Monitoring Process Variability Research Articles

A new omnibus SPRT chart for monitoring process mean and variability based on the average number of observations to signal

The recent development of the omnibus sequential probability ratio test (OSPRT) chart marks a significant contribution to the advancement of joint monitoring schemes. As the OSPRT chart is a variable-sample-size control chart, practitioners often wish to understand its inspection efficiency, i.e. the number of observations it samples before producing a signal. In this article, we propose two enhanced optimization designs for the OSPRT chart based on the average number of observations of signal (ANOS) and expected value of the ANOS (EANOS) metrics under deterministic and unknown shift sizes, respectively. The ANOS metric is central to our design as it perfectly combines both the average run length (ARL) and the average sample number. A comparative analysis reveals that the OSPRT chart outperforms four benchmarking control charts in terms of the ANOS and EANOS metrics. Finally, an implementation of the OSPRT chart is presented with a ball shear test dataset.

Dynamic control of cardboard-blank picking by using reinforcement learning

Abstract Standard packaging lines with high output rates often struggle when dealing with uncertainties in the conditions of the handled materials. This paper focuses on a piece of machinery in an automatic packaging line, namely an automated apparatus that extracts cardboard blanks from a buffer and transfers them to the next section through suction cups. In this context, the success of the operation depends on various controllable parameters, disturbances, and time-dependent variables, whose mutual relationships are not easily identifiable and whose understanding has so far been entrusted to the experiential knowledge of human operators. Currently, drops in picking success rates require the machine to be stopped and operators to intervene on-site, making use of their expertise to identify the issue and recalibrate the machine. To address the problem, this paper presents an artificial-intelligence-enabled controller, capable of continuously and autonomously recalibrating the apparatus and compensating for disturbances, in order to avoid missed or incorrectly picked cardboard blanks. In particular, this work exploits experimental data to build a model of the system, on which a reinforcement-learning algorithm is trained. The controller is tasked with regulating the controllable parameters while monitoring process variables. The developed agent is tested on the real apparatus to assess its performance.

Development of average run length formulas for EWMA control chart under the AR(1) with quadratic trend model for detecting and monitoring process variability

Development of average run length formulas for EWMA control chart under the AR(1) with quadratic trend model for detecting and monitoring process variability

A double sampling multivariate GLR control charting method for joint monitoring of process mean vector and covariance matrix under additive covariate model

A process is in its stable state when it has sufficient precision and accuracy. The accuracy is expressed as the deviation of the process mean from its target while the low dispersion indicates the high process precision. Therefore, joint monitoring of multivariate process mean and variability ensures a high rate of producing items. This study proposes a multivariate generalized likelihood ratio scheme called DS-MGLR with two features: (1) considering the effect of imprecise observations, and (2) enhancing the chart sensitivity in reacting to process disturbances. Since the shift magnitude in the distribution parameters is not known in advance, the expected average run length is also calculated for both the MGLR and DS-MGLR charts. To reduce the impact of measurement errors, an improved version of the DS-MGLR chart based on multiple measurement approach is developed. Three comparative studies are presented to indicate the importance of the properties of the developed charts. The first study confirms the efficiency of the double sampling strategy, while the second study indicates the error effect. The third one shows the effectiveness of the multiple measurement approach in compensating for the error contamination. Finally, the applicability of the proposed chart is highlighted using a real data example.

Process monitoring for covariance matrices with latent structures

The monitoring of process variability is generally achieved by prompt detection of changes in the covariance matrix of multiple correlated quality characteristics. In the literature, there have been many approaches successfully developed for this purpose. Notably, in many chemical processes or some with a special or symmetric structure, involved high-dimensional data are strongly linearly correlated, so that they can be regarded to be determined by a number of low-dimensional latent variables, and hence, have a small intrinsic dimension. In this sense, the observed high-dimensional data have actually a latent structure, formed by latent variables and sensor errors. If such a latent structure can be fully exploited, more efficient monitoring of covariance matrices can be achieved. To this end, this article refines the structure of covariance matrices and develops a series of charting statistics, which are able to efficiently detect shifts in the covariance matrices of latent variables and of sensor errors. Based on the sensitivity analysis of the proposed charting statistics, we provide their choices, as well as a diagnostic scheme to determine the source of shifts. Monte Carlo simulations have demonstrated their superiority over existing alternatives in detecting covariance matrix shifts in latent variables or in sensor errors.

Deep-learning based in-situ micrograph analysis of high-density crystallization slurry using image and data enhancement strategy

Process analytical technology (PAT) plays a crucial role in optimizing crystalline powder product qualities, improving process stability, and reducing experimental costs by enabling real-time monitoring and control of process variables during the crystallization process. In-situ process imaging and analysis have gained significant attention due to their capacity to provide abundant information through images, leading to remarkable advancements in image processing technology, particularly with the support of artificial intelligence. However, the performance suffers when processing high-density slurry images due to challenges such as defocusing, overlapping, background and crystal edges blurriness, and inconsistencies in crystal scales. To address these challenges and explore research strategies based on deep learning for segmenting and analyzing high slurry density images effectively, this study constructed a specific dataset that contains high-density slurry crystal images which is well-labeled, and introduced state-of-the-art neural networks including YOLOv8, U2-net, and Mask R-CNN combined with image and data enhancement strategies. Comparative analysis revealed that YOLOv8 outperformed Mask R-CNN and U2-net by capturing multi-dimensional information in high-density crystal slurry scenarios. Finally, based on the segmentation results of the practical taurine crystallization process using proposed strategy, 1D crystal size, aspect ratio along with 2D size distributions were extracted for further evaluation of crystallization kinetics and crystal properties accurately and quickly.

Change-point design-based charting schemes for monitoring process variability

ABSTRACT Most of the recommended nonparametric control charts apply to monitor location parameter. In this paper, we propose a nonparametric exponentially weighted moving average (EWMA) control chart for monitoring scale parameter, which combines the Ansari–Bradley test and the framework of change point detection. As with most nonparametric control charts, a large number of historical observations are used to motivate the control chart, and we propose a nonparametric EWMA control chart that achieves the same purpose with a very small number of historical observations. Simulation results demonstrate that our control chart is effective and robust for monitoring scale parameter shifts. For illustration of our main results, an applied example demonstrates the practicality of this control chart for monitoring scale parameter.

Tool-integrated thin-film sensor systems for measurement of cutting forces and temperatures during machining

Effective in-process control mechanisms are vital for the future development of production processes and ask for reliable monitoring of process variables. For this, new tool-integrated sensor systems for measuring cutting forces and temperature for machining of hardened steel are proposed. Thin-film technology is used to apply a thermoresistive temperature sensor in the chip-workpiece contact area for the first time on industrial cutting inserts with chip breaker geometry. Complementary piezoresistive thin-film sensors based on diamond-like carbon (DLC) and manganin layers were developed and deposited on a washer. It was placed beneath the indexable insert to measure the cutting forces. Turning experiments were conducted to study sensor life and accuracy. All sensors showed an adequate lifetime for laboratory purposes. Temperature compensation methods were investigated for force measurement and results compared to forces recorded by a dynamometric platform. While temperature compensation for the DLC-based sensor needs further optimization, results of the manganin-based sensor compared well with the expected cutting forces. The manganin thin-film sensor exhibited rapid responsiveness and great reproducibility, underscoring its prospective utility for real-time monitoring of cutting forces in machining operations.

Industrial Drying of Fruit and Vegetable Products: Customized Smart Monitoring and Analytical Characterization of Process Variables in the OTTORTO Project

In the era of digitalization, the process industry is one of the sectors most affected by the need for change. The adoption of IoT-based intelligent monitoring systems for the collection of real-time measurements of energy and other essential operational variables, on one hand, makes it possible to accumulate big data useful for the company management to monitor the stability of the production process over time, and on the other hand, helps to develop predictive models that enable more efficient work and production. The OTTORTO project stems from the need of the FARRIS company to adapt its production line to agriculture 4.0 policies, responding to the higher goals of digitization and technological transition imposed at the national and EU level. The objectives of the current study are (i) to present an “ad hoc” customized intelligent and multi-parameter monitoring system to derive real-time temperature and humidity measurements inside the company’s industrial drying kilns; and (ii) to show how it is possible to extract information from operational data and convert it into a decision support too and an effective knowledge medium to better understand the production process. Studying the correlations between temperature and humidity measurements showed that for most of the observation period, the system was thermodynamically quite stable in terms of major operational risks, such as humidity saturation inside the kilns causing condensation on the products to be dried. However, to remedy the occasional occurrence of such inefficiencies, implementing kilns with the introduction of forced air extraction systems could bring significant benefits in terms of improved energy-environmental performance.

A phase II run sum chart for monitoring process mean and variability

ABSTRACT This article proposes a run sum Max chart for monitoring a process and detecting changes in mean and variability simultaneously. A Markov chain method is applied to evaluate the statistical performance of the chart by using both average run length (ARL) and expected average run length (EARL) criteria. The numerical analysis demonstrates an improved performance of the run sum Max control chart over the usual Max chart. In addition, a numerical comparison with the Max-EWMA control chart as well as with the Max chart supplemented with runs rules, reveals that the run sum Max chart outperforms these charts in the detection of moderate to large shifts in both process parameters. Furthermore, we provide practical guidance for the selection of the appropriate charting procedure, while the use of the proposed run sum Max chart in practice is illustrated via a numerical example.

Performance of Max-HEWMAMS control chart for simultaneous monitoring of process mean and variability in the presence of measurement errors

Performance of Max-HEWMAMS control chart for simultaneous monitoring of process mean and variability in the presence of measurement errors

IWAAM: An automated system for monitoring and control of wire-arc additive manufacturing

In this work, a wire-arc additive manufacturing (WAAM) automated system named iWAAM is presented. iWAAM is an automated system for monitoring and control of WAAM depositions. The communication framework is based on ROS (Robot Operating System) and kuka_experimental package which allow the execution of predefined trajectories (ros_control) or online trajectory correction during the deposition. The power source control and configuration, and the deposition sequence are also embedded in the proposed framework. A Graphical User Interface (GUI), based on Qt, is used to monitor process variables, such as arc current, voltage, wire feed speed and travel speed. Furthermore, iWAAM controls the fabrication process of parts designed by external planners or by simple depositions using primitive trajectories (raster, zigzag, circular). In addition, a web interface is presented, allowing the users to monitor the process remotely. Experimental results of actual WAAM depositions illustrate the functionalities of the proposed automated system.

A Monitoring Kualitas Kaca Panasab dengan Diagram Kontrol MEWMA

Panasab glass is one type of glass that can absorb some of the heart's heat so that the temperature in the room is more comfortable. One of the national glass companies that produce hot glass monitors the silvering coating process. The silvering method is considered less effective because it is expensive. Therefore, in this paper, the process of controlling the quality of hot glass is carried out on the quality characteristics of the left and right CD edge distortion, which is the length of the roll marks on the right and left sides of the net glass that affect the presence or absence of distortion in the visual check results. The two quality characteristics correlate with each other. Therefore, quality control is carried out using a Multivariate Exponentially Weighted Moving Average (MEWMA) control chart to monitor process variability and averages. MEWMA control chart statistics are affected by the weighting value . The quality characteristic data of panasab glass is divided into phase 1 and phase 2. Phase 1 is used to obtain an in-control parameter estimate (IC) which is then used for monitoring phase 2. Quality monitoring in phase 1, the process has been statistically controlled after iterations have been carried out. By using =0.4. Meanwhile, phase 2, based on the MEWMA control chart, shows that the producton procrss of panasab glass production has not been controlled statistically. Based on process capability analysis, the results obtained that the level of precision and accuracy of the quality of panasab glass products is still low. So it can be concluded that the production process of panasav glass is not yet capable.

A high-dimensional control chart for monitoring process variability under gauge imprecision effect

A high-dimensional control chart for monitoring process variability under gauge imprecision effect

A generalized multiple dependent state sampling chart based on ridge penalized likelihood ratio for high-dimensional covariance matrix monitoring

Online monitoring of high-dimensional processes variability in which the number of variables is larger than the sample size is a challenging issue for quality practitioners because the sample covariance matrix is not invariable. To deal with this challenge, a generalized multiple dependent state sampling (GMDS) chart based on ridge penalized likelihood ratio (RPLR) statistic is developed for Phase II monitoring of multivariate process variability under high-dimensional setting. The developed control chart benefits from three advantages: (1) departing from the conventional covariance matrix charts, it can be efficiently employed for both spars and non-spars covariance matrices; (2) it is able to detect spars shift patterns in which only a few covariance matrix elements are deviated from their nominal values; and (3) it outperforms the detectability of the RPLR chart in terms of average run length (ARL) and standard deviation of run length (SDRL). The performance of RPLR, MDS-RPLR, and GMDS-RPLR charts are compared using extensive simulation studies by considering different diagonal and/or off-diagonal covariance matrix disturbance. Moreover, sensitivity analysis are provided to analyze how the number of process variables and GMDS parameter affect the run length properties of the developed chart.

Online detection of residual stress near the gate using cavity pressure for injection molding

Abstract Residual stress negatively affects the quality of optical products and is a difficult problem to solve. Regulating process parameters is a critical element in quality control. Therefore, the online monitoring of critical process variables is necessary for stabilizing product quality and reducing manufacturing cost. This study proposes a method for the online monitoring of residual stress near the gate of a mold using multiple pressure sensors to measure process variables and verifies the feasibility of online monitoring. The study found that residual stress near the gate could not be accurately measured with only a single sensor because measurements were susceptible to interference with process parameters. When a single sensor was used to measure process variables, correlation coefficients of between 0.48 and 0.59 were obtained, and when multiple sensors were used, correlation coefficients of between 0.80 and 0.92 were obtained, which indicated strong correlation. Therefore, the online monitoring of residual stress near the gate is feasible. This monitoring method can provide a new concept of online monitoring technology for the injection molding of optical products.

Exploring the potential of machine learning for more efficient development and production of biopharmaceuticals.

Principles of Industry 4.0 direct us to predict how pharmaceutical operations and regulations may exist with automation, digitization, artificial intelligence (AI), and real time data acquisition. Machine learning (ML), a sub-discipline of AI, involves the use of statistical tools to extract the desired information either through understanding the underlying patterns in the information or by development of mathematical relationships among the critical process parameters (CPPs) and critical quality attributes (CQAs) of biopharmaceuticals. ML is still in its infancy for directly supporting the quality-by-design based development and manufacturing of biopharmaceuticals. However, adoption of ML-based models in place of conventional multi-variate-data-analysis (MVDA) is increasing with the accumulation of large-scale data. This has been majorly contributed by the real-time monitoring of process variables and quality attributes of products through the implementation of process analytical technology in biopharmaceutical manufacturing. All aspects of healthcare, from drug design to product distribution, are complex and multidimensional. Thus, ML-based approaches are being applied to achieve sophistication, accuracy, flexibility and agility in all these areas. This review discusses the potential of ML for addressing the complex issues in diverse areas of biopharmaceutical development, such as biopharmaceuticals design and assessment of early stage development, upstream and downstream process development, analysis, characterization and prediction of post-translational modifications (PTMs), formulation, and stability studies. Moreover, the challenges in acquisition, cleaning and structuring the bioprocess data, which is one of the major hurdles in implementation of ML in biopharma industry, have also been discussed. Regulatory perspectives on implementation of AI/ML in the biopharma sector have also been briefly discussed. This article is a bird's eye view on the recent developments and applications of ML in overcoming the challenges for adopting "Industry - 4.0" in the biopharma industry.

Multivariate ELR control chart with estimated mean vector and covariance matrix

Recently, simultaneous monitoring of multivariate process mean and variability has received increasing attention in the literature of statistical process monitoring (SPM). However, the deleterious impact of parameter estimation on the capability of control charts designed for simultaneous monitoring of the mean vector and covariance matrix of multivariate processes has been clearly neglected. In this paper, we study the effect of estimation error on both in-control and out-of-control properties of the multivariate exponentially weighted moving average (EWMA)-based generalized likelihood ratio (MELR) chart. Simulation studies in terms of the average run length (ARL), the standard deviation of run length (SDRL), and the median run length (MRL) metrics are conducted to explore how the amount of Phase I reference samples affects the performance of the MELR chart. The results show that extra variability due to estimation error reduces the detecting capability of the MELR chart while increases its false alarm rate. Meanwhile, a real-life data is provided to illustrate poor Phase I estimation results in more false alarms than expected.

Statistical method based on dissimilarity of variable correlations for multimode chemical process monitoring with transitions

Chemical industrial processes are always accompanied by multiple operating conditions, which brings great challenges for multivariate statistical process monitoring methods to extract general characteristics from multimode data, especially for time-varying characteristics in transitions between two modes. In this work, a novel statistical process monitoring method based on the dissimilarity of process variable correlation (DISS-PVC) is proposed. The proposed method aims to monitor multiple stable modes and between-mode transitions simultaneously with no prior knowledge of the number of operating modes. Unlike traditional methods oriented to monitoring process variables, the proposed method is applied to monitor the correlation of process variables based on the idea that variable correlation should always conform to a certain process internal mechanism, no matter in which stable or transition mode. Mutual information is first employed to quantitate variable correlation with a moving-window approach. Cosine similarity between eigenvalues of mutual information matrices is selected as a dissimilarity index to evaluate the difference in variable correlation between two data sets and perform fault detection. The effectiveness of the proposed method is verified on the benchmark Tennessee Eastman (TE) process and an industrial continuous catalytic reforming heat exchange unit.

Effect of Parameter Estimation on the Performance of Shewhart <img src=image/13425522_01.gif>-joint Chart Looked at in Terms of the Run Length Distribution

Using spread-charts to monitor process variation and thereafter using the <img src=image/13425522_02.gif>-chart to monitor the process mean after is a common practice. To apply these charts independently using estimated 3-sigma limits is common. Recently, some authors considered the application of <img src=image/13425522_02.gif> and R-charts together as a charting scheme, <img src=image/13425522_01.gif>-chart when the standards are known, Case KK, only the mean standard is known, Case KU and both standards unknown, Case UU. The average run length (ARL) performance criterion was used. However, because of the skewed nature of the run length (RL) distribution, many authors have frowned at the use of ARL as a sole performance measure and encouraged the percentiles of the RL distribution instead. Therefore, the cdfs of the RLs of the chart under the cases mentioned will be derived in this work, and the percentiles are used to look at the chart for Case KU and the yet to be considered case of the chart, Case UK where only the process variance is known is included for comparison. These are the contribution to the existing literature. <img src=image/13425522_01.gif>-chart performed better in Case KU than in Case UK and the unconditional in-control median run length described the behavior of the chart better than the in-control ARL.