Quality Control Tasks Research Articles

Shelf-life of uranium oxide microparticle reference materials and possible implications for the identification of optimal storage conditions

During their inspections of nuclear facilities, inspectors of the International Atomic Energy Agency (IAEA) collect environmental swipe samples containing uranium dust particles. As reference materials, well-characterized uranium microparticles are required that are suitable for quality control, analytical refinement and method development tasks. At Forschungszentrum Jülich (FZJ), uranium oxide reference microparticles are produced using an aerosol-based process. A core requirement for distribution of a potential reference material is to guarantee a practical shelf-life. Previous studies of structure and shape of uranium oxide microparticles demonstrated possible alteration leading to the formation of uranium hydroxides such as schoepite. A systematic shelf-life study exploring storage under different environmental conditions was launched in late 2021. Uranium microparticles were stored in three environments simulating potential long-term storage conditions, whereas a fourth experiment used an unrealistically harsh environment to assess the effects of accelerated alteration. At the time of writing, only the uranium microparticles intentionally stored under these extreme conditions have shown significant signs of alteration. This implies that synthetic uranium oxide microparticles under common storage conditions have minimal shelf-lives of at least 1 year, and likely much longer.Graphical abstract

Vision System in Product Quality Control Systems

The development of technology for manufacturing products and machines carrying out technological operations is closely linked to developing systems for tracking and controlling product and production process parameters. This paper shows how the development of quality control and production management systems such as TQM and MES is related to the development of imaging and image analysis methods used in industry. The development of imaging methods is discussed in the context of developing product quality control capabilities. It is also shown as to what extent image analysis can be used to observe manufacturing parameters and process management capabilities. It was noted that the use of vision systems as an industrial measurement-quality control system would still increase. Due to the increase in imaging resolution, there was an increase in the imaging frequency, growth in the spectral range of imaging, and the dynamic development of three-dimensional and hybrid imaging methods. Based on experience from industrial applications and tasks described in scientific publications, areas where vision systems will play a key role in inspection tasks have been identified. This is the introductory article for the Special Issue “Image Analysis for Product Quality Control” on using vision systems in various industries to execute production quality control tasks.

Quantifying misdiagnosis rates from cross-calibration biases and precision errors in dual-energy X-ray absorptiometry of the femoral neck.

Dual-energy X-ray absorptiometry (DXA) is an exam that measures areal bone mineral density (aBMD) and is regularly used to diagnose and monitor osteoporosis. Except for exam quality issues such as operator error, the quantitative results of an exam are not modified by a radiologist or other physician. DXA cross-calibration errors can shift diagnoses, conceivably leading to alternate intervention decisions and patient outcomes. After identifying and correcting a cross-calibration bias of 3.8% in our two DXA scanners' aBMD measurements, we investigated misdiagnosis rates for given cross-calibration errors in a single patient cohort to determine the impact on patient care and the value of cross-calibration quality control. The studied cohort was 8012 patients of all ages and sexes with femoral neck exams that were scanned on a single DXA unit from October 1, 2018 to March 31, 2021. There were six subcohorts delineated by age and sex, three female groups and three male groups. Data reporting focused on the highest risk subcohort of 2840 females aged 65 or older. The DXA unit had no calibration changes during that time. Only one femoral neck-left or right-was randomly chosen for analysis. Patients with multiple qualifying exams within the time interval had one exam randomly chosen. The proof-of-principle simulation shifted the aBMD values within a range of ±10%, ±8%, ±6%, ±4%, ±3.5%, ±3%, ±2.5%, ±2%, ±1.5%, ±1%, ±0.5%, and 0 (no shift); the cross-calibration shifts were informed by published results and institutional experience. Measurement precision was modeled by randomly sampling a Gaussian distribution characterized by the worst acceptable least significant change (LSC) of 6.9%, with 100000 samplings for each patient. T-scores were recalculated from the shifted aBMD values, followed by reassigned diagnoses from the World Health Organization's T-score-based scheme. The unshifted original subcohort of women aged 65 and older had 599 normal diagnoses (21.1% of the cohort), 1784 osteopenia diagnoses (62.8%), and 455 osteoporosis diagnoses (16.1%). Osteoporosis diagnosis rates were highly sensitive to aBMD shifts. At the extrema, a -10% aBMD shift led to +161% osteoporosis cases, and a +10% aBMD shift led to -64.5% osteoporosis cases. Within the more plausible ±4% aBMD error range, the osteoporosis diagnosis rate changed -10.5% per +1% aBMD shift as indicated by linear regression (R2 =0.98). Except for the men aged 49 years and younger subcohort, the total cohort and five subcohorts had fit line slopes ranging between -9.7% and -12.1% with R2 ≥ 0.98. Cross-calibration bias had greater influence for diagnosis count rates compared to measurement precision, that is, LSC. These results quantify the degree of misdiagnosis that can occur in a clinically relevant cohort due to cross-calibration bias. In medical practices where patients may be scanned on more than one DXA unit, ensuring cross-calibration quality is a critical and high-value quality control task with direct impact on patient diagnosis and treatment course. The clinical impact and incidence of poor DXA quality control practices, and cross-calibration in particular, should be studied further.

Deep learning based hierarchical classifier for weapon stock aesthetic quality control assessment

In the last years, multiple quality control tasks consist in classifying some items based on their aesthetic characteristics (aesthetic quality control, AQC), where usually the aspect of the material is not measurable and is based on expert observation. Given the increasing amount of images in this domain, deep learning (DL) models can be used to extract and classify the most discriminative patterns. Frequently, when trying to evaluate the quality of a manufactured product, the categories are naturally ordered, resulting in an ordinal classification problem. However, the ordinal categories assigned by an expert can be arranged in different levels that somehow model a hierarchy of the AQC task. In this work, we propose a DL approach to improve the classification performance in problems where categories are naturally ordered and follow a hierarchical structure. The proposed approach is evaluated on a real-world dataset that defines an AQC task and compared with other state-of-the-art DL methods. The experimental results show that our hierarchical approach outperforms the state-of-the-art ones.

Identification of wood species and duration of exposure in weathered wood using guided wave propagation

ABSTRACT Nondestructive health monitoring of weathered timber structures in outdoor applications is a critical safety and quality control task. This study proposes a guided Lamb wave propagation approach for identifying the type of wood and estimating the duration of exposure in different wood species under artificial weathering. Accordingly, alder, fir, oak, and poplar wood specimens underwent accelerated weathering conditions. The intact and degraded wood specimens were assessed via the Lamb wave propagation experiments, and different sensory features were extracted and used to train machine learning models. The decision tree and random forest classification were performed to identify the type of wood and the duration of exposure to weathering. The results of the decision tree classification indicated that the extracted wave-based features could identify the type of wood species with an accuracy of 82.5%. The wave features could also classify the duration of exposure to weathering with a maximum accuracy of 75%. Higher classification performance was obtained using the random forest model where the wood species and exposure duration were classified with an accuracy of 85% and 81.25%, respectively. Overall, the guided Lamb wave propagation exhibited the potential for in-situ assessment and species identification of weathered wood in outdoor conditions.

Development of quality improvement procedures and tools for facility management BIM

Despite the potentially significant benefits that Building Information Modeling (BIM) can offer during a facility's operation and maintenance (O&M) phase, the construction industry has thus far mainly implemented BIM in the design and construction phases. This is because even though as-built BIM models are delivered at the handover stage, owners and operators rarely have the expertise to efficiently use and update them. Additionally, industry standards do not provide precise guidelines on aspects such as the ease of use, interoperability, and maintainability of FM-BIM, that could ensure their efficient and effective utilization. Moreover, given that these models are mainly developed for the design and construction phases, they usually contain design and construction details that are not useful for the building's operation and maintenance or lack information required for this phase. Thus, this paper investigates correspondences between as-built models and O&M requirements, using procedures and semi-automated tools to facilitate quality management activities for FM-BIM. To achieve this, a detailed checklist of items that are required in the BIM models at the handover stage and of the items that can be purged was created. This checklist is part of an overall quality framework that includes quality assurance and quality control tasks to deliver useable models for the operation and maintenance phase. Additionally, a procedure and a set of tools were investigated to semi-automatically apply a collection of the items of the checklist on as-built models. A process flow is presented to assist in quality management activities during the development of the models and to prepare them for handover. Finally, two case studies were conducted to verify and validate the applicability of the developed tools and proposed procedures.

Comprehensive generation, visualization, and reporting of quality control metrics for single-cell RNA sequencing data

Single-cell RNA sequencing (scRNA-seq) can be used to gain insights into cellular heterogeneity within complex tissues. However, various technical artifacts can be present in scRNA-seq data and should be assessed before performing downstream analyses. While several tools have been developed to perform individual quality control (QC) tasks, they are scattered in different packages across several programming environments. Here, to streamline the process of generating and visualizing QC metrics for scRNA-seq data, we built the SCTK-QC pipeline within the singleCellTK R package. The SCTK-QC workflow can import data from several single-cell platforms and preprocessing tools and includes steps for empty droplet detection, generation of standard QC metrics, prediction of doublets, and estimation of ambient RNA. It can run on the command line, within the R console, on the cloud platform or with an interactive graphical user interface. Overall, the SCTK-QC pipeline streamlines and standardizes the process of performing QC for scRNA-seq data.

Privacy-preserving quality control of neuroimaging datasets in federated environments.

Privacy concerns for rare disease data, institutional or IRB policies, access to local computational or storage resources or download capabilities are among the reasons that may preclude analyses that pool data to a single site. A growing number of multisite projects and consortia were formed to function in the federated environment to conduct productive research under constraints of this kind. In this scenario, a quality control tool that visualizes decentralized data in its entirety via global aggregation of local computations is especially important, as it would allow the screening of samples that cannot be jointly evaluated otherwise. To solve this issue, we present two algorithms: decentralized data stochastic neighbor embedding, dSNE, and its differentially private counterpart, DP‐dSNE. We leverage publicly available datasets to simultaneously map data samples located at different sites according to their similarities. Even though the data never leaves the individual sites, dSNE does not provide any formal privacy guarantees. To overcome that, we rely on differential privacy: a formal mathematical guarantee that protects individuals from being identified as contributors to a dataset. We implement DP‐dSNE with AdaCliP, a method recently proposed to add less noise to the gradients per iteration. We introduce metrics for measuring the embedding quality and validate our algorithms on these metrics against their centralized counterpart on two toy datasets. Our validation on six multisite neuroimaging datasets shows promising results for the quality control tasks of visualization and outlier detection, highlighting the potential of our private, decentralized visualization approach.

A novel deep ordinal classification approach for aesthetic quality control classification

Nowadays, decision support systems (DSSs) are widely used in several application domains, from industrial to healthcare and medicine fields. Concerning the industrial scenario, we propose a DSS oriented to the aesthetic quality control (AQC) task, which has quickly established itself as one of the most crucial challenges of Industry 4.0. Taking into account the increasing amount of data in this domain, the application of machine learning (ML) and deep learning (DL) techniques offers great opportunities to automatize the overall AQC process. State-of-the-art is mainly oriented to approach this problem with a nominal DL classification method which does not exploit the ordinal structure of the AQC task, thus not penalizing the error among distant AQC classes (which is a relevant aspect for the real use case). The paper introduces a DL ordinal methodology for the AQC classification. Differently from other deep ordinal methods, we combined the standard categorical cross-entropy with the cumulative link model and we imposed the ordinal constraint via the thresholds and slope parameters. Experimental results were performed for solving an AQC task on a novel image dataset originated from a specific company’s demand (i.e., aesthetic assessment of wooden stocks). We demonstrated how the proposed methodology is able to reduce misclassification errors (up to 0.937 quadratic weight kappa loss) among distant classes while overcoming other state-of-the-art deep ordinal models and reducing the bias factor related to the item geometry. The proposed DL approach was integrated as the main core of a DSS supported by Internet of Things (IoT) architecture that can support the human operator by reducing up to 90% the time needed for the qualitative analysis carried out manually in this specific domain.

Rebar inspection integrating augmented reality and laser scanning

Assuring conformity regarding the number, spacing, and location of rebars is an important quality control task in a rebar operation. Nonconformity attributed to missing or mislocated rebars adversely affects structural performance and construct-ability. Rebar craftsmen normally read two-dimensional (2D) shop drawings and install rebars after converting 2D objects to three-dimensional objects in a cognitive image. To ensure conformity, the installation is inspected manually, hence, being tedious and costly. Existing sensing technologies do not lend themselves to effective visualization of the data obtained by the sensing process either in practice or academia. This paper presents an end-to-end method that addresses the existing limitation by hybridizing augmented reality (AR) and laser scanning technologies to provide intuitive and accurate rebar inspection. The AR prototype visualizes rebar inspection outputs and provides rework instructions in an effective manner. An experiment validating the method was performed using a laboratory-scale rebar layout. The results confirmed that the method successfully highlights detailed dimensional information of mislocated rebars and provides inspectors with intuitive rework instructions. Indeed, the method provides a way to detect and repair the nonconformity involved in rebar positions regardless of type, shape, and/or complexity. The method encourages accurate rebar dimensional inspection and intuitive visualization, hence, contributing to effective rebar quality control.

Low-Cost Education Kit for Teaching Basic Skills for Industry 4.0 Using Deep-Learning in Quality Control Tasks

The main purposes of this paper are to offer a low-cost solution that can be used in engineering education and to address the challenges that Industry 4.0 brings with it. In recent years, there has been a great shortage of engineering experts, and therefore it is necessary to educate the next generation of experts, but the hardware and software tools needed for education are often expensive and access to them is sometimes difficult, but most importantly, they change and evolve rapidly. Therefore, the use of cheaper hardware and free software helps to create a reliable and suitable environment for the education of engineering experts. Based on the overview of related works dealing with low-cost teaching solutions, we present in this paper our own low-cost Education Kit, for which the price can be as low as approximately EUR 108 per kit, for teaching the basic skills of deep learning in quality-control tasks in inspection lines. The solution is based on Arduino, TensorFlow and Keras, a smartphone camera, and is assembled using LEGO kit. The results of the work serve as inspiration for educators and educational institutions.

SAFETY OF CONSUMER GOODS AS THE MAIN PROBLEM OF MODERN COMMODITY SCIENCE

The problem of the safety of consumer goods and the way they are stored is relevant in the modern world. Particular attention is paid to the issue of packaging, namely the choice of this type of packaging, which will not harm the health of consumers. the issue of reducing the negative impact on the environment is also important. Currently, one of the main directions in the development of commodity science is the problem of the safety of consumer goods. With the development of scientific and technological progress and the globalization of production, the number and scale of goods produced are constantly increasing. In these conditions, control over the complete quality control of the product itself is lost. Therefore, the issue of product safety is becoming increasingly important. The main task of product quality control is to provide consumers with products that will not harm their physical and psychological health. Harmful substances pose a danger not only to humans, but also to the environment. The packaging of goods also has a negative impact on nature. It is necessary to ensure the ability to decompose in nature or to introduce systems for the recycling of such materials (recycling). Today, the pollution of the environment in Ukraine with harmful substances is quite high, but modern science is able to minimize this problem as much as possible. The article discusses the existing types of dangers from low-quality food products, the negative impact of some types of packaging on human health. The main possible ways of processing the packaging of goods after their consumption are considered. The authors believe that in Ukraine it is necessary to strengthen the quality control of goods, introduce and stimulate the development of recycling and disposal of packaging to ensure the preservation of the health of citizens and reduce the impact on the environment of negative factors of human existence. The authors noted that in order to minimize the negative impact on the environment, manufacturers need to use safe materials for packaging. Consumers are encouraged to support those manufacturers who produce quality products and promote innovative recycling systems. The production of ecological, safe goods, their safe processing and disposal, ecological packaging do not lose their relevance and have become one of the main directions of development of modern commodity science.

Efficient Fused-Attention Model for Steel Surface Defect Detection

Steel surface defect detection is an essential quality control task in manufacturing. As patterns of defects may be viewed as an object, some current defect detection methods, which have achieved promising performance, have been developed based on object-detection models. However, most of these defect detection methods simply incorporate additional heavy modules to improve the accuracy. These methods do not consider the efficiency of the models or the characteristics of the defects. In this paper, we focus on three challenges of steel surface defect detection, which are scale variations, shape variations, and detection efficiency. To address these challenges, we propose a fused-attention network (FANet) for detecting various steel surface defects. Specifically, we propose a fused-attention framework for efficiently detecting defects. This framework applies an attention mechanism to a single balanced feature map, rather than multiple feature maps. This can improve the accuracy and preserve the detection speed of the detection network. To handle defects with multiple scales, we propose an adaptively balanced feature fusion (ABFF) method that can fuse features with suitable weights. It can enhance the discriminative power of the feature maps for detecting defects of different scales. Moreover, we propose a fused-attention module (FAM) to deal with the shape variations of defects. This module can enhance the channel and spatial feature information to perform precise localization and classification of defects with shape variations. Experimental results on two steel surface defect detection datasets, NEU-DET and GC10-DET, demonstrate that our proposed method can achieve state-of-the-art performance.

Wood moisture monitoring and classification in kiln‐dried timber

Different mass timber structures require a specific range of wood moisture. Thus, moisture monitoring is a crucial quality control task during timber drying that impacts the properties and quality of the subsequent timber structures. Variations in the average and distribution of the final moisture can result in significant variations in the timber properties and thus negatively affect performance in service. This necessitates rigorous moisture monitoring and timber grading during and after kiln drying. This study aims to estimate the kiln-dried population final moisture distribution based on green timber characteristics and machine learning. This will facilitate the timber grading process by reducing the variation in the mechanical properties of the kiln-dried timber, as timbers with a higher chance of moisture variation will be identified before the drying process. Timber initial moisture, initial weight, basic density, and target moisture were used to train machine learning models. Linear discriminant analysis and decision tree were used for moisture classification. Principal component and variable clustering analysis were performed to study the critical parameters affecting the timber overdrying and underdrying. The results indicated that initial moisture level and weight are the essential variables, while density had the least significant effect on the performance of classification models. The decision tree approach exhibited better performance than discriminant analysis with ~91% classification accuracy proving the effectiveness of using initial timber properties for quality control and grading of kiln-dried timber.

A disposable gold foil paper-based aptasensor for detection of enteropathogenic Escherichia coli with SERS analysis and magnetic separation technology.

Rapid and sensitive detection of enteropathogenic Escherichia coli (EPEC) in fluids with complex background is an important task for safety quality control in the field of medicine, environment, and food. In this study, a gold foil paper-based aptasensor was developed for the detection of enteropathogenic EPEC O26:K60 with surface-enhanced Raman spectroscopy(SERS) and magnetic separation technology mediated by Fe3O4@Au composite. The gold foil paper was firstly modified with thiolated capture probe and SERS tag. Thethiolated aptamer probe for EPEC was immobilized onto a Fe3O4@Au composite. In the presence of EPEC, highly specific recognition between the aptamer probe and EPEC made the Fe3O4@Au composite partially dissociated from the gold foil paper. This led to a decreased Raman intensity response, which showed an obvious negative linear correlation with increasing concentration of EPEC over a wide concentration range from 10 to 107CFU/mL under an excitation wavelength of 633nm. The detection limit was about2.86CFU/mL in a buffer solution and a licorice extractum and the detection time was only 2.5h. Theresults demonstrate that the gold foil paper-based aptasensor can be an excellent biosensing platform that offers a reliable, rapid, and sensitive alternative for EPEC detection.

GUIDING PRINCIPLES FOR POLICY DEVELOPMENT ON QUALITY ASSURANCE IN SCHOOL EDUCATION: EUROPEAN EXPERIENCE FOR UKRAINIAN SUCCESS

In present-day conditions, the quality of education is becoming the most important characteristic that determines the competitiveness of educational institutions and national education systems. The tasks of quality assurance and control are central to the educational reforms of many countries, including Ukraine. The problem of measuring and evaluating learning outcomes is becoming one of the most important in pedagogical theory and practice, as its solution allows to determine the effectiveness and ways to improve the content, methods and organization of learning, management system. Objective, theoretically substantiated measurements and evaluation of learning outcomes can give teachers and heads of educational institutions information about the pedagogical process, educational achievements of each student, identify the impact of various factors on the course of learning and its results. The issue of quality assurance continues to be relevant for Ukraine in the context of the reform of the New Ukrainian School and attempts to create a system of education evaluations, which should focus on four key positions: promoting the quality of primary, basic secondary and complete secondary education; accumulation of objective information about the educational process and learning outcomes of students of general secondary education; support of the educational process in general secondary education institutions (what and how is assessed – that is why attention is paid to the extent of organizing the educational process); supporting teachers, increasing trust in them. The experience of EU countries where similar educational assessment systems have operated successfully can be useful for Ukrainian policy-makers and researchers in the field of education.

HAZARD ANALYSIS CRITICAL CONTROL POINT PADA PRODUKSI PERKEDEL DAGING DI INSTALASI GIZI RUMAH SAKIT MUHAMMADIYAH LAMONGAN

HACCP (Hazard Analysis Critical Control Point) was quality assurance system used in supervision in hospital nutrition services. The application of HACCP at Muhammadiyah Hospital Lamongan in production at the nutrition installation was carried out to anticipate hazards and identify control points by paying attention to preventive measures rather than control in testing meat perkedel products. The research design used in this study is descriptive analytic which aims to determine the application of HACCP to the production of meat perkedel in the nutrition installation of Muhammadiyah Hospital Lamongan. Some of the HACCP implementations are not appropriate, namely the HACCP team, the requirements of the HACCP team members, quality control tasks, and APD forms. The implementation of HACCP had been in accordance with the schedule of HACCP verification on patient food processing and control devices at the nutrition installation of Muhammadiyah Hospital Lamongan.

A normalized signal calibration with a long-term reference improves the robustness of RPLC-MRM/MS lipidomics in plasma.

Improving the reliability of quantification in lipidomic analyses is crucial for its successful application in the discovery of new biomarkers or in clinical practice. In this study, we propose a workflow to improve the accuracy and precision of lipidomic results issued by the laboratory. Lipid species from 11 classes were analyzed by a targeted RPLC-MRM/MS method. The peak areas of species were used to estimate concentrations by an internal standard calibration approach (IS-calibration) and by an alternative normalization signal calibration schema (NS-calibration). The latter uses a long-term reference plasma material as a matrix-matched external calibrator whose accuracy was compared to the NIST SRM-1950 mean consensus values reported by the Interlaboratory Lipidomics Comparison Exercise. The bias of lipid concentrations showed a good accuracy for 69 of 89 quantified lipids. The quantitation of species by the NS-calibration schema improved the within- and between-batch reproducibility in quality control samples, in comparison to the usual IS-calibration approach. Moreover, the NS-calibration workflow improved the robustness of the lipidomics measurements reducing the between-batch variability (relative standard deviation <10% for 95% of lipid species) in real conditions tested throughout the analysis of 120 plasma samples. In addition, we provide a free access web tool to obtain the concentration of lipid species by the two previously mentioned quantitative approaches, providing an easy follow-up of quality control tasks related to lipidomics.

Построение регрессионных уравнений для виртуальных анализаторов основных показателей качества процесса алкилирования

The growth prospects of the chemical and petrochemical industries are shaped by the necessity of developing and implementing advanced technological solutions for improving operational performance of production by the effect of selecting the most suitable process parameters and predicting quality factors of the end products once very minute. The task of continuous product quality control may be successfully resolved by means of implementing an Advanced Process Control System (APCS). The functioning of APCS is based on virtual analyzers (VA) of product quality. A virtual analyzer is a mathematical model which is being developed with reference to long-time statistics of lab collected and performance data. Virtual analyzers facilitate assessing the desired, rather than directly measured, product quality factors by product parameters which are under continuous control by means of advanced control systems. The article describes an approach to developing a system of virtual analyzers for target quality factors of alkylphenols based on regression equations that have been formulated by the least-squares method.

Vehicle Components Defect Detection via Machine Vision

The task of quality control and automatic defect detection on production lines is one of the most important tasks of machine vision and digital image processing (DIP). This article provides one example of solving such a problem using various DIP algorithms.