Assessing the Awareness and Usage of Quality Control Tools with Emphasis to Statistical Process Control (SPC) in Ethiopian Manufacturing Industries

Introduction: The present work was devoted to assess the awareness and usage of quality control tools with the emphasis on statistical process control in Ethiopian manufacturing industries. Semi structured questionnaire has been employed to executive and technical managers of manufacturing industries of various size and specialism across the country. Stratified random sample method by region was used to select sample industries for the study. The samples used for this study are industries mainly from Oromiya, Addis Ababa, Tigray, Amara, SNNP and Diredawa regions proportional to their size of the available industries. Methods: Exploratory method and descriptive statistics was used for data analysis. Available documents and reports related to quality control policy of the selected companies were investigated. Results and Discussions: The number of manufacturing industries involved in this study was 44. Of the sampled manufacturing industries about 60% are from Oromiya and Addis Ababa regions. It has been reported that 100% of the respondents said that the importance of quality control tools is very important to their organizations’ productivity and quality improvement (Figure 3). Quality control professionals were also asked the extent to which quality control system is working in their industry and majority of the respondents (45%) have indicated that quality control system is working to some extent in their respective industries (Figure 18). Conclusions and Recommendations: Most of the quality department of the industries did not fully recognize the importance of statistical process control as quality control tools. This is mainly due to lack of awareness and motivation of the top managements, shortage of man power in the area, and others together would make it difficult to apply quality control tools in their organization. In general, the industries in Ethiopia are deficient in vigor and found to be stagnant hence less exposed to a highly competitive market and don’t adopt the latest quality control techniques in order to gain knowledge about systems to improve quality and operational performance. We conclude that quality management system has to be established as an independent entity with a real power and hence the quality control department which is responsible for quality can make an irreversible decision with respect to quality of any given product. Moreover, the concerned bodies (government and ministry of industries) should give attention and work together with universities to ensure how these statistical process control techniques could be incorporated in a curriculum of the universities at higher levels in degree and masters programs. Furthermore, different trainings which could improve quality and efficiency of their respective management system should be given as short and long term to the employees including top and middle managers found in various industries relevant to their process.

基于Statistical Process Control风险等级判定及神经网络模型构建珠海市传染病指数

目的建立珠海市传染病指数预报模型,为传染病风险预测预报提供思路。方法利用统计过程控制(SPC)的控制下限、中线和控制上限划分全市2014—2017年以周次为时间计量单位的流感样病例比例、手足口病及其他感染性腹泻发病率的风险等级(布雷图指数采用5、10、20判定)。运用长短时记忆神经网络模型(LSTM)和自回归移动平均模型(ARIMA)对2018年15~19周数据进行预测。计算传染病指数并将预测值与实际值对比进而评估预测一致性。结果珠海市手足口病发病率LSTM模型中,测试集MSE为9.0441,RMSE为3.0073,训练集MSE为1.1812,RMSE为1.0868。其余模型在训练集和测试集均表现良好,没有出现过拟合现象。风险指数等级预测与实际值对比,预测一致率为96.0%。结论利用SPC划分风险等级,运用LSTM等构建传染病指数预测模型可行。

A novel Q-based online model updating strategy and its application in statistical process control for rubber mixing

To overcome the large time-delay in measuring the hardness of mixed rubber, rheological parameters were used to predict the hardness. A novel Q-based model updating strategy was proposed as a universal platform to track time-varying properties. Using a few selected support samples to update the model, the strategy could dramatically save the storage cost and overcome the adverse influence of low signal-to-noise ratio samples. Moreover, it could be applied to any statistical process monitoring system without drastic changes to them, which is practical for industrial practices. As examples, the Q-based strategy was integrated with three popular algorithms(partial least squares(PLS), recursive PLS(RPLS), and kernel PLS(KPLS)) to form novel regression ones, QPLS, QRPLS and QKPLS, respectively. The applications for predicting mixed rubber hardness on a large-scale tire plant in east China prove the theoretical considerations.

Multivariate Statistical Process Monitoring and Control: Recent Developments and Applications to Chemical Industry

Multivariate statistical process monitoring and control (MSPM& C) methods for chemical process monitoring with statistical projection techniques such as principal component analysis (PCA) and partial least squares (PLS) are surveyed in this paper,The four-step procedure of performing MSPM &C for chemical process ,modeling of processes ,detecting abnormal events or faults,identifying the variable(s) responible for the faults and diagnosing the source cause for the abnormal behavior,is analyzed,Several main research directions of MSPM&C reported in the literature are discussed,such as multi-way principal component analysis (MPCA) for batch process ,statistical monitoring and control for nonlinear process,dynamic PCA and dynamic PLS,and on -line quality control by infer-ential models,Industrial applications of MSPM&C to several typical chemical processes ,such as chemical reactor,distillation column,polymeriztion process ,petroleum refinery units,are summarized,Finally,some concluding remarks and future considerations are made.

Integrated Statistical and Engineering Process Control Based on Smooth Transition Autoregressive Model

Traditional studies on integrated statistical process control and engineering process control (SPC-EPC) are based on linear autoregressive integrated moving average (ARIMA) time series models to describe the dynamic noise of the system.However,linear models sometimes are unable to model complex nonlinear autocorrelation.To solve this problem,this paper presents an integrated SPC-EPC method based on smooth transition autoregressive (STAR) time series model,and builds a minimum mean squared error (MMSE) controller as well as an integrated SPC-EPC control system.The performance of this method for checking the trend and sustained shift is analyzed.The simulation results indicate that this integrated SPC-EPC control method based on STAR model is effective in controlling complex nonlinear systems.

Study on Quality and Safety Trust Early Warning of Dairy Products Based on Statistical Process Control

In order to solve such problems as lack of dynamic evaluation system in evaluation of quality and safety trust of dairy products,and weak awareness of prevention,it is necessary to introduce the statistical process control into the quality and safety trust evaluation system of dairy products,and establish quality and safety trust early warning model for dairy products,so as to determine the control limit of control chart and carry out early warning according to eight criteria. According to the empirical results,the statistical process control is helpful for finding the hidden process risks and providing the necessary basis for enterprises taking positive measures to raise the confidence of consumers.

Application of Statistical Process Control for Setting Action Thresholds as Quality Assurance of Dose Verifications in External Beam Radiotherapy

Purpose: To test the concept of Statistical Process Control (SPC) as a Quality Assurance (QA) procedure for dose verifications in external beam radiation therapy in conventional and 3D Conformal Radiotherapy (3D-CRT) treatment of cervical cancer. Materials and Methods: A study of QA verification of target doses of 198 cervical cancer patients undergoing External Beam Radiotherapy (EBRT) treatments at two different cancer treatment centers in Kenya was conducted. The target doses were determined from measured entrance doses by the diode in vivo dosimetry. Process Behavior Charts (PBC) developed by SPC were applied for setting Action Thresholds (AT) on the target doses. The AT set was then proposed as QA limits for acceptance or rejection of verified target doses overtime of the EBRT process. Result and Discussion: Target doses for the 198 patients were calculated and SPC applied to test whether the action limits set by the Process Behavior Charts could be applied as QA for verified doses in EBRT. Results for the two sub-groups of n = 3 and n = 4 that were tested produced action thresholds which are within clinical dose specifications for both conventional AP/PA and 3D-CRT EBRT treatment techniques for cervical cancer. Conclusion: Action thresholds set by SPC were within the clinical dose specification of ±5% uncertainty for both conventional AP/PA and 3D-CRT EBRT treatment techniques for cervical cancer. So the concept of SPC could be applied in setting QA action limits for dose verifications in EBRT.

Statistical Control and Investigation of Capability of Process and Machine in Wire Cut Edm Process of Gas Turbine Blade Airfoil Tip

Statistical process control (spc), as one of the quality devices, can be help manufacturers improve the quality of their products in today’s competitive world. This research examines the statistical method of wire-cut electric discharge machining (WEDM) process of the turbine blade airfoil tip for control and consistency of the process. For this purpose, the standard deviation control chart, S, and the average data, , which are applied for identifying the acquired factors, have been used. Next, regarding the plan features, the manufacturing process is assessed to determine whether the products meet quality and the customer requirements or not. Therefore, the coefficients Cpk is applied which indicate the capability of the manufacturing process. Then, in order to produce high quality blades within the tolerance range, the capability of WEDM machine is examined, using coefficients CM. Finally, it is shown that in order to produce the desired product, the process can be controlled and fixed by using the statistical process control devices and inspecting the standard deviation of data and investigation of capability of process and machine.

Statistical Process Control in Flexible Manufacturing Systems

The statistical process control techniques used in flexible manufacturing systems arestudied in this paper.Control charts which can be used in the low volume production are pro-posed.The automatic recognizer of unnatural patterns for the control chart by using back-propagation neural network is also presented.

New Method for Multivariate Statistical Process Monitoring

A new method using discriminant analysis and control charts is proposed for monitoring multivariate process operations more reliably.Fisher discriminant analysis （FDA） is used to derive a feature discriminant direction （FDD） between each normal and fault operations,and each FDD thus decided constructs the feature space of each fault operation.Individuals control charts （XmR charts） are used to monitor multivariate processes using the process data projected onto feature spaces.Upper control limit （UCL） and lower control limit （LCL） on each feature space from normal process operation are calculated for XmR charts,and are used to distinguish fault from normal.A variation trend on an XmR chart reveals the type of relevant fault operation.Applications to Tennessee Eastman simulation processes show that this proposed method can result in better monitoring performance than principal component analysis （PCA）-based methods and can better identify step type faults on XmR charts.

An Integrated Use of Advanced T2 Statistics and Neural Network and Genetic Algorithm in Monitoring Process Disturbance

Integrated use of statistical process control (SPC) and engineering process control (EPC) has better performance than that by solely using SPC or EPC. But integrated scheme has resulted in the problem of “Window of Opportunity” and autocorrelation. In this paper, advanced T2 statistics model and neural networks scheme are combined to solve the above problems: use T2 statistics technique to solve the problem of autocorrelation;adopt neural networks technique to solve the problem of “Window of Opportunity” and identification of disturbance causes. At the same time, regarding the shortcoming of neural network technique that its algorithm has a low speed of convergence and it is usually plunged into local optimum easily. Genetic algorithm was proposed to train samples in this paper. Results of the simulation ex-periments show that this method can detect the process disturbance quickly and accurately as well as identify the dis-turbance type.

基于统计过程控制(SPC)的烟草制丝工艺设备自动监测系统

以保障烟草制丝工艺质量为目的,该文设计基于统计过程控制(SPC)的烟草制丝工艺设备自动监测系统。获得制丝工序信息特征信息,制定工序级生产计划和质量规划,在烟草制丝工艺设备运行时,采集其现场数据,通过SPC统计获得监测结果,并将该结果反馈到生产过程监测管理模块,获得当前烟草制丝工艺设备产能,经过运行烟草制丝工艺设备自动监测系统,实现烟草制丝工艺设备自动化监控和管理。实验表明,该系统运行稳定,可有效实现烟草制丝工艺设备自动监测,可实现自动化、高效率、高精度监测,从而提高烟草制丝的产量和质量。

VPLS Based Quality and Cost Control for Tennessee Eastman Process

Product quality and operation cost control obtain increasing emphases in modern chemical system engineering. To improve the fault detection power of the partial least square (PLS) method for quality control, a new QRPV statistic is proposed in terms of the VP (variable importance in projection) indices of monitored process variables, which is significantly advanced over and different from the conventional Q statistic. QRPV is calculated only by the residuals of the remarkable process variables (RPVs). Therefore, it is the dominant relation between quality and RPV not all process variables (as in the case of the conventional PLS) that is monitored by this new VP-PLS (VPLS) method. The combination of QRPV and T2 statistics is applied to the quality and cost control of the Tennessee Eastman (TE) process, and weak faults can be detected as quickly as possible. Consequently, the product quality of TE process is guaranteed and operation costs are reduced.

Monitoring Saccharification Process in Brewery Industry Using Quality Control Charts

The aim of this study was to establish a control system for saccharification process using quality control charts. To achieve this goal, temperature, pH and brix were measured at 12 minutes intervals for 15 consecutive batches which took 2 hours each. The time variations for three process parameters were assessed to establish a good understanding of the saccharification process. The temperature varied between 58℃ and 62℃ while the pH decreased slowly due to oxidation, values of which varied between 5.7 and 5.0. Brix values increased linearly with time. The initial and final values of the three parameters varied from one batch to another. Of the three parameters, brix was not well represented on the quality control charts due to wide difference between initial and final values during saccharification. The final brix values varied between batches, from 10.6% to 11.6%. The control charts used in this study were X-bar and Range charts. The rules for interpreting control charts were implemented for both X-bar and R charts, results of which showed that the process was out of control, although some rules were not violated due to little number of batches studied. The values of for temperature and pH data (2.27℃ and 0.35, respectively) were lower compared to brix data (11.2%). The corresponding values of span between control limits, SPx and SPR for temperature and pH were also comparatively lower than those established from brix data. Due to larger values of for brix measurements, the corresponding control charts for brix were insensitive in identifying out-of-control points during saccharification process.

基于改进SPC的地铁监测数据分析方法

为对地铁监测数据进行科学分析,在传统“双控”指标分析方法的基础上,引入并改进统计过程控制(statistics process control,SPC)技术,建立新的分析方法。参考基础性国标GB 50068—2018《建筑结构可靠性设计统一标准》中的相关规定,明确SPC技术的理论依据3σ准则对应的假设检验显著性水平α=0.27%明显偏小。提出应该以α=5%建立适用于土木工程领域的分析准则,即2σ非连续准则;并将新准则与SPC结合,形成适用于地铁工程建设的监测数据分析方法。最后,基于工程实例验证了新方法的可行性。经研究可以明确:1)引入SPC可以充分利用监测数据,分析出传统分析方法不易识别出的数据变化特征;2)改进SPC技术更加满足地铁工程建设领域的基本要求,对数据的分析判定也更加严格。

基于SPC的高炉炉况异常检测研究

针对高炉冶炼过程复杂多变,影响高炉炉况的因素众多且运行过程复杂,为保证高炉炉况稳定顺行,开发了一种基于主元分析(PCA)和统计过程控制(SPC)的高炉炉况异常检测模型。由于高炉运行参数具有高耦合和非高斯的特点,该模型首先采用主元分析算法对高炉实际生产的历史离线数据进行聚类分析,然后应用基于T 2统计量的多元控制图和单值控制图对聚类后的新变量和相关参数进行监测,从而达到监测高炉出现异常炉况的目的。该模型可以实时监测高炉炉况的异常波动,其中PCA算法将高炉本身的高维数据降到低维,大幅去除数据中的噪声和不重要特征,在实际生产和应用中,节省了大量的成本和时间。选取马钢某高炉炼铁过程为应用场景,结合数据特点调整和改进算法,通过历史数据模拟和实时在线运行验证模型的可靠性和算法的有效性,同时也对指导高炉实际操作技术做出了一定的贡献。

小批量多品种模式下SPC监控系统的设计与运用

文章以铁路交通行业某小批量多品种产品加工工厂为研究对象。在收集实际生产过程数据的基础上,分析统计过程控制(Statistical Process Control,SPC)监控系统的结构与功能,建立了适用于小批量多品种产品的SPC监控系统,并将其运用于实际生产过程。结果表明,在小批量多品种的生产模式下可以进行SPC实时监控分析,对生产过程中可能存在的异常提出预警,从而提升生产过程的稳定性。

质量工具SPC在化工行业的应用

对统计过程控制进行介绍。将统计过程控制工具应用于亚硫酸钠浓度异常波动原因分析,对亚硫酸钠浓度进行精细化控制,落实改进措施,解决问题。推广SPC工具应用范围,可以系统实现精细化过程控制。

SPC在SMT质量控制中的应用

介绍了统计过程控制(SPC)的核心工具之控制图的分类及其核心技术,详细地阐述了控制图的选取流程。首先,分析了不同工序对表面组装技术(SMT)生产质量的影响;然后,通过对缺陷的分析判断,选取了对SMT加工质量影响最大的焊膏印刷工序作为改进对象;最后,针对焊膏厚度的计量值数据特性,选择了X-R控制图。通过采集数据、绘制控制图和数据分析,既可以及时发现工艺异常并进行纠正,又可以排除非工艺原因造成的异常,对于提高工艺稳定性和产品质量有着非常重要的意义。

统计过程控制(SPC)技术在挤出过程中的应用

简要介绍了统计过程控制(SPC)的原理,并论述了将其引入塑料加工行业的必要性。在建立统计过程控制模型后,开发了应用于塑料挤出过程的控制系统。通过类似实际生产过程的模拟试验,介绍了实施SPC的工作程序及实施SPC之后的效果。为提高过程的可靠性,在过程数据的统计分析和过程控制图的绘制时,采用VISUALBASIC6 0和SQLserver2000分别对用户界面和后台数据处理程序进行设计。