The data model of production flow and quality control system

Impact of Quality Control on Production System Performance

High-count cashmere is the inevitable requirement of the high-end yarn cashmere products.To the short of the high-count cashmere yarn production and the difficult of spinning,From raw material selection, technological process, equipment transformation, parameter optimization, temperature and humidity control and other aspects,we analysised the high-ount cashmere yarn production process and quality control system,The development of the woolen 48Nm cashmere yarn has been done,it is of first class.

This chapter describes the techniques for following the movement of product as it passes through the various manufacturing steps, techniques for controlling this movement, and some of the ways of reporting product status. Modern tracking systems are computer-based with real time entry of data at the reporting points using a combination of manual and automatic input techniques described later in this chapter. The Product Tracking technique provides, by physically identifying the product, a means for self correction of counts and absolute physical accountability each time product movement is recorded. The chapter illustrates by example how this movement is reported. It examines the control of product flow into a cell from a reporting point and control within the cell between reporting points. Product flow control within the cell then diverts the product to an alternative machine or prevents it from entering the machine where damage to the product or machine can occur.

In view of the problems of lagging and poor predictability for ship assembly and welding quality control, the digital twin technology is applied to realize the quality prediction and control of ship group product. Based on the analysis of internal and external quality factors, a digital twin-based quality prediction and control process was proposed. Furthermore, the digital twin model of quality prediction and control was established, including physical assembly and welding entity, virtual assembly and welding model, the quality prediction and control system, and twin data. Next, the real-time data collection based on the Internet of Things and the twin data organization based on XML were used to create a virtual-real mapping mechanism. Then, the machine learning technology is applied to predict the process quality of ship group products. Finally, a small group is taken as an example to verify the proposed method. The results show that the established prediction model can accurately evaluate the welding angular deformation of group products and also provide a new idea for the quality control of shipbuilding.

Quality transitivity and traceability system of herbal medicine products based on quality markers

In this study, we used artificial intelligence (AI) technology to explore for automated medical record quality control methods, standardize the process for medical record documentation, and deal with the drawbacks of manually implemented quality control. In this study, we constructed a medical record quality control system based on AI. We first designed and built, for the system, a quality control rule base based on authoritative standards and expert opinions. Then, medical records data were automatically collected through a data acquisition engine and were converted into structured data through a post-structured engine. Finally, the medical record quality control engine was combined with the rule base to analyze the data, identify quality problems, and realize automated intelligent quality control. This system was applied to the quality control of medical records and five quality control points were selected, including similarities in the history of the present illness, defects in the description of chief complaints, incomplete initial diagnosis, missing in formation in the history of menstruation, marriage, and childbirth, and mismatch between the chief complaints and the history of the present illness. We randomly selected 2 918 medical records of patients discharged in January 2022 to conduct AI quality control. Then we organized medical record quality control experts to conduct an accuracy review, made a comparison with previous manual quality control records, and analyzed the results. The number of quality problems that were verified in the accuracy review was taken as the gold standard and receiver operating characteristic (ROC) curves were drawn for the 5 quality control points. According to the accuracy review performed by medical record quality control experts, the accuracy of AI quality control reached 89.57%. For the sampled medical records, the results of AI quality control were compared with those of previous manually performed quality control and only one problem detected by manual quality control of the sampled medical records was not detected by the AI quality control system. The number of medical record quality problems correctly detected by AI quality control was about 2.97 times that of manual quality control. Analysis of the ROC curves showed that the AUC of the five quality control points of the AI quality control system were statistically significant (P<0.05) and all the AUC values approximated or exceeded 0.9. In contrast, results obtained through manually performed quality control found significant AUC (0.797) for only one quality control point-similarities in the history of present illness (P<0.05). Comparison of the AUC values of the two quality control methods showed that AI quality control system had an advantage over manually performed quality control for the five quality control points. Through the application of medical record quality control system based on AI, efficient full quality control of medical record documentation can be achieved and the detection rate of quality problems can be effectively improved. In addition, the system helps save manpower and improve the quality of medical record documentation.

The noise correction and image enhancements are done at the Data Processing Generation System (DPGS) at the National Remote Sensing Center (NRSC) satellite data production chain. Even after the noise correction algorithms, pixel noise is observed at the checkout by the Product Quality Control (PQC) system. These are detected manually at PQC and an alert is raised at the DPGS to recorrect the image. This process is time consuming due to the manual intervention and the application of noise correction algorithm on the entire image. This manuscript proposes a novel 2-stage, sliding window-based algorithm to automatically detect pixel noise in satellite images and localise the noise pixels so that the correction algorithms can be applied in a localised fashion. This local noise correction also restores the overall SNR of the image compared to global correction. This algorithm is realized and tested on data obtained from the Indian remote sensing (IRS) satellites like Cartosat-2S, Resourcesat-2/2A. The dataset is not open-sourced, and hence very minimal information is provided regarding the IRS data. However, we use Landsat-8 data to conduct a few analyses on the algorithm's performance. The role of patch size considered during the detection of pixel noise in satellite data is also analyzed.

For the successful operation of the tuberculosis infection foci monitoring system implemented in the city of Moscow since 2021, a quality assessment and control system was developed and implemented, which is a methodology that includes internal and external information control, separate quality control for household and ‘manufactory’ (not household) TB foci, and the functionality of joint assessment of the data from the «TB Foci» registry with information on indicator patients who created foci from other registries of the city’s tuberculosis monitoring system.The implementation of this system significantly improved the quality of data in the «TB Foci» registry, which is a key element in the successful operation of the monitoring system. As a result of the implementation of the quality control system, in 2023–2024 the number of household TB foci, information about which in the register was recognized as problematic, decreased from 326 to 115; the number of cases of lack of information on an «manufactory» TB foci decreased from 232 to 172; the number of suspected TB foci that were taken under monitoring control increased from 161 to 206; the proportion of newly diagnosed tuberculosis patients registered during the year, in whom TB foci were identified, increased from 69.6 to 72.6% (p &lt; 0.05), and the same proportion of newly diagnosed patients from the permanent population – from 85.9 to 89.8% (p = 0.016). One of the indirect results of the introduction of this system was that, with an overall decrease in the number of patients, district TB doctors registered and checked 146 more addresses (TB foci) in 2024 than in the previous year (3,559 addresses).An analysis of the quality control of primary documentation showed that it is critically important for effective monitoring of tuberculosis infection. Periodic monitoring compatible with methodological support provided feedback for rapid problem solving and included training for employees in record keeping.

In general, the company always pays attention to the quality of its products, which with good quality will increase consumer confidence in PT Langgeng Buana Jaya has implemented a production quality control system. However, in reality there are still products that are of poor quality, so it is necessary to analyze the quality control efforts implemented by the company and find out what causes the problem and look for improvement solutions using the Six Sigma method. The purpose of this study is to analyze and implement quality control of toy products at PT Langgeng Buana Jaya using the Six sigma approach. This research is a type of qualitative research and test the validity of this research data using source triangulation. The causes of product defects at PT Langgeng Buana Jaya based on the fishbone diagram are product defects due to stitches, scratches and color effects. This research contributes to minimize product defects by using the six-sigma method. Better prepare themselves in the process of collecting and collecting data and for further researchers it is hoped that they can conduct continuous research until the sigma number is high and the results can be even better. In general, the company always pays attention to the quality of its products, which with good quality will increase consumer confidence in PT Langgeng Buana Jaya has implemented a production quality control system. However, in reality there are still products that are of poor quality, so it is necessary to analyze the quality control efforts implemented by the company and find out what causes the problem and look for improvement solutions using the Six Sigma method. The purpose of this study is to analyze and implement quality control of toy products at PT Langgeng Buana Jaya using the Six sigma approach. This research is a type of qualitative research and test the validity of this research data using source triangulation. The causes of product defects at PT Langgeng Buana Jaya based on the fishbone diagram are product defects due to stitches, scratches and color effects. This research contributes to minimize product defects by using the six-sigma method. Better prepare themselves in the process of collecting and collecting data and for further researchers it is hoped that they can conduct continuous research until the sigma number is high and the results can be even better.

Standards of hygiene and health are of major importance in food production, and quality control has become obligatory in this field. Thanks to rapidly developing technologies, it is now possible for automatic and safe quality control of food production. For this purpose, image-processing-based quality control systems used in industrial applications are being employed to analyze the quality of food products. In this study, quality control of chicken (Gallus domesticus) eggs was achieved using a real time image-processing technique. In order to execute the quality control processes, a conveying mechanism was used. Eggs passing on a conveyor belt were continuously photographed in real time by cameras located above the belt. The images obtained were processed by various methods and techniques. Using digital instrumentation, the volume of the eggs was measured, broken/cracked eggs were separated and dirty eggs were determined. In accordance with international standards for classifying the quality of eggs, the class of separated eggs was determined through a fuzzy implication model. According to tests carried out on thousands of eggs, a quality control process with an accuracy of 98% was possible.

BackgroundColorectal and gastric cancer are major causes of cancer-related deaths. In Korea, gastrointestinal (GI) endoscopic biopsy specimens account for a high percentage of histopathologic examinations. Lack of a sufficient pathologist workforce can cause an increase in human errors, threatening patient safety. Therefore, we developed a digital pathology total solution combining artificial intelligence (AI) classifier models and pathology laboratory information system for GI endoscopic biopsy specimens to establish a post-analytic daily fast quality control (QC) system, which was applied in clinical practice for a 3-month trial run by four pathologists.Methods and findingsOur whole slide image (WSI) classification framework comprised patch-generator, patch-level classifier, and WSI-level classifier. The classifiers were both based on DenseNet (Dense Convolutional Network). In laboratory tests, the WSI classifier achieved accuracy rates of 95.8% and 96.0% in classifying histopathological WSIs of colorectal and gastric endoscopic biopsy specimens, respectively, into three classes (Negative for dysplasia, Dysplasia, and Malignant). Classification by pathologic diagnosis and AI prediction were compared and daily reviews were conducted, focusing on discordant cases for early detection of potential human errors by the pathologists, allowing immediate correction, before the pathology report error is conveyed to the patients. During the 3-month AI-assisted daily QC trial run period, approximately 7–10 times the number of slides compared to that in the conventional monthly QC (33 months) were reviewed by pathologists; nearly 100% of GI endoscopy biopsy slides were double-checked by the AI models. Further, approximately 17–30 times the number of potential human errors were detected within an average of 1.2 days.ConclusionsThe AI-assisted daily QC system that we developed and established demonstrated notable improvements in QC, in quantitative, qualitative, and time utility aspects. Ultimately, we developed an independent AI-assisted post-analytic daily fast QC system that was clinically applicable and influential, which could enhance patient safety.

This study describes and analyses a practical implementation of basic sensory analysis principles in a productive context related to typical products. As a case study, we investigated the quality control (QC) system of Trentingrana cheese, a variety of the protected designation of origin Grana Padano. The production of Trentingrana cheese factories is supervised by their consortium through the assessment of sensory quality attributes by an expert panel; and, depending on the evaluation, the associated dairies receive price premiums or penalties. The procedures applied and the data collected over 8 years were evaluated, and critical points were identified. Ameliorations were suggested in accordance with good sensory practice and were verified over an additional 3‐year period. The QC system has been improved by increasing the number of evaluated samples proportionally to the amount of the production of each dairy, by introducing a balanced experimental design, by monitoring experts' performance and by controlling the sample presentation to avoid systematic effects and psychological errors.Practical ApplicationsWith this work, we demonstrate the viability of applying the sensory approach to quality control (QC) within the food industry of typical products. The QC proposed here is a compromise between the practical constraints of implementing sensory testing procedures in an industrial context and the basic requirements of good sensory practice for collecting reliable data. Nevertheless, the implemented QC represents an example of an efficient strategy for protected designation of origin product valorization, fundamental for preserving both market position and consumer confidence and loyalty toward the product.

HLA typing is a laboratory test that allows to determine HLA antigens or HLA genes of a specific individual. The purpose of this article is to summarize the principles of organizing HLA laboratories, requirements for laboratory personnel, quality management, handling of biological material and standardization of laboratory equipment. Materials and methods. To achieve this goal, we analyzed the current domestic and international regulatory documents, as well as the main scientific publications of the last 5 years. Results. This article provides a detailed description of the general principles of organizing HLA laboratories, including requirements for the education and work experience of personnel, regular advanced training of personnel; management requirements, including maintaining an up-to-date fund of standard operating procedures; control of the correctness of the results obtained through a set of activities for internal laboratory quality control; a system of external quality control of HLA typing; a system of control over the maintenance of equipment in good condition, a system of quality control and continuous monitoring of the reagents used; prevention of conflicts of interest and ensuring the independence of research results from any pressure; regular internal audits of all work processes. The requirements for standardization of laboratory equipment and handling of biological material are also set out. Conclusion. A laboratory performing HLA typing is a complex system, the work of which involves many elements: personnel, infrastructure, technological processes, equipment, work methods, documentation, etc. The quality system of the HLA laboratory is ensured by the interconnectedness and interaction of all the above elements, which must be coordinated. The application of the presented requirements to the organization of HLA laboratories can contribute to increasing the efficiency and optimization of their activities, which, in turn, will improve the efficiency of selecting donor-recipient pairs for transplantation, determining diagnostically significant immunogenetic characteristics.

The requirements to the system of control over the quality of audit services are analyzed, which are specified in the Law of Ukraine “Audit of Financial Statements and Auditing” from 21.12.2017 No 2258-VIII; Directive of the Ministry of Finance of Ukraine “Approval of the Procedure for Inspection of the Quality Control of Audit Services” from 29.08.2019 No 362; Resolution of the Council for Supervision over Auditing at the State Institution “The Body of Public Supervision over Auditing” from 30.08.2019 No 6/7/13 “Information Questionnaire of Auditing Entity”. It is demonstrated that the quality control system requires corrections of the following: responsibilities of administration and staff of auditing entities for use of the adopted intra-firm provisions on the quality control system, requirements of ISQC1, IAS, the Law of Ukraine “Audit of Financial Statements and Auditing”; the requirements to staff (quality controller, reviewer, assignment partner); identification of threats to the independence and the auditor’s response on the assessed threats; the requirements to reviews of complaints, complicated issues; issues related with non-stop professional training of auditors. Special attention of auditors need to be drawn to the fact that the policies and procedures of the quality control system do not have to be confined to the audit of financial statements, but have to apply to other assignments on giving confidence and related auditor services. Besides that, assessment of threats to the independence has to be conducted for non-auditor services specified in Article 6 of the aforementioned Law of Ukraine.&#x0D; The studies conducted by the author allowed to elaborate procedures for quality control of auditor services; measures to stimulate staff of auditing entities to scrutinize requirements to implementation of the quality control system; obligating documents of auditing firms on assessment of ethical threats; sharing of duties among assignment group members; requirements to qualification of the key partner, quality controller; identification of auditor assignments; solutions of complicated issues and their documenting, measures on control of the assignment at the final phase and quality control of the assignment. The proposed measures are supposed to help auditing entities revise the already operating quality control systems in a timely manner, which will facilitate supply of high quality information to all the users of auditing reports, external quality controllers from the Body of Public Supervision over Auditing and the Auditor Chamber of Ukraine.

The presented article discusses the problems of quality control of construction materials. As we know, the quality of construction materials is one of the most pressing issues at the moment. Quality control means checking whether the quality indicators of goods meet the requirements of the adopted technical normative documents (standards, specifications, etc.). Quality control – set of quality indicators of the material is to check compliance with the requirements. This article also studies analysis of recent research and publications, the effectiveness of quality control methods and structure of functional diagnostics of quality control system. One of the most important areas of quality control is the diagnosis of control problems. Diagnostics – the identification and study of features that characterize the condition of building, structures of buildings and structures in order to identify possible deviations and prevent violations of the normal mode of operation. There are two main types of diagnostics, functional and systematic diagnostics. The main directions of functional diagnostic are described in tabular form. The structure of concrete is also considered here. Execution of concrete structures is a complex process that includes various activities. The basic principle of the development and the application of quality control system during the execution of concrete structures is the process approach. Defining the processes, identification and interaction of sub-processes are performed with the objective of quality monitoring and control that are required in order to achieve concrete structures’ specifications. Quality control system in the area of execution of concrete structures must also comply with national acts, rules, regulations and standards that are essential for concrete structures design, execution and concrete production. The analysis of specific compositions is presented in terms of diagnosing that structure