Control Chart Techniques Research Articles

Robot-Assisted Radical Prostatectomy (RARP) Trifecta Learning Curve for Surgeons with Previous Experience in Laparoscopy.

Background and Objectives: Robot-assisted radical prostatectomy (RARP) is a complex surgery with a steep learning curve (LC). No clear evidence exists for how previous laparoscopic experience affects the RARP LC. We report the LC of three surgeons with vast experience in laparoscopy (more than 400 procedures), analyzing the results of functional and oncological outcomes under the "Trifecta" concept (defined as the achievement of continence, potency, and oncological control free of biochemical recurrence). Materials and Methods: The surgical experience of the three surgeons from September 2021 to December 2022, involving 146 RARP consecutive patients in a single institution center, was evaluated prospectively. Erectile disfunction patients were excluded. ANOVA and chi-square test were used to compare the distribution of variables between the three surgeons. LC analysis was performed using the cumulative sum control chart (CUSUM) technique to achieve trifecta. Results: The median age was 65.42 (±7.34); the clinical stage were T1c (68%) and T2a (32%); the biopsy grades were ISUP 1 (15.9%), ISUP 2 (47.98), and ≥ISUP 3 (35%). The median surgical time was 132.8 (±32.8), and the mean intraoperative bleeding was 186 cc (±115). Complications included the following: Clavien-Dindo I 8/146 (5.47%); II 9/146 (6.16%); and III 3/146 (2.05%). Positive margins were reported in 44/146 (30.13%). The PSA of 145/146 patients (99%) at 6 months was below 0.08. Early continence was achieved in 101/146 (69.17%), 6-month continence 126/146 (86%), early potency 51/146 (34.9%), and 6-month potency 65/146 (44%). Surgeons "a", "b", and "c" performed 50, 47, and 49 cases, respectively. After CUSUM analysis, the "Trifecta" LC peak was achieved at case 19 in surgeon "a", 21 in surgeon "b", and 20 in surgeon "c". Conclusions: RARP LC to accomplish "Trifecta" can be significantly reduced in surgeons with previous experience in laparoscopy and be achieved at around 20 cases.

Diagram Kendali Triple Homogeneously Weighted Moving Average dan Penerapannya pada Proses Plating Aksesori Mobil PT. XYZ

Abstract. Quality control is carried out to maintain or improve product quality to meet consumer satisfaction. One of the methods that can be used is control chart. Triple Homogeneously Weighted Moving Average (THWMA) is one of the control chart techniques that can be used in quality control. The Triple Homogeneosly Weighted Moving Average control chart is a control chart for efficient monitoring of the process average under zero state and steady state which is very sensitive in identifying small and continuous changes in the process effectively and using three times sample information. THWMA control is considered superior to the previously existing Homogeneosly Weighted Moving Average (HWMA) and Double Homogeneosly Weighted Moving Average (DHWMA) control charts and from other competitors. In our thesis we will apply a THWMA control chart to monitor the plating process of PT. XYZ. The results obtained in Phase I of the in control chart, so that the parameter values ​​used in Phase II are of 10.691 dan of 0.67. Phase II control chart using K = 2.994 and λ = 0.25, it is found that the THWMA control chart is controlled.
 Abstract. Quality control is carried out to maintain or improve product quality to meet consumer satisfaction. One of the methods that can be used is control chart. Triple Homogeneously Weighted Moving Average (THWMA) is one of the control chart techniques that can be used in quality control. The Triple Homogeneosly Weighted Moving Average control chart is a control chart for efficient monitoring of the process average under zero state and steady state which is very sensitive in identifying small and continuous changes in the process effectively and using three times sample information. THWMA control is considered superior to the previously existing Homogeneosly Weighted Moving Average (HWMA) and Double Homogeneosly Weighted Moving Average (DHWMA) control charts and from other competitors. In our thesis we will apply a THWMA control chart to monitor the plating process of PT. XYZ. The results obtained in Phase I of the in control chart, so that the parameter values ​​used in Phase II are of 10.691 dan of 0.67. Phase II control chart using K = 2.994 and λ = 0.25, it is found that the THWMA control chart is controlled.

A Review of Control Charts and Exploring Their Utility for Regional Environmental Monitoring Programs

Industrial control charts are used in manufacturing to quickly and robustly indicate the status of production and to prompt any necessary corrective actions. The library of tools available for these tasks has grown over time and many have been used in other disciplines with similar objectives, including environmental monitoring. While the utility of control charts in environmental monitoring has been recognized, and the tools have already been used in many individual studies, they may be underutilized in some types of programs. For example, control charts may be especially useful for reporting and evaluating data from regional surveillance monitoring programs, but they are not yet routinely used. The purpose of this study was to promote the use of control charts in regional environmental monitoring by surveying the literature for control charting techniques suitable for the various types of data available from large programs measuring multiple indicators at multiple locations across various physical environments. Example datasets were obtained for Canada’s Oil Sands Region, including water quality, air quality, facility production and performance, and bird communities, and were analyzed using univariate (e.g., x-bar) and multivariate (e.g., Hotelling’s T2) control charts. The control charts indicated multiple instances of unexpected observations and highlighted subtle patterns in all of the example data. While control charts are not uniquely able to identify potentially relevant patterns in data and can be challenging to apply in some monitoring analyses, this work emphasizes the broad utility of the tools for straightforwardly presenting the results from standardized and routine surveillance monitoring.

Alternative Design of One-Sided Shewhart Control Charts for the Multivariate Coefficient of Variation

The control charting technique is an approach to quality control and was implemented in various industries. There are many control charts, where the coefficient of variation control chart was one of the common charts and greatly used in Statistical Process Control. Since most processes are multivariate, the multivariate coefficient of variation charts has received great attention in the past few years. However, the existing multivariate coefficient of variation control charts was evaluated in terms of the average run length criterion, which may misinterpret the actual performance of the charts. This paper designs an alternative for the Shewhart multivariate coefficient of variation chart by considering the median run length and expected median run-length criteria to circumvent this problem. The research on the multivariate coefficient of variation chart is very limited in the existing literature by considering the median run length criterion. This proposed chart in this paper can minimize this research gap. The formulas and algorithms of the proposed chart are presented. The outputs of the proposed charts are shown by examining the different upward and downward process shifts. Additionally, the sample sizes, the process shifts, and the variation of the run-length distribution are investigated for their effects on the proposed chart. The findings reveal that the run-length distribution’s variation is inversely proportional to the shift size. Furthermore, it shows that the variation decreases if the shift size increases.

Process Development for Printed Copper With Surface Mount Devices on Inkjet Metallization

Abstract Printed electronics is a fastest growing and emerging technology that have shown much potential in several industries including automotive, wearables, healthcare, and aerospace. Its applications can be found not only in flexible but also in large area electronics. The technology provides an effective and convenient method to additively deposit conductive and insulating materials on any type of substrate. Despite its status, it is not without its challenges. Inkjet technology has gained much attention due to its low cost, low-material consumption, and capability for mass manufacturing. The preferred conductive metal of choice has been mostly silver due to its excellent electrical properties and ease in sintering. However, silver comes to be expensive than its counterpart copper. Since copper is prone to oxidation, much focus has been given toward photonic sintering that involves sudden burst of pulsed light at certain energy to sinter the copper nanoparticles. With this technique, only the printed material gets sintered in a matter of seconds without having a great impact on its substrate. With all the knowledge, there is still a large gap in the process side with copper where it is important to look how the print process affects the electrical and mechanical properties of copper. With the process developed, the resistivity of printed copper was found to be five times the bulk copper. In regards to adhesion to the polyimide film, mechanical shear load to failure was found to be within 15–20 gF. To demonstrate the complete process, commercial-off-the-shelf components are also mounted on the additively printed pads. Statistically, control charting technique is implemented to understand any process variation over long duration of prints.

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

Double Moving Average Control Chart for Autocorrelated Data

The assumption of normality and independence is necessary for statistical inference of control charts. Misleading results are obtained if the traditional control chart technique is applied on the auto-correlated data. When data is correlated, a time series model is employed to produce an optimum output. The objective is to create a new control chart methodology that takes the autocorrelation of observations into account. Charts of Moving Average, Exponentially Weighted and Cumulative Sum better perform in the existence of autocorrelation data for small and moderate changes. The proposed methodology is highly skilled and receptive to minor, moderate and major changes in the process. Propsed DMA chart increases efficiency of average run length (ARL) chart for moving average (MA) to detect the small to medium magnitude shifts in the mean. The simulation also demonstrates that the DMA chart with spans of w=10 and 15 generally performs well in terms of average run length (ARL) as compared to clasical MA. This research may be extended to a multivariate autocorrelated statistical process control, but it can also be used to recognise and categorise seven categories of traditional control chart patterns, such as Downward, Upward Shift, Normal Trend, Cyclic, Systematic patterns, Increasing and Decresing Trend. In order to identify and categorize a set of subclasses of abnormal patterns, this model (multivariate autocorrelated statistical process control chart) should employ a multilayer feed forward Artificial Neural Network (ANN) architecture controlled by a back-propagation learning rule.

A distribution-free control charting technique based on change-point analysis for detection of epidemics

Worldwide, the detection of epidemics has been recognized as a continuing problem of crucial importance to public health surveillance. Various approaches for detecting and quantifying epidemics of infectious diseases in the recent literature are directly influenced by methods of Statistical Process Control (SPC). However, implementing SPC quality tools directly to the general health care monitoring problem, in a similar manner as in industrial quality control, is not feasible since many assumptions such as stationarity, known asymptotic distribution etc. are not met. Toward this end, in this paper, some of the open statistical research issues involved in this field are discussed, and a distribution-free control charting technique based on change-point analysis is applied and evaluated for detection of epidemics. The main tool in this methodology is the detection of unusual trends, in the sense that the beginning of an unusual trend marks a switch from a control state to an epidemic state. The in-control and out-of-control performance of the adapted control scheme from SPC is thoroughly investigated using Monte Carlo simulations, and the applied scheme is found to outperform its parametric and nonparametric competitors in many cases. Moreover, the empirical comparative study provides evidence that the adapted change-point detection scheme has several appealing properties compared to the current practice for detection of epidemics.

Securing cyber-physical additive manufacturing systems by in-situ process authentication using streamline video analysis

In cyber-physical systems (CPS) of additive manufacturing (AM), cyber-attacks may significantly alter the design of the AM part, compromising its mechanical properties and functionalities. In-situ process authentication may assure that the AM part is fabricated as intended. Most cyber-physical attacks towards AM processes can be manifested as printing path alterations, and an in-situ optical imaging system can detect alteration in the printing path. This will prevent catastrophic geometric changes and mechanical property compromises in the AM parts, ultimately improving the AM process security. In this paper, a novel process authentication methodology is proposed based on image texture analysis of the layer-wise in-situ videos. The layer-wise distribution of the segmented textures’ geometric features is characterized as the layer-wise texture descriptor tensor (LTDT). Given the high dimensionality and sparsity of the extracted LTDTs, the multilinear principal component analysis (MPCA) algorithm is used for dimension reduction. Subsequently, the Hotelling T2 control charting technique is adopted for alteration detection based on the extracted low-dimensional layer-wise features. Case studies based on a fused filament fabrication (FFF) process were conducted to evaluate and validate the proposed framework. The proposed method can achieve over 95% of accuracy, which illustrates that the proposed method can accurately detect process alterations due to printing path changes. In addition, the proposed method significantly outperforms the benchmark method. The computation time for both the proposed and benchmark method is also compared.

A Comparison Between the MEWMA and Mahalanobis Distance Control Chart

Statistical Process Control (SPC) is approaching that uses statistical techniques to monitor the process. The methods of quality control are widely used in controlling any strategy—the widely used control charts. A traditional variable control chart includes three lines: the Centre Line (CL), the Upper Control Limit (UCL), and the Lower Control Limit (LCL), all of which are represented by numeric values—represented by numeric values. A control chart illustrates the centerline of the average value of the quality feature under investigation.
 Depending on numeric observation values, a process is either "in control" or "out of control."
 Considering the current, there are no questions regarding the observations and their values during the production process. However, when these observations include human judgments, assessments, and choices, a continuous random variable (xi) of a manufacturing process should be made up of the variable.
 The MEWMA and Mahalanobis distance control chart techniques are used when more than one variable is involved.
 This paper aims to compare the MEWMA and Mahalanobis distance chart for three compounds for water drinking production in the ALA -Factory in the Sulaimani-Iraq region.
 So, linguistic terms can be used instead of the exact value of the continuous random variable. Today, quality control has become one of the most essential techniques for studying all variables to control production or consumption decision factors. Quality control's primary aim is to guarantee that the goods, services, or processes delivered satisfy particular standards and are reliable and satisfactory. The factor's direct approach is based on the product's quality.

Assessing the performance of control charts for detecting previously unexplored shift types in high density spatial data

As the collection and use of high-density (HD) spatial datasets has increased, the Statistical Process Control research community has strived to develop effective and efficient control charting techniques for these datasets. In general, these research efforts propose new control charting techniques and evaluate their abilities to detect different shift types. However, these works typically considered only conventional shift types, such as mean and variance shifts, which only account for a portion of the shift types that can manifest themselves in HD spatial datasets. In essence, advanced mathematical approaches are being developed for use with state-of-the-art measurement systems but assess their performance with traditional shift types developed for univariate statistics. This may hinder the effectiveness of these approaches in practice, as real-world systems may experience shift types other than (or in addition to) those addressed in the literature. The goal of this paper is to understand the ability of previously proposed control charting techniques to detect these unexplored shift types. This goal is accomplished through a simulation study that considers five different control charting techniques, identified from both the spatial statistics and spatial scan statistics literatures. The performances of these control charts are assessed against previously unexplored HD spatial dataset shift types. The results indicate that many control charting approaches were highly sensitive to variety of shift types. This suggests significant promise in the use of these approaches in systems that are susceptible to a wide variety of shift types, including shift types they were not specifically designed to detect.

Using the Statistical Process Control to evaluate the learning process in Higher Education / Aplicación del Control Estadístico de Procesos para la evaluación de procesos de aprendizaje en la Educación Superior

The purpose of this paper is to propose an approach for incorporating Statistical Process Control (SPC) charting technique to monitor and continuously improve the learning processes by monitoring the satisfaction of students who used an interactive computer-based learning material, which was produced to solve problems in the Operation Management course. By applying SPC methods, the authors examine the sources of process variation (common or special causes) and analyse the ability of teaching strategies to ensure the acquisition and development of the competencies and skills demanded in current university studies. A total of 184 students participated in the learning experience. The findings show that the learning process is under control and therefore the variation of process is due to common causes. However, the Capability Analysis carried out, reveals that process has not enough capacity to achieve the specifications required by the teachers involved in the educational project. This quantitative approach will not only allow self-assessment, but can be used for comparative purposes (other teaching strategies, colleges, etc.). Thus, the control charting is a complementary assessment technique that should be included within the current Quality and Learning Assurance Systems at higher education institutions.

Role of Robotics and Artificial Intelligence in Oral Health and Preventive Dentistry - Knowledge, Perception and Attitude of Dentists.

To assess the knowledge, attitude and perception of dentists (dental students, dental school graduates/interns, postgraduate dentists) of the role of robotics (R) and artificial intelligence (AI) in oral health and preventive dentistry. The null hypothesis was that dentists would not be aware of R and AI use in dentistry and would not be ready to accept them in oral health and preventive dentistry for dental care management and training. This was an observational cross-sectional study in which data was collected from a representative population in Saudi Arabia. 570 participants answered 26 closed-ended questions. The questionnaire's validity and reliability were evaluated for vetting and remarks. The questionnaire collected demographic data of participants and their knowledge, perception and attitude about R and AI. Questions were to be answered with 'yes', 'no' and 'I don't know'. Descriptive statistical analysis was performed using the control chart technique and the chi-squared test, with statistical significance set at p < 0.05. The majority of the participants (n = 313; 54.6%) were males. Dental students, dentist school graduates/interns, and postgraduate dentists comprised of 58.8%, 18.2%, and 23.0% respectively. Most of the respondents gave affirmative answers for knowledge, attitude and perception of R and AI (58.3%, 67.4%, and 60.3%, respectively). Participants agreed that R and AI is beneficial in dentistry and would provide better results. Most (83.3%) would be willing to be treated using R/AI and would recommend (84.5%) treatment with R/AI, as shown in the control chart by affirmative answers. These were significantly above the overall affirmative answers, as the corresponding point lies above 95% UCL (upper confidence limit). Most dentists were unacquainted with R and AI. Dentists had a positive attitude towards R/AI, but due to inadequate knowledge and understanding, its use and applications were very limited. There is significant need in the near future to increase awareness of this concept, as it may increase treatment efficiency and effectiveness.

Performance of EWMA and ANN-based Schemes in Detection of Denial of Service Attack

To ensure successful implementation of cyber-physical systems, industries require computer networks to be protected from malicious attacks. Despite various intrusion detection techniques being proposed by researchers, computer networks are still vulnerable to attacks. As new attacks becoming more complicated, more research is needed to develop more effective and reliable intrusion detection schemes. This study investigated the exponentially weighted moving average control charting technique for detection of malicious denial of service (DoS) trafic and compared it with artificial neural network (ANN) based scheme. Eight features from the Benchmark KDD Cup99 computer network datasets were extracted and their respective ARL1 and false alarm rate were evaluated. The results suggest that EWMA technique is effective only for selective features and the ANN-based scheme is relatively consistent in handling variability in traffic data. This study opens new opportunities for further investigation to enhance performance of the proposed schemes.

Implementation of Statistical Quality Control to Reduce Defects in Mabell Nugget Products (Case Study at PT. Petra Sejahtera Abadi)

Food and beverage industry is one of the five priority industrial sectors in the implementation of Industry 4.0 program. According to data from the ministry of industry throughout 2018, the food and beverage industry is able to grow by 7.91 percent or exceed national economic growth at 5.17 percent. PT. Petra Sejahtera Abadi is a company engaged in producing processed foods made from chicken and beef with freezing processes such as; sausage, smoke beef, spicy chick, spicy wings, nugget, rollade, and cornade beef. Data on the number of defects per year of nugget products from PT. Petra Sejahtera Abadi for the period of 2015 were 23, 453 disabled, in 2016 as many as 21, 876 disabled, in 2017 as many as 20, 987 disabled. Based on the description above, the goal to be achieved in this study is to determine the Statistical Quality Control (SQC) method with control chart techniques and cause and effect diagrams applied by the company in controlling quality to minimize failed products. The results of the SQC implementation contained data that was out of control. For those out of the upper control limit, the 4th period in August 2018 and the 6th period in October 2018 are Out of the Upper Control Line (UCL). The most influential factor in the occurrence of product defects is the machine and material factors that must be immediately corrected.

A nonparametric CUSUM control chart for multiple stream processes based on a modified extended median test

In statistical process control applications, situations may arise in which several presumably identical processes or “streams” are desired to be simultaneously monitored. Such a monitoring scenario is commonly referred to as a “Multiple Stream Process (MSP).” Traditional MSP charting techniques rely on the assumption of normality, which may or may not be met in practice. Thus, a cumulative summation nonparametric MSP control charting technique, based on a modification of the classical extended median test was developed and is referred to as the “Nonparametric Extended Median Test Cumulative Summation (NEMT-CUSUM) chart.” Chart development, including calculation of control limits, is given. Through simulation, the NEMT-CUSUM is shown to perform consistently in the presence of normal and non-normal data. Moreover, it is shown to perform more optimally than parametric alternatives in certain circumstances. Results suggest the NEMT-CUSUM may be an attractive alternative to existing parametric MSP monitoring techniques in the case when distributional assumptions about the underlying monitored process cannot reasonably be made.

Impact of Different Repetitive Sampling Schemes on the Performance of X-bar Control Chart

Repetitive sampling has been found very popular in improving the control chart techniques for last couple of years. In repetitive sampling based control charts, there are two additional control limits inside the usual upper control limit (UCL) and lower control limit (LCL). If any subgroup crosses these limits but remain inside outer limits, it is deferred and replaced with another selection. Process is said to be out of control if any subgroup falls outside UCL/LCL. In this article, the technique has been modified by introducing a relation between outer and inner control limits in terms of a ratio and need of this modification has also been justified by highlighting a gap in the existing technique. By using Monte Carlo simulation, several results have been generated relevant to different sample sizes and introduced ratios. The results have been described with the help of average run length (ARL) tables that how the efficiency of control chart is effected by using different ratios. The modification in the technique also provides variety of alternatives within the scope of repetitive based control charts. All the discussed options have summarized to one table to see that how the control limits under this technique behave and impact on detecting shifts in the process average. The schemes have been interpreted in the light of above ratio and their comparison has been described under different sample sizes that facilitate the user to select most appropriate scheme for a desired process control. An example has been included by choosing one of the proposed schemes to show the application and performance of the proposed control chart in a manufacturing process.

Simultaneous monitoring of magnitude and time-between-events data with a Max-EWMA control chart

Control charting techniques for monitoring the magnitude and frequency of an event are crucial in manufacturing and industrial activities. Several articles on monitoring the two quantities separately are available in the literature, however, a simultaneous monitoring of the magnitude and frequency of an event may be more time efficient. In this paper, we propose the maximum exponentially weighted moving average (Max-EWMA) control chart to simultaneously monitor the magnitude and frequency of an event. The Max-EWMA chart’s statistic is based on the maximum of the absolute values of two EWMA statistics - one for controlling the magnitude and the other for the frequency of an event. In addition, the magnitude is assumed to follow a gamma distribution while the frequency is assumed to follow an exponential distribution. The objective of the proposed scheme is to enhance the speed for detecting shifts in the magnitude and/or frequency of an event. Overall, performance study indicates that the Max-EWMA chart outperforms its existing counterparts with the same objective of detecting small shifts in process parameters. It also competes strongly in detecting large shifts. Finally, we illustrate the application of the proposed chart with examples.

A Re-Evaluation of the Run Rules Control Charts for Monitoring the Coefficient of Variation

An efficient process monitoring system is important for achieving sustainable manufacturing. The control charting technique is one of the most effective techniques to monitor process quality. In certain processes where the process mean and variance are not independent of one another, the coefficient of variation (CV), which measures the ratio of the standard deviation to the mean should be monitored. Castagliola et al. (2013a) proposed the two-sided run rules (RR) control charts for monitoring the CV and it is found that the RR CV charts revealed the problem of ARL-biased performances, especially when the monitored sample size is small, for detecting downward CV shifts. This paper alters the RR CV chart by suggesting the two one-sided run rules (ORR) CV charts achieve the unbiased ARL performances. Additionally, this paper also investigates the ORR CV charts in terms of the expected average run length (EARL) criterion, which is not discussed in Castagliola et al. (2013a). A Markov chain model is established for designing the proposed charts. The statistical performances of the ORR CV, RR CV and Shewhart CV (SH CV) charts are compared in terms of the average run length (ARL) and EARL criteria. The results show that the proposed charts surpass the RR CV and SH CV charts for detecting small and moderate upward and downward CV shifts. The implementation of the ORR CV charts is illustrated with an example using a real dataset.

Analysis of Slope Failure Behaviour Based on Real-Time Measurement Using the X–MR Method

A real-scale slope failure model experiment is performed to analyze the movement behavior of the slope during failure, and the results are analyzed through the x–MR control chart method, along with inverse displacement and various analysis sections such as K-values. As a result, the portent of failure can be identified to be about 7.7–18.3 minutes prior to the final slope failure. As a result of the analyses of changes in the control limit in the various analyses sections, it is considered that the application of K = 3 to the x-MR control chart is effective. It is observed that using the x-MR control chart technique of the inverse displacement is useful for the early prediction of the anomalous behavior of a slope, through a more quick and objective judgment. Henceforth, it is necessary to establish clear techniques for prediction and analyses of slope failure through continuous research, and those results can be used as the basic data of a slope instrumentation management standard that can contribute to the mitigation of life and property damage caused by slope failure hazards.