On-line Quality Control System Research Articles

Design and Implementation of an On-Line Quality Control System for Latch-Based True Random Number Generator

Design and Implementation of an On-Line Quality Control System for Latch-Based True Random Number Generator

Trends in percutaneous coronary intervention in China: analysis of China PCI Registry Data from 2010 to 2018

Background and purpose: In the past 30 to 40 years, percutaneous coronary intervention (PCI) in China has greatly improved. The State Ministry of Health established a quality control (QC) system in 2009, and all medical centers were required to report their PCI cases. We analyzed the data from the QC system to describe the current status and development of PCI in China. Methods: PCI case data during a period from 2010 to 2018 were extracted from the online QC system. Data quality was audited by QC centers at the provincial level. Statistical analysis was mainly descriptive. Trend analysis was conducted by recoding the year as a continuous variable in linear regression or logistic regression. Results: The number of medical centers reporting PCI cases was 941 in 2010, and increased to 1,788 in 2018. Total number of PCI cases reported online to the QC system was 236,717 in 2010, and increased to 705,970 in 2018. Estimate of the actual case number that included those reported offline only (based on offline audit by provincial QC centers) was 284,936 in 2010, and increased to 915,256 in 2018. The most common indication for PCI was unstable angina (52.9%–59.1%), followed by ST elevation myocardial infarction (STEMI; 25.0%–27.4%). Majority of the PCI procedures were performed via the radial artery (65.4% in 2010 and 90.7% in 2018). Angiography detected a left main lesion in 5%, single-vessel disease in 38.7% to 44.4%, two-vessel disease in 22.3% to 27.5%, and three-vessel disease in 27.0% to 28.7% of the patients. The rate of primary PCI for STEMI in all STEMI cases was 28.0% in 2010, and increased to 45.9% in 2018. The average number of stents implanted per case was 1.63 in 2010, and decreased to 1.46 in 2018. There was a trend for decreasing mortality (0.31% in 2010; 0.26% in 2018). Conclusions: The number of PCI cases in China increased steadily during a period from 2010 to 2018, with stable mortality rate. Transradial access is the dominant PCI route. The rate of primary PCI for STEMI increased substantially, but remained relatively low.

Development of an Online Quality Control System for Injection Molding Process

This research developed an adaptive control system for injection molding process. The purpose of this control system is to adaptively maintain the consistency of product quality by minimize the mass variation of injection molded parts. The adaptive control system works with the information collected through two sensors installed in the machine only—the injection nozzle pressure sensor and the temperature sensor. In this research, preliminary experiments are purposed to find master pressure curve that relates to product quality. Viscosity index, peak pressure, and timing of the peak pressure are used to characterize the pressure curve. The correlation between product quality and parameters such as switchover position and injection speed were used to produce a training data for back propagation neural network (BPNN) to compute weight and bias which are applied on the adaptive control system. By using this system, the variation of part weight is maintained to be as low as 0.14%.

A computer vision based online quality control system for textile yarns

Yarn quality control is a crucial step in producing high quality textile end products. Online yarn testing can reduce latency in necessary process control by providing rapid insights into yarn quality, leading to production of superior quality yarns. However, both widely used capacitance based evenness testers and emerging imaging based evenness testing systems are largely offline in operation (i.e. a posteriori). A suitable online system that could be employed to test quality of a variety of yarns in normal industrial processing conditions does not yet exist.In this study, we propose an online evenness testing system for measurement of a certain type of yarn defect called nep by using imaging and computer vision techniques. The developed system directly captures yarn images on a spinning frame and uses Viola-Jones object detection algorithm for real-time detection of nep defects. The validation of nep detection algorithms and comparison of the new method with an existing evenness tester in terms of nep count demonstrated its reasonable defect detection accuracy and promising potential for application in wider yarn spinning industry.

Online Quality Control System in Single Jersey Circular Knitting Machine

With the great development in the technology of manufacturing knitting machines, and high productivity. There are still some drawbacks that might be overcome by applying novel measurement methods, particularly, in the circular single Jersey knitting machines. The most important of these drawbacks is the laddering defects. In this paper, an online quality control system - the first of its kind - was developed and assembled on the machine and consists of a transmission unit (laser) and a receiving unit (photocell), to detect laddering defects in real-time. Once occurred, the system beeps to draw the attention of the operator and the machine stops automatically, the machine then runs again after the defect is being fixed. Thus, this system can reduce the production of the second degree, raise the level of quality, and reduce the production costs per kilograms of the produced fabric. This system proved its sensitivity and efficiency while the machine runs at the typical production speeds.

The LONI QC System: A Semi-Automated, Web-Based and Freely-Available Environment for the Comprehensive Quality Control of Neuroimaging Data.

Quantifying, controlling, and monitoring image quality is an essential prerequisite for ensuring the validity and reproducibility of many types of neuroimaging data analyses. Implementation of quality control (QC) procedures is the key to ensuring that neuroimaging data are of high-quality and their validity in the subsequent analyses. We introduce the QC system of the Laboratory of Neuro Imaging (LONI): a web-based system featuring a workflow for the assessment of various modality and contrast brain imaging data. The design allows users to anonymously upload imaging data to the LONI-QC system. It then computes an exhaustive set of QC metrics which aids users to perform a standardized QC by generating a range of scalar and vector statistics. These procedures are performed in parallel using a large compute cluster. Finally, the system offers an automated QC procedure for structural MRI, which can flag each QC metric as being ‘good’ or ‘bad.’ Validation using various sets of data acquired from a single scanner and from multiple sites demonstrated the reproducibility of our QC metrics, and the sensitivity and specificity of the proposed Auto QC to ‘bad’ quality images in comparison to visual inspection. To the best of our knowledge, LONI-QC is the first online QC system that uniquely supports the variety of functionality where we compute numerous QC metrics and perform visual/automated image QC of multi-contrast and multi-modal brain imaging data. The LONI-QC system has been used to assess the quality of large neuroimaging datasets acquired as part of various multi-site studies such as the Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) Study and the Alzheimer’s Disease Neuroimaging Initiative (ADNI). LONI-QC’s functionality is freely available to users worldwide and its adoption by imaging researchers is likely to contribute substantially to upholding high standards of brain image data quality and to implementing these standards across the neuroimaging community.

Quality Indexes Design for Online Monitoring Polymer Injection Molding

Quality control is a crucial issue in the injection molding process with target of obtaining a high yield rate and reducing production cost. Consequently, effective methods for monitoring the injection conditions (e.g., pressure, velocity, and temperature) in real-time and adjusting these conditions adaptively as required to ensure a consistent part quality are essential. This study proposes a quality index based on the clamping force increment during the injection cycle, as determined by four strain gauges attached to the tie bars of the injection molding machine. Also, various quality indexes for online quality monitoring and prediction purposes based on the pressure, viscosity, and energy features extracted from the pressure profiles obtained at the load cell, nozzle, and molding cavity, respectively, are compared. The feasibility of the proposed quality indexes is investigated experimentally for various settings of the barrel temperature, back pressure, and rotational speed of the plasticizing screw. It is shown that all of the quality indexes are correlated with the injection-molded quality and hence provide a feasible basis for the realization of an on-line quality monitoring and control system. Particularly, the tie-bar elongation quality index requires no modification or invasion of the injection molding system or cavity and hence provides a particularly attractive solution for monitoring and controlling the part quality.

Feature identification using acoustic signature of Ocean Researcher III (ORIII) of Taiwan

Feature identification using acoustic signature of Ocean Researcher III (ORIII) of Taiwan

Online cracking detection by means of optical techniques in laser-cladding process

Additive manufacturing processes are advanced production methods used to build parts layer by layer or to repair components. One of the common limitations of this type of processes is the presence of defects that may cause failure of the part. In order to avoid this undesirable situation, quality control is essential. This is typically performed postprocess, once the part has been manufactured. However, online quality control systems would bring great benefits to detect the defects whereas they occur. In this work, a novel method is presented based on three online monitoring systems; thermal imaging and two different acoustic emission sensors. The results given by these methods are compared with traditional inspection procedures and validated by microscopic analysis. Results reveal the effectiveness of the procedure for detecting two different cracking mechanisms and also to determine values above which no layer cracking occurs.

Identification of Volatile Oxidation Compounds as Potential Markers of Walnut Oil Quality.

In this study, a 10-day accelerated storage at 60 °C was carried out to investigate the evolution of volatile profiles of walnut oils from three cultivars (cvs. Santai, Xiangling, Qingxiang). Eighteen volatile oxidation compounds, including three alcohols, 11 aldehydes, one furan, one ketone, and two acids, were identified in all oil samples. Data from the three cultivars were combined and the analysis showed that 2-heptenal and 1-octen-3-ol exhibited the strongest linear correlations with peroxide value (PV), p-anisidine value (p-AnV), and the residual content of total tocopherols (Rtocos ). The contents of both compounds were also proved to be significant variables for PV, p-AnV, and Rtocos , according to the jack-knife uncertainty test applied in partial least squares analysis. Hence, 2-heptenal and 1-octen-3-ol could be used as potential markers of walnut oil quality. Besides, the oleic-derived compounds such as octanal and nonanal were suggested to be used to indicate notable decrease of PUFAs during walnut oil oxidation, since they showed the strongest negative correlations with the residual content of PUFAs (RPUFAs ). PRACTICAL APPLICATION: Nowadays a large-scale planting of walnut has taken place in China. Since walnut contains more than 60% of oil, the development of walnut oil is of great significance. Volatiles such as aldehydes are secondary oxidation products. Study on the dynamic changes of their types and contents could be helpful to understand the process of walnut oil oxidation. The measurement of walnut oil quality could also be more convenient and predictable with the use of volatile markers. Hence, this research provides insights for the future development of online quality control system for walnut oil.

Self-Optimizing Robot Vision for Online Quality Control

An online quality control system based on robot vision designed as a quality control agent is presented. It is part of a multi-agent system conceived for control of a production line at factory level, integrating quality control with process control. The implementation of self-optimizing behaviors aimed to improve reliability and confidence level of the diagnosis in a quality control system is also explained. Focus is on integration of advanced state-of-the-art experimental techniques to a real-world industrial problem. These concepts are developed for a robot vision system in eye-in-hand configuration for in-line inspections, which allows the flexibility necessary to implement agent behaviors. The design of the quality control station is presented. The set of self-optimization strategies, which have been implemented, is outlined. They consist in (a) the spatial repositioning of the vision system so as to optimize the line of sight and allow imaging of parts, whichmay randomly be hidden by other components and (b) self-optimization in image acquisition and processing. This increases the level of confidence in the diagnostic output. Tests on the laboratory prototype show the performance improvement of the inspection and allow to discuss the potential of agent technologies for in-line quality control.

Ultrasonic sensor based defect detection and characterisation of ceramics

Ceramic tiles, used in body armour systems, are currently inspected visually offline using an X-ray technique that is both time consuming and very expensive. The aim of this research is to develop a methodology to detect, locate and classify various manufacturing defects in Reaction Sintered Silicon Carbide (RSSC) ceramic tiles, using an ultrasonic sensing technique. Defects such as free silicon, un-sintered silicon carbide material and conventional porosity are often difficult to detect using conventional X-radiography. An alternative inspection system was developed to detect defects in ceramic components using an Artificial Neural Network (ANN) based signal processing technique. The inspection methodology proposed focuses on pre-processing of signals, de-noising, wavelet decomposition, feature extraction and post-processing of the signals for classification purposes. This research contributes to developing an on-line inspection system that would be far more cost effective than present methods and, moreover, assist manufacturers in checking the location of high density areas, defects and enable real time quality control, including the implementation of accept/reject criteria.

Assessment of breadmaking performance of wheat flour dough by means of frequency dependent ultrasound

Technological performance of wheat flour varies among different wheat varieties. Gluten plays a key role within the solid phase of dough in the formation and the retention of gas bubbles during breadmaking. Rheological tests are usually performed to predict breadmaking potential. The aim here was to investigate the ability of ultrasound to discriminate wheat doughs based on breadmaking qualities. The ultimate goal is the development of an online quality control system currently unavailable in the baked goods industry, rendering this work innovative. Samples were prepared from a strong wheat flour, with one control sample and one added with inulin and distilled monoglycerides, producing doughs of distinct breadmaking quality. Doughs were subjected to density determination, elongation tests, and ultrasound analysis. The ultrasound tests were performed in the frequency range of 300 kHz – 6 MHz. Ultrasonic phase velocity increased with increasing frequency to about 2 MHz, becoming constant and then decreasing from 3 MHz for the control sample. Distinct differences in attenuation coefficient between the fibre-enriched and control doughs were observed. Ultrasound can potentially add to a better understanding of dough quality and can discriminate between doughs of contrasting properties.

Characterization of mineral paper by air-coupled ultrasonic spectroscopy

A novel technology in the paper industry makes possible to produce paper by using a mineral powder and a polymer instead of cellulose fibers. This new product is called mineral paper, it presents some potential environmental advantages compared with conventional paper, while it exhibit a similar appearance and properties. The purpose of this work is to determine the possibilities of an air-coupled ultrasonic technique using wide band signals and spectral analysis to study this kind of materials. As no direct contact nor coupling fluids between the paper and the transducers is required, this technique is specially well suited to this problem. It also offers good perspectives for the development of a on-line quality control system. A through transmission technique (0.15–2.3MHz) is employed and Fourier analysis is performed to obtain both magnitude and phase spectra of the transmission coefficient. Properties in the thickness direction as well as in the paper plane has been determined by the excitation and analysis of thickness and plate resonances at several incident angles and different directions within the paper plane. Different paper grades (from 140 to 480g/m2) have been studied. Very high attenuation coefficients and very low propagation velocities (and hence elastic constant) have been obtained for most cases, this can be explained by considering the large porosity of this material (up to 50%) and the microstructure: a mixture of solid grains with a resin with a relatively large fraction of air-filled pores. Measurements show that unlike conventional cellulose machine made paper this material is transversely isotropic (isotropic in the paper plane) and that the degree of anisotropy (when in-plane directions are compared with the thickness direction) largely depends on the level of resin impregnation.

Hybrid active learning for reducing the annotation effort of operators in classification systems

Active learning is understood as any form of learning in which the learning algorithm has some control over the input samples due to a specific sample selection process based on which it builds up the model. In this paper, we propose a novel active learning strategy for data-driven classifiers, which is based on unsupervised criterion during off-line training phase, followed by a supervised certainty-based criterion during incremental on-line training. In this sense, we call the new strategy hybrid active learning. Sample selection in the first phase is conducted from scratch (i.e. no initial labels/learners are needed) based on purely unsupervised criteria obtained from clusters: samples lying near cluster centers and near the borders of clusters are expected to represent the most informative ones regarding the distribution characteristics of the classes. In the second phase, the task is to update already trained classifiers during on-line mode with the most important samples in order to dynamically guide the classifier to more predictive power. Both strategies are essential for reducing the annotation and supervision effort of operators in off-line and on-line classification systems, as operators only have to label an exquisite subset of the off-line training data resp. give feedback only on specific occasions during on-line phase. The new active learning strategy is evaluated based on real-world data sets from UCI repository and collected at on-line quality control systems. The results show that an active learning based selection of training samples (1) does not weaken the classification accuracies compared to when using all samples in the training process and (2) can out-perform classifiers which are built on randomly selected data samples.

Coaxial Control of Aluminum and Steel Laser Brazing Processes

Laser brazing has become firmly established as a joining process in the automotive industry. While this process offers many advantages, brazed seams also have to meet particularly high quality requirements. The challenge of creating a suitable online quality control system is magnified by the increasing use of aluminum in automotive engineering. This paper introduces recent works on the development of a system for controlling both aluminum and steel brazing processes. It also discusses some of the challenges connected to this task and discloses some of the results derived from the corresponding process analysis.

In-situ quality monitoring during laser brazing

Laser brazing of zinc coated steel is a widely established manufacturing process in the automotive sector, where high quality requirements must be fulfilled. The strength, impermeablitiy and surface appearance of the joint are particularly important for judging its quality. The development of an on-line quality control system is highly desired by the industry. This paper presents recent works on the development of such a system, which consists of two cameras operating in different spectral ranges. For the evaluation of the system, seam imperfections are created artificially during experiments. Finally image processing algorithms for monitoring process parameters based the captured images are presented.

Improved Pattern Clustering Algorithm for Recognizing Transversal Distribution of Steel Strip Thickness

Transversal distribution of the steel strip thickness in the entry section of the cold rolling mill seriously affects to the flatness and transversal thickness precision of the final products. Pattern clustering method is introduced into the steel rolling field and used in the patterns recognition of transversal distribution of the steel strip thickness. The well-known k-means clustering algorithm has the advantage of being easily completed, but still has some drawbacks. An improved k-means clustering algorithm is presented, and the main improvements include: (1) the initial clustering points are preselected according to the density queue of data objects; and (2) Mahalanobis distance is applied instead of Euclidean distance in the actual application. Compared to the patterns obtained from the common k-means algorithm, the patterns identified by the improved algorithm show that the improved clustering algorithm is well suitable for the patterns recognition of transversal distribution of steel strip thickness and it will be useful in online quality control system.

Implantation of an On-Line Quality Control System for the Glucose Meters of the Health Area From the Virgen Macarena University Hospital

s: Critical and Point-of-Care Testing: Managing Technology for the Benefit of All Populations: 22nd International Symposium: September 17-20, 2008: Catalonia Barcelona Plaza Hotel|Barcelona, Spain

Resultados de la implantación de un sistema de control de calidad para los glucómetros del Área Hospitalaria Virgen Macarena, con conexión on-line al Laboratorio de Bioquímica Clínica, durante el período 2003–2007 ,

Objectives To implement an on-line quality control system in the Virgen Macarena Hospital Health Area (2 hospitals, 3 specialised care centres, 1 dialisis centre and 2 primary care centres), in order to achieve the ADA/AACC/NACB recommended analytical goals, and maintain them by on-line control from the Clinical Biochemistry Laboratory. Materials and methods 90 glucose meters (PCx from Abbott) connected on-line to the central information network of the hospital. Meters send the patient and control measurements to the Central Data Station, where they are registered. Then data are analysed in the Clinical Biochemistry laboratory. Before implementing the system, we carried out training program for personnel with responsibilities for glucose monitoring (more than 900 nurses). Periodical reports are sent to the different health care units. The report includes quality control data and the consumables used. Results During the last 5 years more than 1,500,000 blood glucos determinations and more than 140,000 controls were measured. Mean total error of control measurements was 6.37% (2.18–7.52) at a low glucose level and 5.39% (1.53–7.52) at high glucose levels. Conclusions Glucose monitoring of the patients from the Virgen Macarena University Hospital Health Area is now under the responsibility of the Clinical Biochemistry Labratory. The new system has demonstrated that the glucose meters from Specialised Care in a whole Health Area can be controled on-line, and achieving the analyical goals recommended by ADA/AACC/NACB. The success has been based on the educational program together wit the motivation of nurses.