Quality Control Charts Research Articles

Advances in statistical quality control chart techniques and their limitations to cement industry

Advances in statistical quality control chart techniques and their limitations to cement industry

Establishment of risk quality control charts based on risk management strategies.

Risk management strategies have been proposed for applications in clinical laboratories to reduce patient risks; however, effective and visual risk-monitoring tools are currently lacking in medical laboratories. In this study, we constructed a risk quality control (QC) chart based on risk management strategies. We calculated the risk levels of QC materials based on Bayes' theorem by combining the total allowable error, QC results, and the maximum number of unacceptable errors in the laboratory. Then, we constructed a risk QC chart by presenting the Z values and corresponding risk levels of QC materials simultaneously. Finally, we evaluated the risk-monitoring capabilities of the risk QC charts by simulating different long-term errors in the laboratory. The risk levels of QC materials increased as the QC results moved further away from the set mean. Larger sigma values led to fewer risks obtained for the same QC results. The constructed risk QC charts intuitively showed specific risk levels and could warn lab staff out-of-control, without the need for QC rules to make judgments. The risk levels of erroneous results differed for items with different sigma performance. Risk-based QC charts allowed visualization of the QC results and specific risk levels simultaneously, providing more intuitive results than those obtained from traditional QC charts.

A Multi-Stage Ensemble Neural Network for Accurate Differentiation Between False and Real Alarms in Quality Control Charts

A Multi-Stage Ensemble Neural Network for Accurate Differentiation Between False and Real Alarms in Quality Control Charts

Physics-based detection of cyber-attacks in manufacturing systems: A machining case study

The overlap between operational technologies and information technology has resulted in profound improvements in the manufacturing ecosystem, but it increases the risk of a non-conventional class of cyber-attacks capable of inflicting physical damages on manufacturing processes and/or products. If successful in penetrating traditional cyber-only defenses, such attacks may not be detected timely, leading to financial losses, and potentially endangering human safety. However, malicious alterations of products and/or processes intended by these attacks can be manifested as anomalous changes in process dynamics. Hence, monitoring physical process variables such as vibration and power consumption (known as side-channels in cybersecurity literature) can provide a physical-domain defense layer to detect such attacks. Focusing on product-oriented attacks, we propose a method to connect the product design, process design, and in situ monitoring to identify the physical manifestations of these attacks. The proposed approach can verify the geometric integrity of a machined part by observing cutting power signals during machining. We utilize the process and product knowledge to segment the power signal into the cutting cycles corresponding to specific geometrical features and extract process-related information accordingly. This work primarily focuses on extracting machining times for individual geometric features in parts. Next, we use the extracted information to construct quality control charts to use in detecting geometric integrity deviations of machined parts. Finally, we demonstrate our proposed method using a case study of cyber-physical attacks on machining processes aiming to tamper with different product's dimensional and geometrical features.

Modeling the patient mix for risk-adjusted CUSUM charts.

The improvement of surgical quality and the corresponding early detection of its changes is of increasing importance. To this end, sequential monitoring procedures such as the risk-adjusted CUmulative SUM chart are frequently applied. The patient risk score population (patient mix), which considers the patients’ perioperative risk, is a core component for this type of quality control chart. Consequently, it is important to be able to adapt different shapes of patient mixes and determine their impact on the monitoring scheme. This article proposes a framework for modeling the patient mix by a discrete beta-binomial and a continuous beta distribution for risk-adjusted CUSUM charts. Since the model-based approach is not limited by data availability, any patient mix can be analyzed. We examine the effects on the control chart’s false alarm behavior for more than 100,000 different scenarios for a cardiac surgery data set. Our study finds a negative relationship between the average risk score and the number of false alarms. The results indicate that a changing patient mix has a considerable impact and, in some cases, almost doubles the number of expected false alarms.

Rapid Detection of Direct Compound Toxicity and Trailing Detection of Indirect Cell Metabolite Toxicity in a 96-Well Fluidic Culture Device for Cell-Based Screening Environments: Tactics in Six Sigma Quality Control Charts.

Microfluidic screening tools, in vitro, evolve amid varied scientific disciplines. One emergent technique, simultaneously assessing cell toxicity from a primary compound and ensuing cell-generated metabolites (dual-toxicity screening), entails in-line systems having sequentially aligned culture chambers. To explore dual-tox screens, we probe the dissemination of nutrients involving 1-way transport with upstream compound dosing, midstream cascading flows, and downstream cessation. Distribution of flow gives rise to broad concentration ranges of dosing compound (0→ICcompound100) and wide-ranging concentration ranges of generated cell metabolites (0→ICmetabolites100). Innately, single-pass unidirectional flow retains 1st pass informative traits across the network, composed of nine interconnected culture wells, preserving both compound and cell-secreted byproducts as data indicators in each adjacent culture chamber. Thereafter, to assess effective compound hepatotoxicity (0→ECcompound100) and simultaneously classify for cell-metabolite toxicity (0→ECmetabolite100), we reveal utility by analyzing culture viability against ramping exposures of acetaminophen (APAP) and nefazodone (NEF), compounds of hepatic significance. We then discern metabolite generation with an emphasis on amplification across μchannel multiwell sites. Lastly, using conventional cell functions as indicator tools to assess dual toxicity, we investigate a non-drug induced liver injury (non-DILI) compound and DILI compound. The technology is for predictive evaluations of new compound formulations, new chemical entities (NCE), or drugs that have previously failed testing for unresolved reasons.

다소비 식품에 대한 비타민 D 함량 분석 다소비 식품에 대한 비타민 D 함량 분석

In the present study, the vitamin D content of frequently-consumed Korean foods were evaluated to update the National Standard Food Composition Table (NSFCT) that is published by the Korean Rural Development Administration. Total 181 types of frequently-consumed foods including cereals, beans, vegetables, meat and, fish products, drinks, condiments, and mushrooms were analyzed by liquid chromatography-mass spectrometry. The standard materials, BCR-122 and SRM 3235, exhibited recovery rates of 98.40% and 104.17%, respectively. All the analyses were performed under the control line based on the quality control chart for vitamin D2 and D3. Among the analysis of 181 species, vitamin D was detected only in 16 species including mushrooms (Vit D2), eggs and fish products (Vit D3). The vitamin D2 content of mushroom ranged from 0 to 147.96 μg/100 g, with naturally dried Sparassis crispa exhibiting the highest vitamin D2 content. In mushroom of Sparassis crispa, Lentinula edoles, Pleurotus eryngii, and Agaricus bisporus the natural drying showed the superior to vitamin D content compared to those of device drying. However, raw mushrooms except for Auricularia auricula-judae did not contain vitamin D2. The vitamin D3 content of eggs and fish products ranged from 0 to 8.90 μg/100 g (solids part in canned salmon). However, vitamin D was not detected in a majority of the frequently-consumed foods under analysis, such as cereals, beans, vegetables, nut and seeds, ginseng, fruits and seaweed, most fish products and animal foods.

Practices on the Structure and Operation of Quality Control Charts

Practices on the Structure and Operation of Quality Control Charts

Modelling time-series process of an agricultural crop production process by EWMA quality control chart

Control charts are considered to be one of the main tools for analysing and controlling the steps of a product chain process. This research emphasises the control charts' versatility not only in manufacturing, but in taking enough actions for maintaining the quality of the crops with more flexibility. The time-series quality proceedings for crops that utilised the statistical process control (SPC) method serve as a case study in the EWMA control chart. One week of 168 hours of observation data is analysed with a sampling time of one hour. These measurements have been investigated over three models: AR, MA, and ARMA. The results of plotting EWMA for autoregressive (AR), moving average (MA), and ARMA models included eight points at which the process is out of control. It is found that a higher order of AR or MA model shows a lower out-of-control process. This satisfies the concluded point that higher order of the ARMA model means lower out-of-control process points.

Using Bayes Chart to control the qualitative characteristics of newborns

In this study, we will deal with a set of measurable variables (properties) in the quality control charts in this study. There are many qualities whose quality cannot be determined by a single variable, but by a combination of variables that collectively describe the quality of the unit under consideration. Because abnormal children account for a specific number of newly born children, it is necessary to investigate measurement rates for newborn children in order to understand the child's health, which is the foundation of society's health. As a result, the purpose of this study is to create a control chart that uses Bayes statistics, specifically a Bayes chart for qualitatives . We will utilize the values of the final distribution in this chart because it incorporates all of the information accessible during the decision-making process. In this research, a Bayes chart for the (Bnp) number is used to determine the quality of the examined unit. The unit examined in our research represents this newborn child. The research includes how to use the Bayes Theorem to find Bayes estimators for the parameters of the binomial distribution. The study of the control Chart (nP-Chart) uses Bayesian method, including five variables that represent the measurements of newborns, namely (weight, length, head circumference, chest circumference, arm circumference), where these measurements are recorded for (100) cases (child) randomly in Ibn Al-Baladi Hospital in Baghdad for the year (2017) and these observations are divided into (20) samples (set), and the set includes (5) views and these views consist of (52) males , (48) females. We find that predicting the final distribution of any number of observations is the mainstay in building a Bayes chart and making decisions about the progress of the process.

The contribution of statistical processes in the control of technological processes

In this paper, a manufacturing process is analyzed, having as quality characteristic the “height of the screw head”, using analyzes and representative diagrams. Based on this case study, the way to solve these types of problems using the Quality Control Chart module of the WinQSB program, as well as the XLSTAT program is presented.

Guaranteeing acceptable in-control and out-of-control performance of joint X ̅-S control charts with estimated parameters

ABSTRACT Quality control charts are widely used to monitor production processes. To fix the control limits, the distribution of the statistic being controlled must be known. For using and S control charts together, when the parameters are unknown, the mean and standard deviation of the process must be estimated. Errors in these estimates can cause the real performance of the joint charts to be different from that expected by the user, and the in-control and out-of-control ARL may therefore be very different from the theoretical values. To date, only the effect of the estimation errors on the in-control performance of the joint charts has been studied in the literature. In this article, we study jointly this effect on both performance measures, the in-control and out-of-control ARLs for joint charts, and estimate the number of samples needed in Phase I to guarantee a required level in the probability of obtaining joint charts with acceptable in-control and out-of-control performances.

Robust g-type quality control charts for monitoring nonconformities

The g-type quality control charts based on the geometric distribution are commonly used to monitor the number of conforming cases between the two consecutive appearances of nonconformities. The process parameter in these charts is generally estimated based on conventional methods such as the maximum likelihood and minimum variance unbiased estimators, whereas these are very sensitive to outliers and thus could result in severe bias for obtaining the control limits of the charts. To overcome this issue, based on the memoryless property of the geometric distribution along with truncation of a distribution, we develop two robust estimators for the process parameter. Simulation studies and real-data applications illustrate that in terms of the accuracy of the parameter estimation, the relative efficiency, and the run length analysis, the proposed methods outperform existing ones when the data contain outliers.

Establishing an internal quality control method for the stable extraction of nucleic acids of severe acute respiratory syndrome coronavirus 2 and RT-PCR-based detection.

BackgroundSevere acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2), the causative agent of the coronavirus disease 2019 (COVID‐19), is detected using real‐time RT‐PCR. However, there are limitations pertaining to quality control, particularly with respect to establishing quality control measures for extraction of viral nucleic acids. Here, we investigated the quality control measures for the various processes using an extrinsic quality control substance and quality control charts.MethodsAn extrinsic quality control substance was added to the sample, and then, real‐time RT‐PCR was performed. Samples with negative test results and the corresponding data were analyzed; a quality control chart was created and examined.ResultsData analysis and the quality control charts indicated that SARS‐CoV‐2 could be reliably detected using real‐time RT‐PCR, even when different nucleic acid extraction methods were used or when different technicians were employed.ConclusionWith the use of quality control substances, it is possible to achieve quality control throughout the process—from nucleic acid extraction to nucleic acid detection—even upon using varying extraction methods. Further, generating quality control charts would guarantee the stable detection of SARS‐CoV‐2.

Improving management of hypersensitivity reactions: A BC Cancer-Victoria quality improvement initiative.

230 Background: Hypersensitivity reactions (HSR) are a documented, predictable side effect of multiple chemotherapy agents. Reactions negatively affect the patient experience, increase the amount of chair time, nursing and physician resources, may result in the omission of a potentially effective cancer management tool from a patient’s treatment plan and could potentially result in death. BC Cancer is a Health Care Organization with 6 cancer centres across British Columbia, Canada. Guideline(GL)s have been developed at BC Cancer to support clinicians to manage reactions acutely and reduce the risk of reactions with subsequent cycles. A recent audit identified that the GLs were not always being followed at the Victoria Centre. Our goal was to encourage physician and nursing staff to follow GLs, which we hypothesized would result in decreased rates of HSR. Methods: Our aim was to decrease HSR to < 5% of doses delivered within 1 year at BC Cancer-Victoria. We engaged stakeholders (nursing, physicians, pharmacy, clerical staff and administration). Our change ideas improved adherence to GLs by focusing on: physician attendance and documentation, written orders for rescue medication, and rate of infusion of the chemotherapy drug rechallenge. Our interventions included: two physician-education sessions, one nursing education session, daily huddles, pre-printed order development for management of the reaction (PPOA) and prophylaxis for subsequent cycles (PPOB), and a modified clinic flow. All interventions were introduced and underwent modifications through PDSA cycles. Our family of measures were: Outcome: number of reactions, percent of reactions per dose given. Process: percent of PPO use per reaction, physician attendance and notes dictated per reaction. Balancing: physician and nursing satisfaction. We analyzed the data using quality improvement run charts and control charts. Results: After the start of our initiative, our total number of reactions displayed special cause variation, and a shift in the baseline from a mean of 11.27 HSR per month to 7.526. This change was reflected in the percentage of reactions per doses given which fell from 3.1% to 1.9%. Average percentage of dictated notes per reaction increased from 55% to 64%. Physician attendance per reaction also showed special cause variation with the average increasing from 57% to 90%. PPOA and PPOB use both increased over time. Nursing and Physician satisfaction data will also be presented. Conclusions: Our successful initiative has resulted in HSR management which more closely reflects GLs, including increased physician attendance and notes, and clear consistent written orders detailed on PPO A and B. This has led to decreased HSRs at our site, resulting in decreased resource use and increased patient safety and quality. This has provincial implications as there is the potential to spread this initiative to other BC Cancer sites.

New Quality Control Chart to Quickly Detect the Changes of Process Average

New Quality Control Chart to Quickly Detect the Changes of Process Average

Construction X-Bar Chart for Quality Control Using of Robust (MAD) Estimator in Under Consideration Is Outlier with An Application

The presence of outliers in a sample significantly affects the estimated values, which is why the highly efficient Median Absolute Deviation (MAD) method has been used due to of its great robust against the existence of outliers values in the data. In this article, we construct a mean quality control charts using the (MAD) robust. A comparison between the mean control chart based on robust and the mean control chart based on standard deviation to reach a better method, this is through a practical application to the real data (the pressure force on the block). The findings revealed that mean control chart based on (MAD) was more effective than the classical mean control chart based on standard deviation in identifying anomalies from the average production process.

Novel approach for screening milk based on fast and environmentally friendly determination of protein and fat

Milk is very vulnerable to fraud and is generally priced by the protein and fat content, but most analytical alternatives available for quantifying these components are limited in terms of technical aspects or instrument availability. Therefore, a novel, fast, and green analytical alternative for screening milk samples based on the protein and fat content is proposed herein. The procedure relies on the effect of ethanol in milk, which induces protein precipitation (chaotropic effect) and promotes fat extraction. The analytical measurements are based on the precipitated mass (protein content) and the amount of water needed to induce turbidity in the ethanolic extract (fat determination). The amount of water was determined by microtitration of the extract, giving values of (64 ± 5) μL for whole milk (3 % m/v fat) and (104 ± 5) μL for skim milk (1 % m/v fat), whereas the mass of precipitate (from 0.1036 ± 0.0072 to 0.1134 ± 0.0068 g) did not differ significantly for these milk types because the protein content is standardized to ca. 3.0 % m/v. Quality control charts were used for sample screening, achieving specificity and sensitivity rates of 100 % for fat and 100 % and 75 % for protein, respectively. Ethanol (800 μL of 92.8 % v/v solution per determination) was the only reagent needed, demonstrating the environmental friendliness of the approach. The proposed procedure is suitable for fast and cost-effective screening of milk samples, with the aim of detecting non-conformities in the most important quality parameters of bovine milk at different stages of the production chain.

The Ranking Function for Solving the Fuzzy Hotelling T2 Test

A multivariate control chart is measured by many variables that are correlated in production, using the quality characteristics in any product. In this paper, statistical procedures were employed to find the multivariate quality control chart by utilizing fuzzy Hotelling test. The procedure utilizes the triangular membership function to treat the real data, which were collected from Baghdad Soft Drinks Company in Iraq. The quality of production was evaluated by using a new method of the ranking function.

Using the moving average and exponentially weighted moving average with COVID 19

The study was concerned with clarifying the work of the quality control charts and their application in the health field, where the quality control charts for the moving average and the Exponential Weighted moving average were used to clarify the number of people infected with the Corona virus (COVID 19) for the month of April of the year 2020 and compare it with the number of people infected with the virus for the month of April of 2021, where these charts were drawn and it was found The number of injured in April 2021 is more than in April 2020, and the drawings showed the upper and lower limits of the level of injury in addition to the average number of injuries . The graphics showed the number of days out of control in the animations of moving averages and Exponential Weighted moving averages, and the study showed citizens' lack of interest in preventive methods that reduce infection with this virus and their lack of interest in urban health and not taking the vaccine for this virus in a timely manner.