Process Control Approach Research Articles

Deep Learning and Statistical Process Control Approach to Improve Quality in Immigration Services

Technological advancements have profoundly influenced various sectors, including tourism, by simplifying international travel and increasing the demand for passports. In this context, public feedback on the Surabaya Immigration Office's services, gathered from Google Maps reviews, presents an invaluable dataset for analysis. This study introduces a novel approach to quality management systems in immigration services by applying the Convolutional Long Short-Term Memory (Co-LSTM) for sentiment analysis of public reviews and p-attribute control charts for statistical process control. Focusing on the Surabaya Immigration Office, we analyze public feedback from Google Maps to assess service quality. Our methodology preprocesses and classifies opinion data into sentiment classes, employing Co-LSTM for its superior accuracy over traditional models. The sentiment analysis reveals a predominance of positive over negative reviews, with classification accuracy demonstrating Area Under Curve (AUC) values of 98.61% for training and 85.66% for testing data. Furthermore, the p-attribute control charts are utilized to monitor service defects, identifying areas of uncontrolled variability and suggesting the necessity for service improvement interventions. The study uncovers that the primary public grievance relates to the perceived lack of friendliness and politeness from office staff. By integrating sentiment analysis with statistical process control, this research offers a comprehensive approach for immigration service providers to enhance service quality, respond proactively to public sentiment, and ensure customer satisfaction.

An image-based nonparametric multivariate EWMA control chart for metal additive manufacturing process monitoring

Additive manufacturing offers considerable promise in terms of manufacturing flexibility, but print quality remains a major obstacle to its widespread adoption. Consequently, timely and accurate detection of process variations or anomalies is critical for implementing corrective actions. Melt pool characteristics are crucial to final product quality, and current state-of-the-art studies on melt pool image flow have primarily focused on in-situ sensing, feature extraction, and their relationship with process settings and material properties. This study aims to develop a statistical process control approach to detect process variations immediately using a predefined distribution of monitoring statistics. Two main challenges are addressed: 1) the unknown distribution of melt pool image features, rendering traditional parametric control charts unreliable and 2) the autocorrelation of melt pool image flow features, violating the traditional control chart assumption of independent and identically distributed monitoring data. To overcome these challenges, we propose a multivariate nonparametric Exponential Weighted Moving Average (EWMA) control chart that can appropriately accommodate stationary serial data correlation to monitor process stability by tracking physical features extracted from the original images. Key features of the proposed control chart include its ability to eliminate serial correlation effects and its distribution-free nature, requiring no prior information about the data distribution. Comparative analysis of simulated and real data demonstrates that the proposed approach outperforms baseline methods in anomaly detection accuracy. An actual case study in laser metal deposition (LMD) further validates the effectiveness of the proposed method.

Surface integrity analysis and inspection for nanochannel sidewalls using the self-affine fractal model-based statistical quality control for the atomic force microscopy (AFM)-based nanomachining process

The atomic force microscopy (AFM) technology is a promising method for nanofabrication due to the high tunability of this affordable platform. The quality inspection and control significantly impact the manufacturing effectiveness for realizing the functionality of the achieved nanochannel. Particularly, the surface characteristics of nanochannel sidewalls, which play a significant role in determining the quality of the nanomachined products, can not be accurately captured using conventional surface integrity metrics (e.g., surface roughness). Therefore, it is necessary to propose a method to quantitatively characterize the surface morphology and detect the abnormal parts/regions of the nanochannel sidewall. This paper presents a statistical process control approach derived from the self-affine fractal model to detect the sidewall surface anomalies. It evaluates changes in the self-affine fractal model parameters (standard deviation, correlation length, and roughness exponent), which can be used to signify the changes on the sidewall surface; the statistical distributions of these parameters are derived and used to develop control charts to allow inspection of the sidewall morphology. The approach was tested on the AFM-based nanomachined samples. The results suggest that the presented approach can effectively reflect the abnormal regions on the machined parts, which opens up a new avenue toward guiding the quality control and rework for process improvement for AFM-based nanomachining.

L-PBF AM process failures causal chain: an FMEA-based monitoring approach for process control

ABSTRACT The laser powder bed fusion (L-PBF) additive manufacturing process offers many advantages. However, its industrial integration still needs to be improved due to the certification constraints of the produced parts. Thus, this paper proposes a phenomenological causal chain of process failures to ensure the control of the L-PBF process and further certify the quality of the parts produced. Consequences of unsuitable thermal regimes are analysed using a failure mode and effects analysis (FMEA) approach, allowing a more global understanding of all the causal chain-related data. The proposed approach is based on standard acquisition equipment available on industrial machines and proves relevant for identifying process failures. Experimental validation of the proposed monitoring method is implemented on a use case using dedicated software. Finally, it is shown that the method is relevant in this confrontation and compatible with the time scale of the process for future NC integration and real-time monitoring.

Non-parametric fractional-order controller design for stable process based on modified relay

The relay feedback approach is an effective approach for real-time process control. However, it requires accurate limit cycle data. This paper proposes a novel relay feedback approach for fractional-order (FO) controller design to regulate the stable process performance. The proposed methodology does not require prior knowledge of the process and assures accurate limit cycle. Accurate limit cycle parameters are first estimated using the proposed relay. Then, the FO controller is designed based on the output of the limit cycle obtained through the proposed relay, phase, and Nyquist stability criteria. Further, the accuracy of the proposed relay is verified analytically through harmonic analysis. Various performance parameters and indices are estimated and compared with recently published techniques to assess the effectiveness. Additionally, disturbance and parametric uncertainty analyses are conducted to examine the disturbance rejection capacity and robustness. The efficacy of the proposed methodology is further validated on a real-time liquid-level control four-tank process.

ETF dead-time compensation based multiloop control approach for multivariable processes

Due to significant interaction dynamics and time delays in multivariable processes, designing a well-tuned PI controller is always challenging. When tuning multi-loop PI controllers, effective open-loop transfer function should be used instead of the diagonal transfer function element. The complexity of the effective open-loop transfer function (EOTF) increase rapidly for high-dimensional multivariable processes. Therefore, in this manuscript, it is proposed to use equivalent transfer functions (ETFs) under perfect control approximation. Subsequently, a dead-time compensation (DTC) scheme is used for ETFs and PI controllers are tuned. DTC can facilitate eliminating the impact due to time delay from the multivariable process, making the task of tuning PI controllers relatively less complex. The proposed multi-loop control topology minimizes the controller effort, and a good controller design for a multivariable process can be achieved. The PI controller parameters for dead-time compensated ETFs are calculated using simple IMC PI tuning rules, where the filter factor is taken based on the paired element relative gain value. To examine the effectiveness of the proposed controller tuning approach, the proposed control strategy was applied to a wide range of multivariable processes. Compared to state-of-the-art control strategies, the proposed control strategy provides superior closed-loop performance indices, even when there is a significant plant-model mismatch.

Plasma Surface Treatment of Metals: A Comprehensive Review of Recent Developments and Future Prospects

In recent years, plasma surface treatment has seen tremendous breakthroughs, which have revolutionized both the field of material engineering and the procedures used to modify surfaces. This paper offers a thorough discussion of the most recent discoveries and developments that have been made in the field of plasma surface treatment. It investigates cutting-edge technologies, innovative plasma sources and sophisticated process control approaches, all of which have greatly improved the effectiveness and adaptability of the therapy. The research also explores how these breakthroughs solve earlier obstacles, provide new opportunities for industrial applications and pave the way for future developments in the area. Moreover, the study looks at how these advancements open the door for future developments in the sector. This study gives useful insights into the emerging landscape of plasma surface treatment and its potential influence on a variety of sectors by summarizing and assessing current research. These insights may be found in this paper.

Reduced-order Koopman modeling and predictive control of nonlinear processes

In this paper, we propose an efficient data-driven predictive control approach for general nonlinear processes based on a reduced-order Koopman operator. A Kalman-based sparse identification of nonlinear dynamics method is employed to select lifting functions for Koopman identification. The selected lifting functions are used to project the original nonlinear state–space into a higher-dimensional linear function space, in which Koopman-based linear models can be constructed for the underlying nonlinear process. To curb the significant increase in the dimensionality of the resulting full-order Koopman models caused by the use of lifting functions, we propose a reduced-order Koopman modeling approach based on proper orthogonal decomposition. A computationally efficient linear robust predictive control scheme is established based on the reduced-order Koopman model. A case study on a benchmark chemical process is conducted to illustrate the effectiveness of the proposed method. Comprehensive comparisons are conducted to demonstrate the advantage of the proposed method.

An Integrated Real-time Monitoring and Statistical Process Control Approach for Coating Process and Product Quality via In-line Optical Coherence Tomography

An Integrated Real-time Monitoring and Statistical Process Control Approach for Coating Process and Product Quality via In-line Optical Coherence Tomography

Real-time detection of mean and variance changes in experience sampling data: A comparison of existing and novel statistical process control approaches.

Retrospective analyses of experience sampling (ESM) data have shown that changes in mean and variance levels may serve as early warning signs of an imminent depression. Detecting such early warning signs prospectively would pave the way for timely intervention and prevention. The exponentially weighted moving average (EWMA) procedure seems a promising method to scan ESM data for the presence of mean changes in real-time. Based on simulation and empirical studies, computing and monitoring day averages using EWMA works particularly well. We therefore expand this idea to the detection of variance changes and propose to use EWMA to prospectively scan for mean changes in day variability statistics (i.e., , , ln( )). When both mean and variance changes are of interest, the multivariate extension of EWMA (MEWMA) can be applied to both the day averages and a day statistic of variability. We evaluate these novel approaches to detecting variance changes by comparing them to EWMA-type procedures that have been specifically developed to detect a combination of mean and variance changes in the raw data: EWMA- , EWMA-ln( ), and EWMA- - . We ran a simulation study to examine the performance of the two approaches in detecting mean, variance, or both types of changes. The results indicate that monitoring day statistics using (M)EWMA works well and outperforms EWMA- and EWMA-ln( ); the performance difference with EWMA- - is smaller but notable. Based on the results, we provide recommendations on which statistic of variability to monitor based on the type of change (i.e., variance increase or decrease) one expects.

A bio-inspired emergent control approach for distributed processes

The complexity of industrial processes has grown exponentially, with high degrees of dependency, non-linearity, imprecision, among other aspects, which motivates the interest in developing distributed control systems for their management. In this sense, this work proposes a bio-inspired distributed control approach, where control actions emerge from the component interactions. The distributed control approach is based on the response threshold model to solve the control problem by imitating the behavior of ants. Particularly, our approach is inspired by the way as the ants carry out the division of labor in a colony. Thus, our control approach based on the threshold response model refers to the possibility of reacting to stimuli associated with the distributed control tasks. It has the ability to stabilize the process in the presence of abrupt/successive changes and various initial conditions, with a minimum effort of the actuators to achieve the objectives. Also, it has shown its versatility in different operational contexts with the same parameter tuning. The bio-inspired control approach is proved in a quadruple tank process, a complex system due to its multivariate nature. In this way, our paper introduces a new domain of application of the response threshold model in industrial processes. Several experiments were carried out in different contexts to evaluate its stability, robustness, etc., and compare it with other similar works. In general, the control performance metrics show satisfactory results, which reflects its ability to adapt to changes in the dynamics of the process, which encourages additional studies.

A practical Reinforcement Learning implementation approach for continuous process control

Industrial process control using model-based technologies is well established. These technologies are typically non-adaptive and so have limitations. Reinforcement Learning (RL) provides a model-free adaptive alternative. RL is a type of machine learning (ML) where models or data sets of the environment are not necessary before learning can start. It generates data, by exploring the environment and then learn the behavior from it. Though RL has been successfully applied for learning and playing various games such as Go, Chess, Atari; its application to continuous process control problems is not trivial. There is a need for online RL implementation to be safe, fast learning and explainable when applied to industrial control problems. Rather than adding to the extensive research on augmenting existing RL algorithms, the paper presents a unique systematic method of formulating the RL problem incorporating domain-specific knowledge about process constraints and objectives, resulting in reduced dimensionality, along with modifications to the exploration process, applicable to any model free RL algorithm supporting continuous states and actions, to enhance safety, speed and explainability of online RL implementation without requiring a simulation model. The approach is successfully implemented on two multivariable processes: a simulated distillation column and a temperature control lab setup using the Deep Deterministic Policy Gradient (DDPG) algorithm. The work demonstrates that the presented method is applicable to multivariable, noisy, non-linear processes with disturbances. It will further the potential of introducing the advances in Artificial Intelligence and Machine Learning algorithms for intelligent process control capable of enabling autonomous operation in the process industry.

Event‐triggered adaptive stabilization control of stochastic nonlinear systems with unmodeled dynamics

AbstractIn this paper, we present a novel controller design method for stochastic nonlinear systems with unmodeled dynamics, uncertain parameters, and unknown covariance noise. In order to deal with these uncertainties, a new event‐based small gain controller is designed. The event‐triggered scheme can reduce the computational burden caused by disturbance and noise. By combining the technique of changing supply rate with small gain condition, a stochastic input‐to‐state practically stability Lyapunov function is obtained for the subsystem. Simultaneously, the changing supply function is used to treat with unmodeled dynamics. Based on backstepping process and adaptive control approach, the influence of unknown covariance noise is overcome. It is proved that the designed state‐feedback controller can ensure the overall system is stochastic input‐to‐state practically stability.

Expectations of non-COVID-19 deaths during the pre-vaccine pandemic: a process-control approach

BackgroundDebate over “social distancing” as a response to the pandemic includes the claim that disrupting clinical and public health programming dependent on human-to-human contact increased non-COVID-19 deaths. This claim warrants testing because novel pathogens will continue to emerge. Tests, however, appear frustrated by lack of a convention for estimating non-COVID-19 deaths that would have occurred had clinical and public health programming during the pre-vaccine pandemic remained as efficacious as in the pre-pandemic era. Intending to hasten the emergence of such a convention, we describe and demonstrate “new-signal, prior-response expectations” suggested by research and methods at the intersection of epidemiology and process control engineering.MethodsUsing German data, we estimate pre-pandemic public health efficacy by applying Box-Jenkins methods to 271 weekly counts of all-cause deaths from December 29 2014 through March 8 2020. We devise new-signal, prior-response expectations by applying the model to weekly non-COVID-19 deaths from March 9 2020 through December 26 2020.ResultsThe COVID-19 pandemic did not coincide with more non-COVID-19 deaths than expected from the efficacy of responses to pre-pandemic all-cause deaths.ConclusionsNew-signal, prior-response estimates can contribute to evaluating the efficacy of public health programming in reducing non-COVID-19 deaths during the pre-vaccine pandemic.

Microwave-Assisted Freeze-Drying with Frequency-Based Control Concepts via Solid-State Generators: A Simulative and Experimental Study

Freeze-drying is a common process to extend the shelf life of food and bioactive substances. Its main drawback is the long drying time and associated high production costs. Microwaves can be applied to significantly shorten the process. This study investigates the effects of modulating the electromagnetic field in microwave-assisted freeze-drying (MFD). Control concepts based on microwave frequency are evaluated using electromagnetic simulations. The concepts are then applied to the first part of primary drying in a laboratory-scale system with solid-state generators. Targeted frequency modulation in the electromagnetic simulations enabled an increase in energy efficiency or heating homogeneity throughout MFD while having negligible effects on the power dissipation ratio between frozen and dried product areas. The simulations predicted the qualitative effects observed in the experimental proof of concept regarding energy efficiency and drying homogeneity. Additionally, shortened drying times were observed in the experiments with a targeted application of energy-efficient frequencies. However, differences occurred in the quantitative validation of the electromagnetic models for energy efficiency in dependence on frequency. Nevertheless, the models can be used for a time-efficient investigation of the qualitative effects of the control concepts. In summary, frequency-based control of MFD represents a promising approach for process control and intensification.

Deep Learning-based Sequence Modeling for Advanced Process Control in Semiconductor Manufacturing

Semiconductor manufacturing is one of the most data-intensive industries in manufacturing. Many so-called Advanced Process Control (APC) approaches based on equipment or process data have been presented over the years to improve quality and efficiency, reduce waste and increase energy savings. While on the one hand, intensive production leads to large amount of data available for the development of Machine Learning-based technologies, on the other hand, the high-mix production, multiple processes and machines lead researchers and developers to deal with ’small data’ scenarios. In this context, domain adaptation approaches are highly relevant to enhance scalability. In this work, we are extending a state-of-the-art Deep Learning architecture called Domain Adversarial Neural Network based Alignment Model (DBAM) by considering new sequence learning layers. In the real-world case study, the proposed architecture is shown to achieve higher accuracy, allow for better management in the absence of large amounts of data and make previously trained models reusable in similar scenarios.

The usage of Statistical Process Control (SPC) in Industry 4.0 conditions

Purpose: The purpose of this publication is to present the usage of Statistical Process Control (SPC) approach in Industry 4.0 conditions. Design/methodology/approach: Critical literature analysis. Analysis of international literature from main databases and polish literature and legal acts connecting with researched topic. Findings: The integration of Statistical Process Control (SPC) with Industry 4.0 signifies a transformative shift in quality management, elevating SPC from a conventional monitoring tool to a proactive force in contemporary manufacturing. Originally employed for ensuring consistent quality through process monitoring, SPC's role has been redefined in the Industry 4.0 era, utilizing data analytics, real-time monitoring, and connectivity to offer a comprehensive understanding of the manufacturing ecosystem. Enabled by the Internet of Things (IoT), SPC gains real-time insights into production processes, crucial for swift anomaly identification and issue resolution. Advanced analytics and artificial intelligence enhance SPC's predictive capabilities, enabling proactive measures to maintain product quality. This publication underscores the significance of SPC in Industry 4.0 conditions, emphasizing its roots in statistical principles, systematic process control, and the distinction between common and special cause variations. The integration of SPC with Industry 4.0 and Quality 4.0 leverages technologies for enhanced quality management, emphasizing real-time data monitoring and collaborative, data-driven approaches. While Table 2 outlines specific aspects of SPC integration, highlighting its versatility, Table 3 enumerates the advantages, emphasizing improved visibility and predictive quality management. However, challenges such as data security concerns and technology integration complexity, outlined in Table 4, necessitate strategic solutions. In conclusion, this integration represents a pivotal advancement, positioning SPC as an indispensable asset for organizations seeking quality excellence, operational efficiency, and resilience in the dynamic landscape of modern manufacturing. Originality/Value: Detailed analysis of all subjects related to the problems connected with the usage of Statistical process Control in Industry 4.0 conditions. Keywords: Industry 4.0; Quality 4.0, quality management; quality methods, SPC, Statistical Process Control. Category of the paper: literature review.

Image-Based Feedback Control Using Tensor Analysis

In manufacturing systems, many quality measurements are in the form of images, including overlay measurements in the semiconductor manufacturing and dimensional deformation profiles of fuselages in an aircraft assembly process. To reduce the process variability and ensure on-target quality, process control strategies should be deployed, in which the high-dimensional image output is controlled by one or more input variables. To design an effective control strategy, the process model should be first estimated via relationship exploration between the image output and inputs, off-line. Next, the control law is formulated by minimizing the control objective function online. The main challenges of achieving such a control strategy include (i) the high dimensional output of a regression model, (ii) the integrated analysis of both the spatial structure of image outputs and the temporal structure of the image sequence, and (iii) non-iid noises. To address these challenges, we propose a novel tensor-based process control approach by incorporating the tensor time series and regression techniques. Based on the process model, we can then obtain the control law by minimizing a control objective function. Although our proposed approach is motivated by a 2D image case, it can be extended to higher-order tensors such as point clouds. Simulation and case studies show that our proposed method is more effective than benchmarks in terms of relative mean square error.

Multisource and temporal variability in Portuguese hospital administrative datasets: Data quality implications

BackgroundUnexpected variability across healthcare datasets may indicate data quality issues and thereby affect the credibility of these data for reutilization. No gold-standard reference dataset or methods for variability assessment are usually available for these datasets. In this study, we aim to describe the process of discovering data quality implications by applying a set of methods for assessing variability between sources and over time in a large hospital database. MethodsWe described and applied a set of multisource and temporal variability assessment methods in a large Portuguese hospitalization database, in which variation in condition-specific hospitalization ratios derived from clinically coded data were assessed between hospitals (sources) and over time. We identified condition-specific admissions using the Clinical Classification Software (CCS), developed by the Agency of Health Care Research and Quality. A Statistical Process Control (SPC) approach based on funnel plots of condition-specific standardized hospitalization ratios (SHR) was used to assess multisource variability, whereas temporal heat maps and Information-Geometric Temporal (IGT) plots were used to assess temporal variability by displaying temporal abrupt changes in data distributions. Results were presented for the 15 most common inpatient conditions (CCS) in Portugal. Main findingsFunnel plot assessment allowed the detection of several outlying hospitals whose SHRs were much lower or higher than expected. Adjusting SHR for hospital characteristics, beyond age and sex, considerably affected the degree of multisource variability for most diseases. Overall, probability distributions changed over time for most diseases, although heterogeneously. Abrupt temporal changes in data distributions for acute myocardial infarction and congestive heart failure coincided with the periods comprising the transition to the International Classification of Diseases, 10th revision, Clinical Modification, whereas changes in the Diagnosis-Related Groups software seem to have driven changes in data distributions for both acute myocardial infarction and liveborn admissions. The analysis of heat maps also allowed the detection of several discontinuities at hospital level over time, in some cases also coinciding with the aforementioned factors. ConclusionsThis paper described the successful application of a set of reproducible, generalizable and systematic methods for variability assessment, including visualization tools that can be useful for detecting abnormal patterns in healthcare data, also addressing some limitations of common approaches. The presented method for multisource variability assessment is based on SPC, which is an advantage considering the lack of gold standard for such process. Properly controlling for hospital characteristics and differences in case-mix for estimating SHR is critical for isolating data quality-related variability among data sources. The use of IGT plots provides an advantage over common methods for temporal variability assessment due its suitability for multitype and multimodal data, which are common characteristics of healthcare data. The novelty of this work is the use of a set of methods to discover new data quality insights in healthcare data.

Optimal control approach for nonlinear chemical processes with uncertainty and application to a continuous stirred-tank reactor problem

Practical chemical process is usually a dynamic process including uncertainty. Stochastic constraints can be used to chemical process modeling, where constraints cannot be strictly satisfied or need not be fully satisfied. Thus, optimal control of nonlinear systems with stochastic constraints can be available to address practical nonlinear chemical process problems. This problem is hard to cope with due to the stochastic constraints. By introducing a novel smooth and differentiable approximation function, an approximation-based approach is proposed to address this issue, where the stochastic constraints are replaced by some deterministic ones. Following that, the stochastic constrained optimal control problem is converted into a deterministic parametric optimization problem. Convergence results show that the approximation function and the corresponding feasible set converge uniformly to that of the original problem. Then, the optimal solution of the deterministic parametric optimization problem is guaranteed to converge uniformly to the optimal solution of the original problem. Following that, a computation approach is proposed for solving the original problem. Numerical results, obtained from a nonlinear continuous stirred-tank reactor problem including stochastic constraints, show that the proposed approach is less conservative compared with the existing typical methods and can obtain a stable and robust performance when considering the small perturbations in initial system state.