regulations require significant reform to be more adaptive to digital challenges. Policy recommendations include adopting GDPR principles, strengthening government oversight, and enhancing consumer education. This study's contribution is expected to strengthen a safe, transparent, and equitable e-commerce ecosystem in Indonesia while boosting consumer trust in online transactions.

Complement C3d enables cell-mediated immunity capable of distinguishing spontaneously transformed from nontransformed cells

Immune surveillance depends in part on the recognition of peptide variants by T cell antigen receptors. Given that both normal B cells and malignant B cells accumulate mutations we chose a murine model of multiple myeloma to test conditions to induce cell-mediated immunity targeting malignant plasma cell (PC) clones but sparing of normal PCs. Revealing a previously unknown function for intracellular C3d, we found that C3d engaged T cell responses against malignant PC in the bone marrow of mice that had developed multiple myeloma spontaneously. Our results show that C3d internalized by cells augments immune surveillance by several mechanisms. In one, C3d induces a master transcription regulator, E2f1, to increase the expression of long noncoding (lnc) RNAs, to generate peptides for MHC-I presentation, and increase MHC-I expression. In another, C3d increases expression of RNAs encoding ribosomal proteins linked to processing of defective ribosomal products that arise from noncanonical translation and known to promote immunosurveillance. Cancer cells are uniquely susceptible to increased expression and presentation of mutant peptides given the extent of protein misfolding and accumulation of somatic mutations. Accordingly, although C3d can be internalized by any cell, C3d preferentially targets malignant clones by evoking specific T cell-mediated immunity and sparing most nontransformed polyclonal B cells and PC with lower mutation loads. Malignant PC deletion was blocked by cyclosporin or by CD8 depletion confirming that endogenous T cells mediated malignant clone clearance. Besides the potential for therapeutic application our results highlight how intracellular C3d modifies cellular metabolism to augment immune surveillance.

Monitoring of Directed Energy Deposition Laser Beam of Nickel-Based Superalloy via High-Speed Mid-Wave Infrared Coaxial Camera

Directed Energy Deposition Laser Beam (DED-LB) is a promising additive manufacturing technique that uses a laser source and a powder stream to build or repair metal components. Repair applications offer significant economic and environmental benefits but are more challenging to develop, especially for components that are difficult to process due to their intricate geometries and materials. Process conditions can change precipitously, and it is essential to implement monitoring systems that ensure high process stability and, consequently, superior end-product quality. In the present work, a mid-wave infrared coaxial camera was used to monitor the melt pool geometry. To simulate the challenging repair process conditions of the DED-LB process, experimental tests were carried out on substrates with different thicknesses. The stability of the deposition process on nickel-based superalloys was analyzed by means of MATLAB algorithms. Thus, the effect of open-loop and closed-loop monitoring with back control on laser power on the process conditions was assessed and quantified. Metallographic analysis of the produced samples was carried out to validate the analyses performed by the monitoring system. The occurrence of production defects (lack of fusion and porosity) related to parameters not directly controllable by monitoring systems, such as penetration depth and dilution, was determined.

Analisis biaya kualitas untuk mengurangi risiko produk cacat kantong persembahan pada Toko Buku Pustaka Mulia Manado

Quality cost refers to the expenditure incurred both to prevent quality shortages and to handle the results from low-quality production to defective products. This study aims to determine the management of quality costs to reduce the risk of defective products in the Toko Buku Pustaka Mulia Manado. The research method used in this study is qualitative descriptive. The results show that quality costs in 2022 will increase in 2023. The management of quality costs for offering bag products at the Toko Buku Pustaka Mulia Manado has succeeded in reducing the number of defective products before they reach consumers. However, the cost of controlling quality at the Toko Buku Pustaka Mulia Manado is not efficient because it is not in accordance with the quality cost standards based on applicable theories and principles.

Lung diseases in adult patients with primary immunodeficiencies with defects of antibody production according to real clinical practice

Introduction. The problem of lung diseases in patients with primary defects in antibody production has not been sufficiently studied, especially depending on the climatic, geographical and demographic conditions of real clinical practice.Aim. To study the structure of lung disorders in adult patients with primary antibodies defects in the Middle Urals in real clinical practice.Materials and methods. Register of adult patients with primary immunodeficiencies (PID) were created in 2013 in the Sverdlovsk region. Now it contains 209 people. The main group of the register is patients with primary antibodies defects (PAD, n = 143, 68.4%: agammaglobulinemia (AGG, n = 11, common variable immune deficiency (CVID, n = 37), PIK3-Kinase deficiency (n = 3), Selective IgA deficiency (SD IgA, n = 92). The diagnosis of PID was established on the criteria for the Russian Association of Allergists and Clinical Immunologists and European Societies of Immunodeficencies, in some cases it has a genetic confirmation. We used medical history of patients, radiological, functional studies to establish lung lesions. We occurred immunological examination for all PID patients.Results. Repeated pneumonia were observed in all patients with AGG and CVID, especially in the onset of PID. Patients also had diseases such as bronchiectasis (up to 37.6% of patients), chronic obstructive lung disease (up to 70.3% of patients), bronchial asthma (only SD IgA), interstitial lung disease (only CVID).Conclusion. According to our data, in the Middle Urals, lung diseases, especially pneumonia and bronchiectasis, are the most common clinical manifestations in patients with DA. Analysis of immunoglobulins' level is necessary in patients with repeated pneumonia, bronchiectasis and interstitial lung disease, bronchial asthma and early onset of chronic obstructive lung disease without smoking status.

Effect of Alumina Proportion on the Microstructure and Technical and Mechanical Characteristics of Zirconia-Based Porous Ceramics

The current study investigates the process of preparing and analysing porous-structured ceramics made from zirconium, aluminium, and magnesium ceramic oxides. The starch consolidation casting (SCC) technique, with different types of starches (potato and tapioca), was used for this purpose. Our objective was to methodically examine the impact of different processing factors, such as the temperature at which pre-sintering and sintering occur, and the proportions of ceramic powders, on the microstructure, mechanical characteristics, and porosity of the resultant composites. Pre-sintering effectively reduced the rate of shrinkage during the final sintering stage; this resulted in more controlled and predictable shrinkage, leading to better dimensional stability and reduced risk of defects in the final product. A higher alumina content was associated with an increase in apparent porosity and a reduction in volume shrinkage and apparent densities. The mercury intrusion porosimetry (MIP) findings concluded that the prepared porous ceramics have a multi-modal pore structure. The highest calculated compressive strength was 76.89 MPa for a sample with a porous structure, which was manufactured using 20 wt.% tapioca starch and 30 wt.% alumina content. The main advantage of alumina is its ability to improve compressive strength by refining the grain structure and serving as a barrier against fracture development.

Reliability-Based Design Optimization of Bearing Hub Preform for Minimizing Defects Considering Manufacturing Tolerance in Hot Forging Process

A study on the optimal design of preforms has previously been actively conducted as a method to solve defects such as voids and flash in forged products. However, previous research has generally been performed through deterministic optimization for ideal cases that do not take manufacturing tolerances into account. As a result, the application of such optimal designs in actual processes may be limited due to various factors such as material manufacturing tolerances and the machining environment of preforms. Therefore, this study conducted reliability-based optimization considering tolerances in billets and preforms. The objective of the study was to optimize the design of a bearing hub and minimize defects in the final product. When comparing deterministic optimization and reliability-based optimization, the former showed relatively superior results in terms of defect indicators but had a higher occurrence of voids and lower forming loads, increasing the probability of void occurrence. On the other hand, the reliability-based optimization showed relatively lower performance in quality improvement indicators, but it successfully met the target reliability of 99% by reducing the probability of defect occurrence. These results were derived using an approximate model based on the Kriging method, providing an optimal design that is practical and effective in actual manufacturing processes.

DEFECT CHARACTERIZATION OF METALLIC MATERIALS USING SELF-PARAMETERIZED DENSITY-BASED CLUSTERING AND COMPUTATIONAL INTELLIGENCE TECHNIQUES

Rapid growth of manufacturing industries is propelled by transformative technologies such as machine intelligence, autonomous computing and non-destructive testing (NDT). During the manufacturing of wrought products, there is no guarantee that the final product is 100-% flawless. Thus, all final products are subjected to quality checking to identify and eliminate defective products. In industries, most of internal defects are identified using NDT techniques, which fail to precisely characterize the defects. In this paper a novel algorithm, called Self-Parameterized Density-Based Clustering (SPDBC), is proposed for defect characterization. The proposed clustering method uses spatial parameters to identify the size and position of defects by filtering out the noise and other data that correspond to the non-defect area. Using these filtered data, computational intelligence techniques are employed to predict the defect type. SPDBC achieved Jaccard indices of 97.02 % and 98.78 % for identifying the defect size and position, respectively. Gradient boosting regression trees (GBRT) achieved a maximum accuracy of 97.44 % in predicting the defect type. As a result, the proposed approach can assist NDT experts in various sectors to differentiate between problem severities faster and replace defective parts before any major breakdown occurs.

A systematic study of disclination-induced internal stress associated with multiple types of twin-twin reactions in Mg alloys: analytical solutions and numerical calculations

Deformation twinning plays an important role in enhancing the mechanical properties of metals and alloys by mechanisms such as twinning-induced plasticity and strain hardening. However, internal stress concentration associated with the interactions and reactions of twins may also lead to the degradation of ductility and fracture toughness. While the elastic behavior of individual twin types has been extensively studied, the crystallographic reactions among multiple types of twins have not been well recognized, which could produce high-index or irrational twins as well as byproduct disclinations. A theoretical framework to predict twin-twin reaction products and quantify associated internal stress is still unavailable. Here we suggest a general approach to investigate twin-twin reactions through symmetry-dictated characteristic defect analyses and stress field calculations based on disclination theory. By using Mg as an example, a systematic study of multiple types of twin-twin reactions has been conducted. The product defects arising from the reactions and associated internal stress fields have been determined analytically, which are validated by numerical simulations and experimental measurements. This work not only establishes a theoretical foundation for quantifying defect-induced internal stress concentration from complicated twin-twin interactions/reactions, but also offers new insights into the impact of deformation twins on mechanical properties.

Transcriptomic and genetic analysis reveals a Zn2Cys6 transcription factor specifically required for conidiation in submerged cultures of Thermothelomyces thermophilus.

Filamentous fungi are important producers of enzymes and secondary metabolites. The industrial thermophilic species, Thermothelomyces thermophilus, is closely related to the model fungus, Neurospora crassa. A critical aspect of the filamentous fungal life cycle is the production of asexual spores (conidia), which are regulated by various stimuli, including nutrient availability. Several species of fungi, including T. thermophilus, produce conidia under submerged fermentation conditions, which can be detrimental to product yields. In this study, transcriptional profiling of T. thermophilus was used to map changes during asexual development in submerged cultures, which revealed commonalities of regulation between T. thermophilus and N. crassa. We further identified a transcription factor, res1, whose deletion resulted in a complete loss of conidia production under fermentation conditions, but which did not affect conidiation on plates. Under fermentation conditions, the ∆res1 deletion strain showed increased biomass production relative to the wild-type strain, indicating that the manipulation of res1 in T. thermophilus has the potential to increase productivity in industrial settings. Overexpression of res1 caused a severe growth defect and early conidia production on both plates and in submerged cultures, indicating res1 overexpression can bypass regulatory aspects associated with conidiation on plates. Using chromatin-immunoprecipitation sequencing, we identified 35 target genes of Res1, including known conidiation regulators identified in N. crassa, revealing common and divergent aspects of asexual reproduction in these two species.IMPORTANCEFilamentous fungi, such as Thermothelomyces thermophilus, are important industrial species and have been harnessed in the Biotechnology industry for the production of industrially relevant chemicals and proteins. However, under fermentation conditions, some filamentous fungi will undergo a switch from mycelial growth to asexual development. In this study, we use transcriptional profiling of asexual development in T. thermophilus and identify a transcription factor that specifically regulates the developmental switch to the production of unwanted asexual propagules under fermentation conditions, thus altering secreted protein production. Mutations in this transcription factor Res1 result in the loss of asexual development in submerged cultures but do not affect asexual sporulation when exposed to air. The identification of stage-specific developmental regulation of asexual spore production and comparative analyses of conidiation in filamentous ascomycete species have the potential to further manipulate these species for industrial advantage.

Implementasi Pengendalian Kualitas pada Produk Processed Food

Product quality is essential because it relates to consumer reputation and loyalty. Quality control needs to be carried out to maintain product quality. This study used descriptive research to analyze the implementation of quality control in processed food products, especially banana chips at PT DK. Based on qualitative research conducted through interviews and observations, it was found that PT DK uses 3 (three) approaches or methods for quality control, namely the implementation of 5S, quality control circle (QCC), and standard operating procedures (SOP). Implementing 5S for efficiency is also a testament to the company's commitment to improving employee performance. Meanwhile, QCC is employed to regulate the quality of raw materials. SOP, on the other hand, plays a crucial role in maintaining production quality. The company uses a standard tool to ensure that the production process adheres to the company standards. By implementing SOP, the company can minimize the production of defective products, guaranteeing product quality.

Analisis Peramalan Persediaan Sparepart Menggunakan Metode Exponential Smoothing di PT. XYZ

XYZ is a manufacturing company that produces Yamaha brand motorbike parts such as electrical switches, electrical sockets, lever assembly and horns. Many of the products sent were returned by customers because they did not meet quality standards, for example in March 2024 there were 1,420 pcs out of a total of 19,900 pcs that were returned. This means that companies have to increase production time and costs to replace defective products that are returned because they do not have safety stock. Forecasting is needed to control the production system so that it does not experience over stock and safety stock shortages. This research aims to provide recommendations for forecasting methods that companies can use to minimize the occurrence of production excesses and shortages in the company. Forecasting is done based on historical company sales data for 12 months. The method used is the exponential smoothing method which is then tested whether this method can be used in the future. This calculation uses the help of POM-QM (Production Operation Management – Quantitative Method) software. Calculations are carried out by testing the MAD, MSE, and MAPE values to obtain calculation error values. The results obtained by forecasting the main sw srtg lock assembly product using the exponential smoothing method were 15,708 for demand for the next period, MAD 3.38, MSE 22.84 and MAPE value 26%. Based on these results, the exponential smoothing method can be a recommendation for companies to forecast future demand. This is because the value of forecasting accuracy or MAPE is reasonable. The smallest percentage of MAPE values has a fairly minimal possibility of error in forecasting results.

Molecular dynamics assessment of primary damage efficiency for fusion relevant elements

Accurate computational modeling of fusion reactors depends upon precise figures of merit for radiation damage. The Norgett-Robinson-Torrens displacements per atom (NRT-dpa) model, a gold standard parameter for characterizing primary damage in solids, has been shown to overestimate surviving Frenkel defects in materials under high-energy irradiation. Here, we investigate the NRT-dpa model at higher primary knock-on atom (PKA) energies for six pure metals of interest in fusion. We compute updated defect production efficiency values and athermal recombination corrected (ARC)-dpa efficiency parameters for each element, which can be used for increased accuracy in neutronics simulations and fitting other extensions to the NRT-dpa.

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.