Statistical Quality Control Approach Research Articles

ANALISA PARAMETER MESIN INJECTION MOLDING TERHADAP CACAT PRODUK HANDLE GRIP MENGGUNAKAN METODE TAGUCHI

One of the causes of losses from the company is the presence of product defects in the production process, the cause of product defects that occur is suboptimal machine parameters. This study aims to analyze machine parameters so that product defects are reduced or minimal. In this study, the largest product defects were found, significant product defect factors, optimal combination of machine parameters and minimizing the defects of the company's products using the Taguchi Method with a Statistical Quality Control approach. The research data comes from the defects of the PT.X. Handle Grip machine product, from these data, it will then be processed using the Statistical Quality Control method, taguchi method, analysis of variants (ANOVA) and repair control. From statistical quality control analysis data, the biggest product defects can be obtained, namely heavy product defects of 4.74%. The analysis of the taguchi method can be obtained the best combination of parameters, namely an injection pressure of 52 bar, a cooling duration of 5 s and an injection temperature of 195oC. While the most influential factors on product defects based on ANOVA are injection pressure, injection temperature and finally cooling duration because F hit > F table with successive results of 403 > 3.04, 61 > 3.04 and 27 > 3.04. The results of the repair control were obtained better results, before the study of severe product defects of 4.74% dropped to 0.08%.

A study of performance measures and quality management system in small and medium enterprises in India

PurposeThe paper aims to identify causality amongst small and medium enterprises' (SMEs') performance indicators, propose an integrated index of business performance and quality practices and investigate the effect of quality management system (QMS) on business performance.Design/methodology/approachThis study proposes a two-stage approach. In the first stage, the authors gathered responses with a questionnaire on variables affecting business performance and identified dimensions using exploratory and confirmatory factor analyses. Path analysis was carried out to identify causality between the dimensions. In the next stage, the validation of stage 1 findings was carried out to substantiate the proposition that QMS affects performance. Questionnaire survey and in-depth interviews were conducted during the second stage. The analysis was done following the case study protocol – within- and cross-case analysis and validated with supportive and conflicting literature.FindingsThe results show that the employee – dimension is crucial to process effectiveness, customer satisfaction and finances. Customer satisfaction results from employee satisfaction and processes. QMS impacts processes and employee performance, thus establishes the causality between business performance factors and QMS. QMS impacts finances in the short run but yields long-term benefits and is dependent on two factors – degree of knowledge of quality practices and its application in business.Originality/valueThis study reveals the performance dimensions of SMEs, their causality and the impact of QMS on performance. It suggests a shift from traditional approaches, correcting defects using statistical quality control approaches, to a sustainable growth path – a long-term approach. This study puts forward two associated indices – the business performance and the knowledge of quality practices and its application index.

Application of Statistical Quality Control in Monitoring the Production, Packaging and Marketing Process of Sachet Water

Statistical process control is a technique employed to enhance the quality and productivity of processes and the distribution or marketing of its products. Sachet water is a product that has become popular and is being used as a replacement for lack of potable water. It is an alternative that is readily available, affordable but with questions about its purity, production and marketing processes. The objective of this study is to apply statistical control charts in monitoring the production, packaging and distribution or marketing processes of sachet water in Nigeria. This paper employed statistical quality control approach to monitor process stability in a Table Water manufacturing company. Quality control tools such as p-chart, u-chart, X-bar and R charts as well as process capability chart were use to observed field data obtained from the sachet water manufacturing company on important processes of sachet water production and marketing for 30 working days. This was done to check if the processes were in control or out of control and to verify the capability of the marketing process of the product meeting preset specifications. With this, the statistical control charts suitable for the processes were constructed using package “qcc” in R software version 3.6.1. The results from p-chart and u-chart showed that the production and packaging process of the product is not in control and hence the need for further investigations and corrective measures to prevent variability in the process and thus allowing improvement in the quality of the product. Also, the results from X-bar and R charts showed that the marking process was in statistical process control in respects of the product sales recorded by the four independent marketers, with no assignable cause of variation. It also revealed that, the product marketing process has low capability of successfully attending the preset specification limits in respect of the product sales and hence generating low profit for the company.

Thread Quality Control in High-Speed Tapping Cycles

Thread quality control is becoming a widespread necessity in manufacturing to guarantee the geometry of the resulting screws on the workpiece due to the high industrial costs. Besides, the industrial inspection is manual provoking high rates of manufacturing delays. Therefore, the aim of this paper consists of developing a statistical quality control approach acquiring the data (torque signal) coming from the spindle drive for assessing thread quality using different coatings. The system shows a red light when the tap wear is critical before machining in unacceptable screw threads. Therefore, the application could reduce these high industrial costs because it can work self-governance.

Energy diagnosis of variable refrigerant flow (VRF) systems: Data mining technique and statistical quality control approach

The variable refrigerant flow (VRF) system has changeable energy performance due to shifty meteorological conditions and unstable internal factors (e.g. complex control strategies, various part load ratios). It is difficult to determine the causes of drastic energy consumption variations are normal factors or faults. This study therefore proposed an energy diagnosis method for VRF systems based on data mining techniques and statistical quality control approaches. The correlation analysis is employed to select key factors and the DBSCAN method is used to remove the transient data and outliers. Besides, the system power consumption is predicted using the SVR algorithm. The EWMA control chart is used to diagnosis the system energy performance and it is comparatively analyzed with the x¯-R and CUSUM control charts. The reliability of the proposed method is verified by diagnosing the system energy in refrigerant undercharge and overcharge conditions, respectively. Results show that the SVR method is reliable to predict the system energy consumption with a R2 value of 0.9974. In addition, the EWMA control chart can significantly improve the VRF energy diagnosis performance compared to the x¯-R and CUSUM control charts. It achieves high correctly diagnosis ratios at both refrigerant undercharge and overcharge conditions.

Planning Risk-Based SQC Schedules for Bracketed Operation of Continuous Production Analyzers.

To minimize patient risk, "bracketed" statistical quality control (SQC) is recommended in the new CLSI guidelines for SQC (C24-Ed4). Bracketed SQC requires that a QC event both precedes and follows (brackets) a group of patient samples. In optimizing a QC schedule, the frequency of QC or run size becomes an important planning consideration to maintain quality and also facilitate responsive reporting of results from continuous operation of high production analytic systems. Different plans for optimizing a bracketed SQC schedule were investigated on the basis of Parvin's model for patient risk and CLSI C24-Ed4's recommendations for establishing QC schedules. A Sigma-metric run size nomogram was used to evaluate different QC schedules for processes of different sigma performance. For high Sigma performance, an effective SQC approach is to employ a multistage QC procedure utilizing a "startup" design at the beginning of production and a "monitor" design periodically throughout production. Example QC schedules are illustrated for applications with measurement procedures having 6-σ, 5-σ, and 4-σ performance. Continuous production analyzers that demonstrate high σ performance can be effectively controlled with multistage SQC designs that employ a startup QC event followed by periodic monitoring or bracketing QC events. Such designs can be optimized to minimize the risk of harm to patients.

Towards reproducible MRM based biomarker discovery using dried blood spots

There is an increasing interest in the use of dried blood spot (DBS) sampling and multiple reaction monitoring in proteomics. Although several groups have explored the utility of DBS by focusing on protein detection, the reproducibility of the approach and whether it can be used for biomarker discovery in high throughput studies is yet to be determined. We assessed the reproducibility of multiplexed targeted protein measurements in DBS compared to serum. Eighty-two medium to high abundance proteins were monitored in a number of technical and biological replicates. Importantly, as part of the data analysis, several statistical quality control approaches were evaluated to detect inaccurate transitions. After implementing statistical quality control measures, the median CV on the original scale for all detected peptides in DBS was 13.2% and in Serum 8.8%. We also found a strong correlation (r = 0.72) between relative peptide abundance measured in DBS and serum. The combination of minimally invasive sample collection with a highly specific and sensitive mass spectrometry (MS) technique allows for targeted quantification of multiple proteins in a single MS run. This approach has the potential to fundamentally change clinical proteomics and personalized medicine by facilitating large-scale studies.

통계적 품질 관리도에 기초한 교량의 응답 보정

본 논문은 통계적 품질관리 기법을 활용하여 건전한 교량의 고유진동수비와 응답비 관리범위를 찾고, 그에 준거한 응답보정 방법을 제시하였다. 이를 위해 본 연구는 재하 시험 및 해석으로부터 도출된 구조형식별 정 동적 응답 특성에 대한 기술통계분석, 정규성 검정, 분산분석 등을 수행하였다. 제안된 방법은 실제 진단 사례에 근거하고 통계적 품질관리 기법을 활용함으로써 응답보정 및 내하력 평가를 위한 과학적 기준과 체계적 절차를 제공할 수 있을 것으로 기대한다. This paper presents the response calibration method based on quality control range, which is established from the concept and method of statistical quality control for natural frequency ratio and response ratio. To this end, statistical analysis including descriptive statistics analysis, normality test, ANOVA were performed for response characteristics obtained from loading tests and structural analysis for more than hundred and thirty well-conditioned bridges. Suggested method is based on real structural integrity evaluation case studies and statistical quality control approach, in this respect it is expected to provide scientific criteria and systematic procedure for response calibration and load carrying capacity assessment.

Statistical Quality Control Approaches to Network Intrusion Detection

In the study of network intrusion, much attention has been drawn to on-time detection of intrusion to safeguard public and private interest and to capture the law-breakers. Even though various methods have been found in literature, some situations warrant us to determine intrusions of network in real-time to prevent further undue harm to the computer network as and when they occur. This approach helps detect the intrusion and has a greater potential to apprehend the law-breaker. The purpose of this article is to formulate a method to this effect that is based on the statistical quality control techniques widely used in the manufacturing and production processes.

On-line prediction of quality and shear strength of spacer pad welds of nuclear fuel pins by applying neural network analysis of acoustic emission signals

In the present study, the development of an acoustic emission technique (AET) based methodology is reported for online prediction of quality and shear strength of spacer pad welds of nuclear fuel pins of pressurised heavy water reactors (PHWRs). The quality evaluation of spacer pad welds was made by classification of different weld categories using cluster analysis and artificial neural network (ANN) study of acoustic emission signals generated during welding. The ANN approach was also effective in arriving at the quantitative estimation of percentage correct classification between any two classes. For assessment of shear strength of individual coins of spacer pad welds by ANN, the properties of basic sigmoidal function were exploited and this could predict the strength of each coin with an accuracy of 97%. The results assume significance because instrumentation methodology is suitable for online application and complement the currently followed statistical quality control approaches for spacer pad weld assessment.

Application of intrawell testing of RCRA groundwater monitoring data when no upgradient well exists.

A statistical quality control approach to detect changes in groundwater quality from a regulated waste unit is described. The approach applies the combined Shewhart-CUSUM control chart methodology for intrawell comparison of analyte concentrations over time and does not require an upgradient well. A case study from the U.S. Department of Energy's Hanford Site is used for illustration purposes. This method is broadly applicable in groundwater monitoring programs where there is no clearly defined upgradient location, the groundwater flow rate is exceptionally slow, or where a high degree of spatial variability exists in parameter concentrations. This study also indicates that the use of the Data Quality Objectives (DQO) process can assist in designing an efficient and cost-effective groundwater monitoring plan to achieve the optimum goal of both low false positive and low false negative rates (high power).

An economic model for clinical quality control

With increased focus on reducing costs in the healthcare industry, the economic aspects of quality control for clinical laboratories must be addressed. In order to evaluate the economic performance of statistical quality control approaches used in the clinical setting, an economic model is developed. Although the economic model is applied specifically to the clinical laboratory in this research, it is easily generalized for use in a wide variety of industry applications. Use of the economic model is illustrated through the comparison of traditional approaches to clinical quality control. Recommendations concerning the performance of the traditional approaches to clinical quality control are provided.

An Approach to the Application of Statistical Quality Control Techniques in Engineering Courses

AbstractThis paper presents an approach to monitoring, controlling and improving the teaching process and learning outcomes in engineering courses using Statistical Quality Control (SQC). The ABET 2000 criteria and their impact on the control and improvement of teaching and learning is discussed, followed by a brief illustration of the university system that these criteria foster. It is argued that in meeting ABET 2000 goals, an engineering faculty can apply the same principles and techniques they have taught for decades; namely systems engineering and quality control. Subsequently, an SQC approach for engineering courses and their common constituents: lectures, laboratories and tutorials, is suggested. Four illustrative case studies of undergraduate mechanical and industrial engineering courses are presented.

Quality control and social processes: a case for acceptance sampling

The “at the source” emphasis of total quality management (TQM) has reduced the reliance on post‐production statistical quality control approaches such as acceptance sampling. In cases where it is appropriate more proactive approaches such as statistical process control have improved productivity in manufacturing environments. For social processes where the inputs are ill‐defined and the outputs are difficult to measure, traditional quality control approaches have rarely been applied. Addresses the problem of monitoring use of illegal drugs, a critical social problem. Because the inputs, the use of drugs, are not easy to document and the process which results in an individual’s decision to use drugs is too complex to model, one must rely on the detection of drugs as a measurement of drug abuse. The behaviour of interest is the detection of illegal drug use through urine testing. The technique for monitoring this behaviour in a population of interest is single‐attribute, Bayesian acceptance sampling. Applies a partial drug‐testing methodology based on single‐attribute acceptance sampling to a population of probationers in Madison County, Illinois, USA. The approach offers probation offices with a lower cost approach to monitoring drug use among populations of known drug users. The use of acceptance sampling allows Madison County to reduce the total cost of testing by reducing the total amount of testing that must be done to monitor use of drugs among their probation populations.