Process Control Issues Research Articles

A retrospective regulatory analysis of FDA recalls carried out by pharmaceutical companies from 2012 to 2023

To introduce products in the US market, pharmaceutical companies must first obtain FDA clearance. Manufacturers might recall a product if it poses a risk of damage or violates FDA regulations. This study investigates the types, causes and consequences of recalls, as well as FDA participation and suitable recall strategies. We relied on the FDA website to gather recall data sets from 2012 to 2023, collecting information on the date of issuance, company and type of violation. The most frequent causes for recalls were sterility issues and inadequate compliance with current good manufacturing practices (cGMP). An examination of sterility recalls revealed two primary causes: a lack of assurance in sterility (accounting for 48% of recalls) and instances of non-sterility (making up 45% of recalls). A thorough examination of cGMP recalls revealed five primary types of violations: process control issues, inadequate storage practices, manufacturing problems, the presence of nitroso-amine impurities and concerns regarding stability. The findings demonstrate that sterility and cGMP compliance are FDA priorities. Pharmaceutical companies must, therefore, enhance quality compliance and create effective quality management systems that oversee the manufacturing process, quality control, personnel training and documentation to avoid these recalls. Companies should establish an internal compliance checklist and be prepared for the rectification process.

Multivariate auto‐correlated process control by a residual‐based mixed CUSUM‐EWMA model

AbstractMultivariate auto‐correlated process control issues in industrial systems are a concern for statistical process monitoring (SPM). Traditional control charts produce large false alarms and/or miss timely detections of quality deterioration because they are unable to recognize the signals from multivariate auto‐correlated response variables. To track multivariate auto‐correlated processes, this paper presents a new residual‐based mixed multivariate control chart using cumulative sum (CUSUM) and exponentially weighted moving average (EWMA) approaches. Using in‐control data, the multi‐output least square support vector regression model's optimal hyper‐parameters are determined, and a bootstrap method is used to estimate the upper control limit of the proposed control chart. The suggested control chart has strong detection performance for a small magnitude mean vector shift based on the average run length (ARL) performance for a particular range of shifts. Experimental result elaborates that the proposed control chart is more sensitive to detecting the mean vector shift compared with the existing commonly used models, such as multivariate CUSUM and multivariate EWMA control charts. The proposed control chart model and corresponding computational algorithm are successfully applied to SPM in an electronic conductor production line with multivariate auto‐correlated attributes.

An ensemble neural network framework for improving the detection ability of a base control chart in non-parametric profile monitoring

Profile monitoring is a challenging issue in statistical process control (SPC). It aims to use a functional relationship between a response variable and one or more explanatory variable(s) to summarize the quality of a process/product. Most of the existing studies consider the same form of a functional relationship for both in-control (IC) and out-of-control (OC) situations or parametric approaches. However, non-parametric profiles with different relationships in OC conditions are very common. In this paper, we propose a novel ensemble framework to monitor changes in both regression relationship and variation of the profile for Phase II applications. This approach employs a pool of artificial neural networks (ANNs) as learners to enhance the efficiency of a base control chart, which is a non-parametric exponentially weighted moving average (NEWMA) in this study. Then, a further ANN is used as a reasoning scheme (incorporator) to perform prediction by combining the outcomes of the learners. Experimental results demonstrate the effectiveness of the proposed framework, denoted by EANNN, in comparison with the base control chart, i.e., NEWMA, and other non-parametric methods. In addition, a practical example regarding a deep reactive ion-etching process from semiconductor device fabrication is provided to show the implementation of the proposed method.

Potential Process Control Issues with Pemetrexed.

Pemetrexed is a folate analog metabolic inhibitor used in treatment of locally advanced or metastatic nonsquamous non-small cell lung cancer and for mesothelioma. Intra-lot and inter-lot variability in the spectra of ALIMTA® was detected in the Drug Quality Study (DQS) using Fourier transform near-infrared spectrometry (FTNIR). One vial of 12 (8%) sampled from lot S20I013A appeared 3.0 multidimensional SDs from the other vials, suggesting that it represents a different material. Consequently, additional spectra from other lots were analyzed. Spectra of 147 vials from 23 lots in the spectral library contained 14 vials that were outside the main group (26.4 SDs using a subcluster detection test), suggesting that the 14 library vials (9.5% of the total) also contain differing materials.

Robust process capability indices for multiple linear profiles

AbstractIn some statistical process control applications, the quality of a process is described by a linear relationship between the response variable(s) and the independent variable(s), which is called a linear profile. Process capability is a significant issue in statistical process control. The ability of a process to meet customer specifications or standards is measured by the process capability indices (PCIs). There are several attempts for studying the process capability in linear profiles. In this research, two robust PCIs for multiple linear profiles are proposed. In the suggested robust PCIs, the process capability is estimated using the M‐estimator and the Fast‐τ‐estimator. Performances of the proposed robust PCIs in comparison with the classical PCIs in the absence and presence of contamination are evaluated. The results show that the robust PCIs proposed in this research perform as well as the classical PCIs in the absence of contamination and much better in the presence of contamination. The proposed PCIs, using Fast‐τ‐estimator, perform better in small shifts, and the proposed PCIs, using M‐estimator, perform better in large shifts. Introduction of robust indices for multivariate multiple linear profiles is an area for further research.

Chemical vapour deposition

Chemical vapour deposition (CVD) is a powerful technology for producing high-quality solid thin films and coatings. Although widely used in modern industries, it is continuously being developed as it is adapted to new materials. Today, CVD synthesis is being pushed to new heights with the precise manufacturing of both inorganic thin films of 2D materials and high-purity polymeric thin films that can be conformally deposited on various substrates. In this Primer, an overview of the CVD technique, including instrument construction, process control, material characterization and reproducibility issues, is provided. By taking graphene, 2D transition metal dichalcogenides (TMDs) and polymeric thin films as typical examples, the best practices for experimentation involving substrate pretreatment, high-temperature growth and post-growth processes are presented. Recent advances and scaling-up challenges are also highlighted. By analysing current limitations and optimizations, we also provide insight into possible future directions for the method, including reactor design for high-throughput and low-temperature growth of thin films. This Primer on chemical vapour deposition summarizes current and emerging experimental set-ups as well as common characterization approaches used to determine thin film formation and quality as applied to graphene and other novel 2D materials.

Monitoring of complex profiles based on deep stacked denoising autoencoders

Profile monitoring remains an interesting issue in statistical process control (SPC). Although there have been considerable researches devoted to analysis of profile data, the challenges concerning the monitoring of complex profiles (e.g., multivariate profiles, nonlinear profile, autocorrelated profiles) is yet to be addressed well. The high-dimension explanatory variables and autocorrelation generally affect effectiveness of those regular profile monitoring models and could cause many false alarms. Recent years have witnessed remarkable successes of deep learning techniques in visual and acoustic studying fields. In this paper, a deep learning model known as stacked denoising autoencoders (SDAE) is developed for complex profiles modeling and monitoring. Three control charts based on the SDAE model are further developed for abnormal detection of complex profiles. Comparison between the proposed method and other typical methods is implemented to illustrate effectiveness of the proposed method in five representative profiles. Finally, a real dataset is further utilized to demonstrate the effectiveness of the proposed method in agriculture fields. The experimental results illustrate the effectiveness of the SDAE-based method on complex profiles monitoring. This paper provides an inspiration for using deep learning techniques to monitor complex profiles.

Process control issues of fine grinding in a planetary mill

The article is devoted to the study of controlled grinding processes in a planetary mill. The possibility of continuous monitoring of the fineness of grinding by using the laws of automatic control, synthesized on the basis of the optimization approach, is considered. A mathematical model of the control object in the linear stationary approximation is formed. The dynamics of the obtained model is compared with data from field experiments according to the main quality criteria of dynamic processes. For this model, a linear-quadratic controller is synthesized, which is a combination of a Kalman filter that generates an optimal RMS estimate of the object’s state vector with a linear-quadratic optimal state controller. The article provides details of the implementation of the adopted approach and presents the results of modeling dynamic processes for the considered closed-loop control system.

Monitoring Aqueous Reprocessing Systems for Detection of Facility Misuse

Traditional approaches to material control and accounting at used nuclear fuel reprocessing facilities rely in large part on laboratory-based destructive assay of periodic samples taken from key stages. These techniques are highly accurate, but limited inspection frequency may limit the size and operation of a reprocessing facility. Online monitoring techniques using in-line detectors can be applied to fuel reprocessing systems to improve the situational awareness as the facility is operating to provide additional indicators of the evolving state of the facility between normal inspection periods, potentially alerting regulators to the need for additional inspection. The multi-isotope process (MIP) monitor combines medium-resolution gamma detectors with statistical data analysis techniques to monitor reprocessing systems for anomalous conditions resulting from process control issues or malfeasance. The MIP monitor is attractive for process and safeguards monitoring because of its unsupervised approach to detection and its inherent data obfuscation. This paper presents the application of the MIP monitor to detect a notional diversion scenario in an operating aqueous reprocessing facility by analyzing gamma spectra with a combination of principal component analysis (PCA) and the sequential probability ratio test. Gamma spectra collected from the H-Canyon reprocessing facility were analyzed to demonstrate the efficacy of the MIP Monitor for detecting notional diversion scenarios. The proposed analysis successfully detected small, protracted diversions, which would alert regulators of the need for additional inspection.

A novel method for accurately monitoring and predicting tool wear under varying cutting conditions based on meta-learning

Monitoring and predicting tool wear is an important issue in dynamic process control under changing conditions, especially for machining large-sized difficult-to-cut materials used in airplanes. Existing tool wear monitoring and prediction methods are mainly based on given cutting conditions over a period of time. This paper presents a novel method for accurately predicting tool wear under varying cutting conditions based on a proposed new meta-learning model which can be easily trained, updated and adapted to new machining tasks of different cutting conditions. Experiments proved a substantial improvement in the accuracy of predicting tool wear compared with existing deep learning methods.

Review—Management of Copper Damascene Plating

An overview of process and quality control issues faced in controlling copper damascene plating is provided. The challenges for effective management of a copper damascene plating process have evolved due to early limitations and problems which were discovered, and then as more demands resulted from Chemical Mechanical Planarization issues, yield and reliability concerns, reduction in critical feature size and the shift to 300 mm wafers as well as the adoption of dielectric films with low dielectric constants. The demands have been addressed by adherence to quality control methodology, improvements in plater design and operation, advancements in plating electrolytes, and by new metrology tools and methodology. The key measurement areas outlined here are bath concentration measurement and control, film properties metrology, contamination issues, and defects. Particular attention is given to the use of electrochemical and optical methods with enhanced capabilities. This includes the use of chemometrics for electroanalytical bath concentration control and optical methods for film thickness, surface roughness, and film stress/wafer curvature measurements.

Precision 3D profile in-line measurement of through-silicon via (TSV) based on high-frequency spectrum signals in the pupil plane

As a deep and fine vertical via, through-silicon via (TSV) is a key factor in three-dimensional (3D) integrated circuit stack, which can pass through a silicon wafer or chip for 3D integration. TSV’s 3D profile metrology is one of the key issues in process control. In this work, we propose and demonstrate a non-destructive in situ measurement technology that can be used to measure the 3D geometric profiles of TSV with an aspect ratio of 10. The optical system designed by us can collect high-frequency signals, which contain the whole geometry information of the TSV in the pupil plane. This measurement is proven effective by analysing the high-frequency spectrum of the pupil plane. Our 3D measurement technology can measure TSV profile diameters as small as 5 μm with an aspect ratio of 10:1. The measurement accuracy is ±0.5% comparable with that of SEM measurements using the same sample.

A decision theoretic approach to change point estimation for binomial CUSUM control charts

ABSTRACTDetecting when the process has changed is a classical problem in sequential analysis and is an important practical issue in statistical process control. This article is concerned about the binomial cumulative sum (CUSUM) control chart, which is extensively applied to industrial process control, health care, public health surveillance, and other fields. For the binomial CUSUM, a maximum likelihood estimator has been proposed to estimate the change point. In our article, following a decision theoretic approach, we develop a new estimator that aims to improve the existing methods. For interval estimation, we propose a parametric bootstrap procedure to construct the confidence set of the change point. We compare our proposed method with the maximum likelihood estimator and Page's last zero estimator in terms of mean squared error by simulations. We find that the proposed method gives more unbiased and robust results than the existing procedures under various parameter designs. We analyze jewelry manufacturing data for illustration.

Indirect Model Based Estimation of Cutting Force and Tool Tip Vibrational Behavior in Milling Machines by Sensor Fusion

Real time prediction of cutting tool condition and machined surface finish have been attractive research objectives over the last decades. However, providing practical and reliable solutions is still a demanding task for milling machine tools. One of the most challenging literature goals is to obtain a robust estimation of the cutting forces through indirect sensor measurements since many process and tool related quantities are indirectly linked to cutting forces. Another challenging issue in machining process monitoring and control is prediction of surface finish and quality. As the vibration plays a major role in the surface generation, this can be done by accurate prediction of the vibrational displacements at the tool tip during machining operation. In this paper, a novel model based estimation of cutting force and tool tip acceleration is designed and tested based on data fusion of different sensors measurements. In this context, two sensors (piezoelectric accelerometer and eddy-current displacement both mounted inside the spindle structure) have been utilized to acquire the experimental signals over a wide range of frequencies.In order to predict the above mentioned quantities, an optimal state estimator based on Kalman Filter (KF) is used. The models have been obtained by system identification method based on experimental measurements performed on a machine tool. The model based estimator is fed by real data. The results show that the estimation of the impulse force and tool tip acceleration can be achieved accurately in low and high frequency ranges by assigning different weights to the measurement sensors.

Identification and quantification of concurrent control chart patterns using extreme-point symmetric mode decomposition and extreme learning machines

Control chart pattern recognition (CCPR) is an important issue in statistical process control because unnatural control chart patterns (CCPs) exhibited on control charts can be associated with specific causes that adversely affect the manufacturing processes. In recent years, many machine learning techniques [e.g., artificial neural networks (ANNs) and support vector machines (SVMs)] have been successfully applied to CCPR. However, such existing research for CCPR has mostly been developed for identification of basic CCPs. Little attention has been given to the utilization of ANNs/SVMs for identification of concurrent CCPs (two or more basic CCPs occurring simultaneously) which are commonly encountered in practical manufacturing processes. In addition, these existing research for CCPR cannot provide more detailed CCP parameter information, such as shift magnitude, trend slope, cycle amplitude, etc., which is very useful for quality practitioners to search the assignable causes that give rise to the out-of-control situation. This study proposes a hybrid approach that integrates extreme-point symmetric mode decomposition (ESMD) with extreme learning machine (ELM) to identify typical concurrent CCPs and in addition to accurately quantify the major CCP parameter of the specific basic CCPs involved. The numerical results indicate that the proposed model can effectively identify not only concurrent CCPs but also basic CCPs. Meanwhile, the major CCP parameter of the identified concurrent CCP can also be accurately quantified.

Operation Optimization in the Hot-Rolling Production Process

The operation optimization problem (OOP) is an important issue in production process control and optimization in process industries, because the desired solution of OOP is the optimal setting for c...

Management control systems for business processes in uncertain environments

Purpose – This paper aims to explore the application of management control systems (MCS) to business processes when an organisation is operating in an uncertain environment. The limitations of conventional process control in this context are described, and the opportunities to exploit current MCS ideas are considered. Design/methodology/approach – One such model, Simons' levers of control, is used for case study research in a European high technology start-up company. Findings – The findings suggest that the organisation responded to its uncertain environment with a package of formal and informal control systems for its strategically important business processes. The relative importance of individual business processes was observed to positively influence the rigour of control systems' implementation. Also, the inherent uncertainty or unpredictability of an individual business process was observed to influence the type of control systems employed. The use of guiding or enabling systems, particularly interactive control systems, was more pronounced in those business processes which were inherently unpredictable. Originality/value – These findings illustrate that adopting a MCS perspective to address business process control issues can yield new useful insights for managers when dealing with uncertainty.

Autoregressive coefficient-invariant control chart pattern recognition in autocorrelated manufacturing processes using neural network ensemble

Pattern recognition is an important issue in statistical process control because unnatural control chart patterns (CCPs) exhibited on control charts can be associated with specific causes that adversely affect the manufacturing process. In recent years, many machine learning techniques [e.g., artificial neural networks (ANNs) and support vector machine (SVM)] have been successfully used to the CCP recognition (CCPR) in autocorrelated manufacturing processes. However, these existing researches can only detect and classify unnatural CCPs but do not provide more detailed process information, for example the autocorrelation level, which would be very useful for quality practitioners to search the assignable causes that give rise to the out-of-control situation. This study proposes a neural network ensemble-enabled autoregressive coefficient-invariant CCPR model for on-line recognition of seven typical types of unnatural CCP, assuming that the process observations are AR(1) correlated over time with unknown constant autoregressive coefficient. In this model, each individual back propagation network (BPN) is trained to recognize only CCPs with the specific autoregressive coefficient of the underlying process, while the outputs of all these individual BPNs are combined via a learning vector quantization network (LVQN). The experimental results indicate that the proposed CCPR model can not only accurately recognize the specific CCP types but also efficiently identify the autoregressive coefficient of the underlying autocorrelated manufacturing process. Empirical comparisons also show that the proposed CCPR model outperform other existing CCPR approaches in literature. In addition, a demonstrative application is provided to illustrate the utilization of the proposed CCPR model to the CCPR in autocorrelated manufacturing processes.

Computer-Aided Production Control of Automobile Injection Molds Based on Process Perception

This paper focuses on major issues in production process control of automobile injection molds based on process perception, such as manufacture resource perception, exchange in three-dimensional model (3D), production planning and scheduling, manufacture process planning (tool path and assembly sequence). After a survey of them, we propose a method for the construction of computer-aided production process control platform. 1) Define the networked manufacture semantic/data model and an encapsulating approach to efficiently manipulate manufacturing resources. 2) Develop structural multi-view data models for the design information of automobile molds transferring in 3D. 3) Propose a production planning and scheduling arithmetic. 4) Develop a variation accumulation model for manufacturing process control. 5) Integrate the tool path and assembly sequence planning of injection molds by using a variation accumulation model. 6) Integrate the existing and/or developed models to the new system. Finally we introduce a validation method and discuss the prospect to the accurate control of production process.

Analysis of Current Issues in Statistical Process Control through Questionnaire Survey

Analysis of Current Issues in Statistical Process Control through Questionnaire Survey