Faulty Products Research Articles

Bone and antler products from the Toretskoye urban settlement of the 15th century ad

Products made of bone and antler complement the characteristics of the material culture of the Toretskoye urban settlement, a major centre of crafts and international trade in the Kazan Khanate. In accordance with the general dating of the single-layer site, all items made of bone and antler have a reliable period of existence within the 15th century AD. Based on frequent numismatic material and other related context, the products receive the status of a chronological and, in some cases, ethnocultural reference point, and in terms of inventory and manufacturing techniques they represent products of urban production. No specialized bone-carving workshops have been found at the site; however, the presence of blanks and faulty products in the coppersmith and blacksmith workshops makes it possible to conclude that bone and antler processing was a related craft of complex production.

A sustainable fuzzy economic production quantity model (SFEPQM) with sporadic machine failures, inspection and prepayments under carbon tax and cap policy

Goals for sustainability have caused an upcoming change in the emphasis of Economic Production Quantity models, which was previously limited mainly to financial considerations to include aspects of the environment as well. The primary tools used in many nations to reduce emissions are carbon cap and price legislation. However, companies in the production industry may have difficulties with their planning and oversight procedures as they frequently have to cope with faulty products and machinery failures. Previous research has not addressed the comprehensiveness of green investment in machine breakdowns. Considering this problem, we have developed a sustainable economic production quantity model with sporadic malfunctions of machinery and prepayments under carbon tax and cap regulations. Our aim is to minimise the overall cost of faulty items with the investment in green technologies. An approach that combines with the outstanding effectiveness of green strategies to minimise emissions will culminate in a greener production that generates more profit than the traditional system. This study considers two cases i.e. with and without green technology investment (GTI). It also has many predictions, including the execution of inspections afterwards as well as during manufacturing, faulty goods are scrapped while others are sold at discounted price.

ResNet-101 based anomaly detection in additive manufacturing: Thermal modeling for quality control in heat exchanger production

In the production of heat exchangers using additive manufacturing, maintaining consistent quality is a significant challenge due to irregular energy transformations during the manufacturing process. These irregularities can result in faulty products, leading to issues such as reduced efficiency and reliability of the heat exchangers. Traditional quality control methods are often inadequate for real-time detection and correction of such faults. This paper focuses on analyzing the occurrence of faulty products caused by irregular energy transformations in heat exchangers. A deep learning network, specifically ResNet-101, is employed to address this issue. The network is trained with characteristic features of the product and instantaneous variations in the heat exchanger to accurately identify faulty patterns resulting from degradation in the heat exchanger’s performance. By leveraging the deep learning network, which comprehensively understands both the product and heat exchanger features, the system can detect and classify faulty products in real time. This enables immediate corrective actions, such as pushing defective products into a recycling process, thereby maintaining high-quality standards in continuous production.

Sestrin2 drives ER-phagy in response to protein misfolding

Protein biogenesis within the endoplasmic reticulum (ER) is crucial for organismal function. Errors during protein folding necessitate the removal of faulty products. ER-associated protein degradation and ER-phagy target misfolded proteins for proteasomal and lysosomal degradation. The mechanisms initiating ER-phagy in response to ER proteostasis defects are not well understood. By studying mouse primary cells and patient samples as a model of ER storage disorders (ERSDs), we show that accumulation of faulty products within the ER triggers a response involving SESTRIN2, a nutrient sensor controlling mTORC1 signaling. SESTRIN2 induction by XBP1 inhibits mTORC1's phosphorylation of TFEB/TFE3, allowing these transcription factors toenter the nucleus and upregulate the ER-phagy receptor FAM134B along with lysosomal genes. This response promotes ER-phagy of misfolded proteins via FAM134B-Calnexin complex. Pharmacological induction of FAM134B improves clearance of misfolded proteins in ERSDs. Our study identifies the interplay between nutrient signaling and ER quality control, suggesting therapeutic strategies for ERSDs.

A novel cost-sensitive quality determination framework in hot rolling steel industry

In the hot rolling industry, a high-precision quality determination framework is the guarantee for intelligent decision-making regarding products. Conventional methods ignore the significance of faulty products, which are assigned the same attention as the qualified products. To overcome these limitations, this paper proposes a novel compensated cost-sensitive multi-layer perceptron with minority attention (CCS-MLP-MIAT) to construct a high-precision quality determination framework. We first transform an imbalanced multi-class ordinal industrial problem into multiple imbalanced binary ordinal classifications using one-versus-one (OVO) decomposition. Then, we implement a minority attention mechanism and a cost-sensitive loss for MLP. Subsequently, a novel cost compensation coefficient is introduced and determined by Artificial Hummingbird Algorithm (AHA) to further improve the identification rate of faulty products. The advantage lies in the model to allocate more attention to the faulty products, thus promoting intelligent decision-making of hot rolling production. The proposed method is validated on eight UCI benchmark datasets and two real-cases of hot-rolled strip crown by cross validation. Experimental results demonstrate that the excellent generalization and robustness of CCS-MLP-MIAT in addressing the quality determination of hot-rolled steel.

Framework for fish freshness detection and rotten fish removal in Bangladesh using mask R–CNN method with robotic arm and fisheye analysis

Fish-exporting countries must meet international standards and customer expectations to avoid trade disruption and economic repercussions. Ensuring the quality of fish products is therefore not just a matter of reputation but also of economic stability. Bangladesh, a major fish exporter, maintaining the quality of exported fish products is crucial. A single faulty product can have serious consequences, potentially causing harm. For this reason, we develop a deep learning-based approach capable of detecting and dividing rotten fish. We have used the Mask R–CNN method for our model. A device captures images of fish eyes and sends them to a computer system. The condition of a fish, whether fresh or rotten, can be determined by its eyes. A collection of 5000 image datasets is developed in this research work. The input images start matching the dataset through the Mask R–CNN and give us the result. Based on the result, if the fish is found fresh, it will proceed through the conveyor system; however, if it is identified as rotten, a robotic arm will separate it. To test its efficiency and reliability, we have tested it with 5000 images. And the test satisfied us with the results of 96.5% accuracy. With the assistance of our model, fish-exporting nations can efficiently distinguish between fresh and rotten fish, enhancing their ability to export more significant quantities of high-quality fish.

Characteristics of online shopping behaviour among Tunisians consumers

IntroductionDuring the COVID-19 pandemic confinement, the number of people shopping online has increased all over the word. To date, little is known about the online shopping behaviours of Tunisians consumers.ObjectivesEvaluate the characteristics of internet shopping among Tunisian consumers.MethodsA cross-sectional, descriptive and analytical study was conducted among subjects who had already made at least one online purchase. Data was collected using a self-questionnaire published by GOOGLE FORMS. We used a survey form collecting socio-demographic data, personal history and characteristics of online shopping behaviour.ResultsA total of 137 participants aged 34.62 ± 9.82 years took part in this study.All participants had made at least one online purchase, with 43.8% (N=60) purchasing “More than once a year”. The products purchased were most often textiles and shoes (50.4%; N=69). The main reasons consumers gave for buying online were special offers (37.2%, N=51), reduced prices (25.5%, N=35) and free delivery (14.6%, N=20). Almost half of the participants (N=63; 46%) said that they had visited physical shops less since they started shopping online. Regarding the average online shopping budget, 44.5% of consumers (N=61) spent less than 50 dinars/month and 18.2% (N=16) did not use all the products they bought online. Almost half of participants (N=68, 49.6%) feared that their credit card information would be at risk. The majority of respondents (88.9%) thought they might receive a faulty product following online shopping.ConclusionsOur study has enabled us to identify certain factors that may act as a blocker for online purchasing. So that, stablishing strategic actions for the continuous improvement of online shopping services with the reduction of subjectivity in customer perception will be helpful.Disclosure of InterestNone Declared

Blockchain-Enabled Transfer Learning for Vulnerability Detection and Mitigation in Maritime Logistics

Abstract With the increasing demand for efficient maritime logistic management, industries are striving to develop automation software. However, collecting data for analytics from diverse sources like shipping routes, weather conditions, historical incidents, and cargo specifications has become a challenging task in the distribution environment. This challenge gives rise to the possibility of faulty products and traditional testing techniques fall short of achieving optimal performance. To address this issue, we propose a novel decentralised software system based on Transfer Learning and blockchain technology named as BETL (Blockchain -Enabled Transfer Learning). Our proposed system aims to automatically detect and prevent vulnerabilities in maritime operational data by harnessing the power of transfer learning and smart contract-driven blockchain. The vulnerability detection process is automated and does not rely on manually written rules. We introduce a non-vulnerability score range map for the effective classification of operational factors. Additionally, to ensure efficient storage over the blockchain, we integrate an InterPlanetary File System (IPFS). To demonstrate the effectiveness of transfer learning and blockchain integration for secure logistic management, we conduct a testbed-based experiment. The results show that this approach can achieve high precision (98.00%), detection rate (98.98%), accuracy (97.90%), and F-score (98.98), which highlights its benefits in enhancing the safety and reliability of maritime logistics processes. Additionally, the computational time of BETL (the proposed approach) was improved by 18.9% compared to standard transfer learning.

Stalled translation by mitochondrial stress upregulates a CNOT4-ZNF598 ribosomal quality control pathway important for tissue homeostasis

Translational control exerts immediate effect on the composition, abundance, and integrity of the proteome. Ribosome-associated quality control (RQC) handles ribosomes stalled at the elongation and termination steps of translation, with ZNF598 in mammals and Hel2 in yeast serving as key sensors of translation stalling and coordinators of downstream resolution of collided ribosomes, termination of stalled translation, and removal of faulty translation products. The physiological regulation of RQC in general and ZNF598 in particular in multicellular settings is underexplored. Here we show that ZNF598 undergoes regulatory K63-linked ubiquitination in a CNOT4-dependent manner and is upregulated upon mitochondrial stresses in mammalian cells and Drosophila. ZNF598 promotes resolution of stalled ribosomes and protects against mitochondrial stress in a ubiquitination-dependent fashion. In Drosophila models of neurodegenerative diseases and patient cells, ZNF598 overexpression aborts stalled translation of mitochondrial outer membrane-associated mRNAs, removes faulty translation products causal of disease, and improves mitochondrial and tissue health. These results shed lights on the regulation of ZNF598 and its functional role in mitochondrial and tissue homeostasis.

DQ-DeepLearn: Data Quality Driven Deep Learning Approach for Enhanced Predictive Maintenance in Smart Manufacturing

In the realm of smart manufacturing, predictive maintenance plays a pivotal role in ensuring equipment reliability, minimizing downtime, optimizing costs, and reducing product failure rate by detecting faulty products in the early stage. However, the efficacy of predictive maintenance hinges on the quality of training data employed for predictive modeling. Inadequate data quality can lead to erroneous predictions and, consequently, ineffective maintenance strategies. This research paper introduces a solution that leverages deep learning and data quality management to enhance predictive maintenance in smart manufacturing. Our proposed methodology encompasses two major components such as data quality management and faulty product detection. Data quality management includes data preprocessing, feature reduction, and data balancing, whereas faulty product detection is done using deep learning techniques. By combining these elements, a predictive model capable of accurately forecasting faulty products in early stages to reduce economic losses is developed. The proposed approach effectively addresses data quality issues and is tested on the SECOM dataset which indicates that it surpasses traditional models with an accuracy of 96.4% and perfect recall. Ultimately, this research contributes to the advancement of predictive maintenance and deep learning in the context of smart manufacturing, benefiting both industrial practitioners and researchers alike.

Manufacturing Line Ablation, an approach to perform reliable early prediction

Manufacturing and assembly lines have become more and more complex over the years. One reason for this complexity is multistage manufacturing, where faults in one stage may carry forward downstream in the production line. Our goal is to find the production stage where reliable fault prediction will already be possible. Our approach takes an holistic view over the complete manufacturing line and performs an ablation study on product-specific data. The outcome of our approach is the analysis of a given manufacturing line that results in locating manufacturing stages where reliable prediction of faulty products is possible. Our objective is to support the implementation of an operative decision support system that allows to decide when to perform early prediction in a manufacturing process.

Detection of Broken Almonds with Image Processing Techniques

Almond is a product with high nutritional and economic value; some quality control procedures are applied before the almonds are packaged. Applications such as product classification, faulty product detection, and non-product substance detection are widely used in quality control processes. With the development of image processing and camera control systems, these operations can be performed with computer vision systems at low costs, high speed and accuracy. In this study, to detect broken almonds; image processing techniques such as thresholding, HSV transformation and blob detection were actively used. Binary image followed by blob detection algorithm was used to detect almonds. HSV transformation was performed on the detected almonds to detect the broken area. It is aimed to detect broken areas by applying appropriate threshold values to H and S bands. When the proposed method was tested, an algorithm was developed that worked 100% as long as the broken areas were visible.

Detection of Missing Component in PCB Using YOLO

The detection of missing components in printed circuit boards (PCBs) is a critical task in the electronics manufacturing industry. The current practice of manual inspection is time-consuming and prone to human error, which can result in faulty products and increased costs. In this paper, we propose a solution that uses the YOLO (You Only Look Once) object detection algorithm to automatically detect missing electronic components in PCBs. Electronic components detection model is trained using YOLOv3 architecture. Dataset is prepared using high quality printed circuit board images and manual labeling in Label Studio. The model is trained on a dataset of 16 different electronic components commonly found in PCBs including Electrolytic Capacitor, QFP, Toroidal core Inductor, Crystal Oscillator etc. Prepared model recognizes these electronic components with an average map score of 65.8%with IoU 50% and 42.6% with IoU 95%. The results show that the proposed solution can detect the missing components.

Research on the Evaluation of the Efficiency of Production Processes Through the Implementation of Key Performance Indicators

Abstract The paper deals with the implementation of key performance indicators (KPI) for the evaluation of the efficiency of production processes. A key performance indicator is a measurable indicator that expresses how effectively the company achieves key goals in both production and non-production areas. As part of the research, various process key performance indicators characterizing product quality were identified. The indicator of the number of faulty products in relation to the volume of production (EXTppm) was chosen for the research. The monthly development of EXTppm in the course of 2022 and the long-term development in the years 2012-2022 were evaluated. The research was also devoted to the analysis of the reasons for not achieving the KPI target values, and measures were proposed to eliminate poor quality, respectively reduction of the number of complained products.

Investigation of Defects Occurring in the Casting Shop Process of Ceramic Sanitaryware on the Final Product

When the export values of ceramic sanitaryware in Türkiye are analyzed, it is seen that the data for the year 2022 of ~660 million dollars continues to increase. In the sector, which reaches an annual average increase of 100 million dollars, the amount of production as well as the design demands change and shape the market. The most important difficulty factors in design are due to the fact that the products in the sector have large sizes, complex shapes and the most human-made production stages. When these factors are compared with other ceramic sectors, it is revealed that although industrialization has increased in the production of ceramic sanitaryware, the lack of production amount and the excess of faults are among the biggest problems. Glaze faults are the leading faults observed in the final product, and since this problem is not recyclable, it harms the ceramic sanitaryware industry in terms of cost. In this study, the glaze faults reflected in the final glazed products originating from the casting shop stage, which attracts attention in this field due to its large size and whiteness and there is not much data for the vitreous ware industry in the literature, have been studied. In the study, which was carried out with the aim of detecting and eliminating some faults in industrial production and creating data for the literature, faults arising from mechanical evenings used in classical and pressure casting benches and material residues used in retouching processes were determined. These faults were produced on the basis of the laboratory and the characterization of the glazed faulty products was made. It is concluded that production losses can be reduced by eliminating these problems, consequently increasing quality and productivity.

DETECTION AND CHARACTERIZATION OF SOME GLAZE FAULTS ENCOUNTERED IN SANITARYWARES

The use of suitable glaze compositions, which enable ceramic sanitaryware (such as sink, toilet bowl, toilet bowl, etc.) to have better hygienic, aesthetic and technical properties, is of great importance as in other ceramic products. Glaze compositions, which provide durability to sanitaryware materials, reduce impact resistance and give ceramic material a hygienic appearance, are produced as ~97% white color depending on the preferences of the consumers, therefore, surface defects attract more attention in glazing and post-glazing process applications. When faults occurred during the different stages of manufacture detected on the final product, failure to detect faults on the final product by passing many production stages poses a problem in terms of intervention in the process. Faults that occur during glaze preparation are important in terms of cost. Since it is a stage in which many economic evaluations have been made in production, eliminating the faults caused from glaze preparation prevents further financial loss. In this study, the appearance of the faults in the sanitaryware items and the change in their regions (occured as a result of the oils contaminations from the machinery and components used in the preparation of glaze, the pipes of the tanks used in the glaze transport tanks and the glaze transfer or the impurities contaminated from the environment, the splashing of the impurities by the colored glazes in the glazing cabinets etc.) were characterized by means of SEM (Scanning Electron Microscopy), XRF (X-ray fluorescence spectrometry), XRD (X-Ray Diffractometry) and color measurement (L a* b*) analyses. According to the data derived from the final defected products, the faulty products were imitated by using the same components and methods on the plates under the laboratory conditions. Finally, the solutions for the problems were determined on the basis of faulty products obtained in the laboratory.

A death due to faulty airbag system-case report.

Injuries due to automobile safety devices have been reported and we report a rare fatality due to a defective airbag system. A compact hybrid car driven by a 37-year-old male at moderate speed had crashed through an unprotected manhole. The car had moved around 200m before it came to a halt. The passers-by had noticed that the driver was unconscious, partly leaning forward with an inflated airbag. He was pronounced dead on admission. The car had sustained minor indentation on front bumper. Autopsy revealed a circular perforated laceration on the neck associated with completely transected right carotid artery and partially transected jugular vein. There was a 2 × 2-cm cylindrical metal object lodged at C4-C5 vertebrae level, and the remaining part of this was retrieved during scene investigation and identified as a part of a metal canister in the inflator component of the airbag system. There was a perforation in the airbag which was similar in size to the retrieved foreign body. The cause of death was ascertained as hemorrhagic shock due to bleeding from ruptured neck vessels caused by primary shrapnel projected from the faulty airbag system. Following the incident, the manufacturers replaced faulty airbag systems in similar models. If abnormally fatal injuries are observed, a scene visit along with automotive expert opinion would provide valuable insight into the incident. Such information would alert the general public on regular screening of safety measures and prompt manufacturers to recall faulty products.

Developing Quantitative Methods for Quality and Maintenance Management in Micro-Electromechanical Systems

In this research, we create and refine a computational model using Matlab software, focusing on the degradation characteristics of light-emitting devices. This work acknowledges the critical importance of burn-in procedures, quality control measures, and preventive maintenance policies in any organization. The burn-in phase serves as a fundamental step in recognizing faulty products by subjecting them to a predetermined testing period. Post the burn-in phase, a quality control system is employed, leading to the exclusion of sub-standard products. Concurrently, we establish a preventive maintenance strategy that aims at enhancing the product's performance over its lifespan. For the identification of the best choices concerning burn-in, quality control, and preventive maintenance, a cost-efficiency optimization model is formulated. The developed model is then used to assess the value of these policies, comparing original and optimal measurements using an optimization algorithm, all demonstrated through a practical case study.

A Data Mining Framework for Maintenance Prediction of Faulty Products Under Warranty

Predicting the sequential patterns of maintenance activities (replacement or repair) with the needed spare parts for faulty products becomes the main challenge to maintenance engineers. This research, therefore, develops a data mining framework for predicting the integrated sequential patterns of maintenance activities and identifying the classification of the frequent components’ spare parts for faulty products under warranty. In this framework, a large data set was mined for products under warranty including product attributes, maintenance activities, and spare parts. Then, data mining techniques were performed to determine the frequent sequence pattern of maintenance activities, involving: the selection of monthly maintenance activities, generalized sequential patterns (GSP), generation of association rules, and rule-based classification with/without considering product attributes. The frequent sequential patterns were validated using testing data. Further, the GSP was applied to determine the frequent sequential patterns of spare parts. Finally, integrated sequential patterns were generated for maintenance activities and spare parts. A case study of water-cooled chiller products was deployed to illustrate the developed framework. The effectiveness of the framework was illustrated with the warranty data mining for a water-cooled chiller. In conclusion, the proposed framework allows maintenance engineers to extract hidden knowledge regarding sequential patterns of maintenance planning and provides valuable information for maintenance prediction.

Inventory model involving reworking of faulty products with three carbon policies under neutrosophic environment

The increasing in industry as well as usage of vehicles for transporting products generate many problems to the environment such as greenhouse gas emission and global warming. As nowadays environmental pollution has become a crucial problem, production companies have now shifted their attention to lower the carbon emission level along with their financial goal. In reality, the values of some parameters such as production cost, holding cost, demand rate, deterioration rate may not fixed due to several factors. Reworking of defective products reduces production cost. This study aims at developing a multi-objective supply chain inventory management by considering deteriorating products together with imperfect quality production under neutrosophic environment. Several carbon reduction policies such as carbon tax, carbon cap-and-trade and carbon cap-and-offset and green technology are implemented here to mitigate the influence of carbon emission by curbing amount of carbon emission. To reduce the deterioration rate, preservation technology is utilized. In real-world situations, all parameters related to an inventory management are not handled as a crisp value, a fuzzy number or an intuitionistic fuzzy number. To confrontation this issue, single valued trapezoidal neutrosophic number is chosen for investigating this type of an inventory problem. A ranking approach is designed to transform all uncertain parameters into deterministic values. A comparison between two types of reworking processes, and a comparison among crisp, intuitionistic fuzzy and neutrosophic models are drawn. A sensitivity analysis regarding important parameters is also taken into consideration to accentual managerial intuitions.