Process Monitoring Strategy Research Articles

1D and 2D error assessment and correction for extrusion-based bioprinting using process sensing and control strategies

The bioprinting literature currently lacks: (i) process sensing tools to measure material deposition, (ii) performance metrics to evaluate system performance, and (iii) control tools to correct for and avoid material deposition errors. The lack of process sensing tools limits in vivo functionality of bioprinted parts since accurate material deposition is critical to mimicking the heterogeneous structures of native tissues. We present a process monitoring and control strategy for extrusion-based fabrication that addresses all three gaps to improve material deposition. Our strategy uses a non-contact laser displacement scanner that measures both the spatial material placement and width of the deposited material. We developed a custom image processing script that uses the laser scanner data and defined error metrics for assessing material deposition. To implement process control, the script uses the error metrics to modify control inputs for the next deposition iteration in order to correct for the errors. A key contribution is the definition of a novel method to quantitatively evaluate the accuracy of printed constructs. We implement the process monitoring and control strategy on an extrusion-printing system to evaluate system performance and demonstrate improvement in both material placement and material width.

Working within the Design Space: Do Our Static Process Characterization Methods Suffice?

The Process Analytical Technology initiative and Quality by Design paradigm have led to changes in the guidelines and views of how to develop drug manufacturing processes. On this occasion the concept of the design space, which describes the impact of process parameters and material attributes on the attributes of the product, was introduced in the ICH Q8 guideline. The way the design space is defined and can be presented for regulatory approval seems to be left to the applicants, among who at least a consensus on how to characterize the design space seems to have evolved. The large majority of design spaces described in publications seem to follow a “static” statistical experimentation and modeling approach. Given that temporal deviations in the process parameters (i.e., moving within the design space) are of a dynamic nature, static approaches might not suffice for the consideration of the implications of variations in the values of the process parameters. In this paper, different forms of design space representations are discussed and the current consensus is challenged, which in turn, establishes the need for a dynamic representation and characterization of the design space. Subsequently, selected approaches for a dynamic representation, characterization and validation which are proposed in the literature are discussed, also showcasing the opportunity to integrate the activities of process characterization, process monitoring and process control strategy development.

Monitoring the abrasive waterjet drilling of Inconel 718 and steel: a comparative study

This paper presents an experimental study on abrasive waterjet (AWJ) drilling of Inconel 718 and AISI 1040 steel, with an aim to understand the AWJ hole drilling performance and to investigate the process monitoring strategy by using acoustic emission (AE). In the experiments, pressure levels were set from 200 to 350 MPa at intervals of 50 MPa, and drilling time between 5 and 30 s at intervals of 5 s. It is found that the hole depth increases along with the drilling time at a slowing down penetration rate. A higher pressure results in a higher penetration rate. While there exists a direct influence of the material property on the drilling efficiency, it has limited significance with regard to the diameter of the hole produced. AE-RMS signal is demonstrated to be able to reflect the decreasing trend of the penetration rate and the influence of the pressure. A strong correlation between the penetration rate and AE-RMS values in AWJ hole drilling is shown through correlation analysis. The AE-RMS is also substantially affected by the material properties. A discrepancy of the AE-RMS under similar condition may suggest an anomaly in this cutting run.

Application of Raman spectroscopy in monoclonal antibody producing continuous systems for downstream process intensification.

Monoclonal antibodies (mAbs) are biopharmaceuticals produced by mammalian cell lines in bioreactors at a variety of scales. Cell engineering, media optimization, process monitoring, and control strategies for in vitro production have become crucial subjects to meet increasing demand for these high value pharmaceuticals. Raman Spectroscopy has gained great attention in the pharmaceutical industry for process monitoring and control to maintain quality assurance. For the first time, this article demonstrated the possibility of subclass independent quantitative mAb prediction by Raman spectroscopy in real time. The developed model estimated the concentrations of different mAb isotypes with average prediction errors of 0.2 (g/L) over the course of cell culture. In situ Raman spectroscopy combined with chemometric methods showed to be a useful predictive tool for monitoring of real time mAb concentrations in a permeate stream without sample removal. Raman spectroscopy can, therefore, be considered as a reliable process analytical technology tool for process monitor, control, and intensification of downstream continuous manufacturing. The presented results provide useful information for pharmaceutical industries to choose the most appropriate spectroscopic technology for their continuous processes.

Correlating nano-scale surface replication accuracy and cavity temperature in micro-injection moulding using in-line process control and high-speed thermal imaging

Micro-injection moulding (μIM) stands out as preferable technology to enable the mass production of polymeric components with micro- and nano-structured surfaces. One of the major challenges of these processes is related to the quality assurance of the manufactured surfaces: the time needed to perform accurate 3D surface acquisitions is typically much longer than a single moulding cycle, thus making impossible to integrate in-line measurements in the process chain. In this work, the authors proposed a novel solution to this problem by defining a process monitoring strategy aiming at linking sensitive in-line monitored process variables with the replication quality. A nano-structured surface for antibacterial applications was manufactured on a metal insert by laser structuring and replicated using two different polymers, polyoxymethylene (POM) and polycarbonate (PC). The replication accuracy was determined using a laser scanning confocal microscope and its dependence on the variation of the main μIM parameters was studied using a Design of Experiments (DoE) experimental approach. During each process cycle, the temperature distribution of the polymer inside the cavity was measured using a high-speed infrared camera by means of a sapphire window mounted in the movable plate of the mould. The temperature measurements showed a high level of correlation with the replication performance of the μIM process, thus providing a fast and effective way to control the quality of the moulded surfaces in-line.

Monitoring Strategy for a Multistage Manufacturing Facility Using K-Means Clustering Technique

The article proposes a process monitoring strategy for a multistage manufacturing facility. In past, researchers have attempted to develop monitoring strategies for multistage processes using multi-block principal component analysis (MBPCA) which requires comprehensive process knowledge. But in most of the cases, the complete acquaintance with the process knowledge may not be possible. Hence, in such cases, a suitable clustering algorithm-based classification of process parameters may be performed. The current article proposes a methodology articulated via amalgamation of the K-means clustering algorithm and traditional MBPCA for monitoring of a process. K-means clustering algorithm is capable of clubbing the process parameters into relevant blocks without any prior process knowledge. In order to validate the devised strategy, a case study related to a multistage manufacturing facility engaged in production of rail blooms is considered. The outcome of the devised strategy has been compared with the outcome of regular MBPCA-based monitoring strategy which is used when the process knowledge is available.

A review of some sampling and aggregation strategies for basic statistical process monitoring: Discussion by Ross Sparks

This article (Zwetsloot and Woodall 2019) is a welcome thorough investigation of sampling and aggregation strategies for the basic control charts. This article has raised the profile of sampling an...

A Review of Some Sampling and Aggregation Strategies for Basic Statistical Process Monitoring

We review the long-established rational subgrouping principle for determining an effective sampling plan for process monitoring. We present some other general advice that has been given in the literature and discuss some issues related to sampling as it applies to monitoring. Because it is very common to form samples by aggregating data over fixed time intervals, we review the literature on the effect of temporal aggregation on process monitoring performance and provide our perspective. We offer some practical advice and some directions for future research.

Discussion of Zwetsloot and Woodall, “A review of some sampling and aggregation strategies for basic statistical process monitoring”

We appreciate the opportunity to comment on the article by Zwetsloot and Woodall (2019). As practitioners, we have seen very little guidance in the literature on this topic. We believe that it deserves more attention and appreciate the work of Zwetsloot and Woodall to increase the visibility on important topics. Our focus is more from a manufacturing perspective based on our work experiences.

Discussion: A review of some sampling and aggregation strategies for basic statistical process monitoring (I. M. Zwetsloot and W. H. Woodall)

I would like to congratulate Drs. Zwetsloot and Woodall (2019) for writing this comprehensive and timely review. The issue of sampling and aggregation is becoming increasingly important in conjunct...

Discussion of article by Zwetsloot and Woodall: A review of some sampling and aggregation strategies for basic statistical process monitoring

Discussion of article by Zwetsloot and Woodall: A review of some sampling and aggregation strategies for basic statistical process monitoring

Unified Process Monitoring Strategy for a Facility Engaged In the Production of Multiple Portfolio of Products

The process monitoring strategies have not kept pace with the development in the production systems. Most of the multivariate statistical process monitoring methods deals with single product portfolio. The current article attempts to devise a unified monitoring strategy for monitoring of multiple products or at least different grades of the same product. A case study from an integrated steel plant producing multiple products has been deliberated to validate the monitoring strategy devised. A comparative evaluation of the unified strategy based on the multigroup variant of principal component analysis (PCA) and the individual strategy based on the conventional PCA was carried out on the basis of three parameters viz. the model fit values, fault detection and the fault diagnosis. In all the three parameters, barring some trifling differences, the unified monitoring strategy seems to replicate the individual strategies well.

Hybrid independent component analysis (H-ICA) with simultaneous analysis of high-order and second-order statistics for industrial process monitoring

Both the Gaussian and non-Gaussian information exist simultaneously in many real industrial processes. However, the standard independent component analysis (ICA) cannot effectively explore process information when there is more than one independent component (IC) following Gaussian distribution. Besides, process may present an obvious temporal structure due to wide applications of controllers, which has not been investigated by standard ICA. To solve the above problem, the paper proposed a hybrid ICA (H-ICA) algorithm for process monitoring by concurrent analysis of both high-order and second-order statistics. The time-delayed correlation matrices are first diagonalized for robust whitening to remove the effect of the additive white noise. Then, non-Gaussian information is explored by FastICA with the utilization of higher-order statistics, and Gaussian information is extracted by analyzing the time structure information contained in second-order nonzero-delayed covariance matrices. By the hybrid analysis of high-order and second-order statistics, both non-Gaussian-distributed and Gaussian-distributed ICs are extracted. In this way, more comprehensive process information is fully investigated and analyzed for the development of process monitoring strategy. The proposed H-ICA process monitoring algorithm is verified by both a numerical example and a real thermal power plant process which illustrates its feasibility and efficacy.

Guided ink and process design for aerosol jet printing based on annular drying effects

Aerosol jet printing (AJP) is a promising microscale additive manufacturing technology for emerging applications in printed and flexible electronics. However, the more widespread adoption of this emerging technique is hindered by a limited fundamental understanding of the process. This work focuses on a critical and underappreciated aspect of the process, the interaction between drying induced by the sheath gas and impaction. Combining focused experiments with support from numerical modeling, it is shown that these effects have a dramatic impact on key outputs of the process, including deposition rate, resolution, and morphology. These effects can amplify minor changes in ink composition or atomization yield, increasing process sensitivity and drift. Moreover, these effects can confound strategies for in-line process monitoring and control based on empirical observables. Strategies to directly manipulate this annular drying phenomenon are presented, providing a viable tool to tailor and study the process. This work clarifies coupled effects of printer design, ink formulation, and print parameters, establishing a more robust theoretical framework for understanding the AJP process and advancing the maturity of this promising technology.

Online low-field NMR spectroscopy for process control of an industrial lithiation reaction-automated data analysis.

Monitoring specific chemical properties is the key to chemical process control. Today, mainly optical online methods are applied, which require time- and cost-intensive calibration effort. NMR spectroscopy, with its advantage being a direct comparison method without need for calibration, has a high potential forenabling closed-loop process control while exhibiting short set-up times. Compact NMR instruments make NMR spectroscopy accessible in industrial and rough environments for process monitoring and advanced process control strategies. We present a fully automated data analysis approach which is completely based on physically motivated spectral models as first principles information (indirect hard modeling-IHM) and applied it to a given pharmaceutical lithiation reaction in the framework of the European Union's Horizon 2020 project CONSENS. Online low-field NMR (LF NMR) data was analyzed by IHM with low calibration effort, compared to a multivariate PLS-R (partial least squares regression) approach, and both validated using online high-field NMR (HF NMR) spectroscopy. Graphical abstract NMR sensor module for monitoring of the aromatic coupling of 1-fluoro-2-nitrobenzene (FNB) with aniline to 2-nitrodiphenylamine (NDPA) using lithium-bis(trimethylsilyl) amide (Li-HMDS) in continuous operation. Online 43.5 MHz low-field NMR (LF) was compared to 500 MHz high-field NMR spectroscopy (HF) as reference method.

Near infrared spectroscopy based monitoring of extraction processes of raw material with the help of dynamic predictive modeling

The control of batch-to-batch quality variations remains a challenging task for pharmaceutical industries, e.g., traditional Chinese medicine (TCM) manufacturing. One difficult problem is to produce pharmaceutical products with consistent quality from raw material of large quality variations. In this paper, an integrated methodology combining the near infrared spectroscopy (NIRS) and dynamic predictive modeling is developed for the monitoring and control of the batch extraction process of licorice. With the spectra data in hand, the initial state of the process is firstly estimated with a state-space model to construct a process monitoring strategy for the early detection of variations induced by the initial process inputs such as raw materials. Secondly, the quality property of the end product is predicted at the mid-course during the extraction process with a partial least squares (PLS) model. The batch-end-time (BET) is then adjusted accordingly to minimize the quality variations. In conclusion, our study shows that with the help of the dynamic predictive modeling, NIRS can offer the past and future information of the process, which enables more accurate monitoring and control of process performance and product quality.

A Quick Reaction System Using Energy, Process and Quality Data for Process Characterization and Holistic Monitoring of Large Scale Assembly Lines

Modern large-scale assembly lines need to deliver a highly varied and flexible output, while achieving 0 ppm scrap. This is becoming more and more demanding due to an increasing complexity of the products. Thus, it will be a major step in manufacturing processes to develop process monitoring strategies which increase productivity as well as flexibility and reliability of the entire assembly process. Therefore, it is necessary to advance the entire chained assembly line instead of only isolated processes and stations. For this reason, technological processes have to be assessed as a chain of upstream and downstream partial processes instead of being considered in isolation. [3] Moreover, data mining projects depend on the available data bases, while additional data sources may increase the derived knowledge. [2] These ideas are extendable by energy data measurements, besides process and quality data. Existing monitoring approaches to reduce scrap usually use dashboards linked with process and quality data. [5] Therefore, this paper presents a new methodology using data mining analysis of energy data for assembly presses as well as complete assembly lines for electromagnetic actuators. This novel holistic approach realized by a Quick Reaction System allows to increase efficiency, while decreasing energy and resource consumption for actuator manufacturing on large scale assembly lines. In particular, the data base consists of process and quality data, enriched by energy data measurements. This approach enables a comprehensive process characterization as well as monitoring of whole assembly lines by using data mining tools. Furthermore, this paper describes a quantitative evaluation of its data mining based event detection of critical process parameters.

Process modeling and control applied to real-time monitoring of distillation processes by near-infrared spectroscopy

A distillation device that acquires continuous and synchronized measurements of temperature, percentage of distilled fraction and NIR spectra has been designed for real-time monitoring of distillation processes. As a process model, synthetic commercial gasoline batches produced in Brazil, which contain mixtures of pure gasoline blended with ethanol have been analyzed. The information provided by this device, i.e., distillation curves and NIR spectra, has served as initial information for the proposal of new strategies of process modeling and multivariate statistical process control (MSPC). Process modeling based on PCA batch analysis provided global distillation trajectories, whereas multiset MCR-ALS analysis is proposed to obtain a component-wise characterization of the distillation evolution and distilled fractions. Distillation curves, NIR spectra or compressed NIR information under the form of PCA scores and MCR-ALS concentration profiles were tested as the seed information to build MSPC models. New on-line PCA-based MSPC approaches, some inspired on local rank exploratory methods for process analysis, are proposed and work as follows: a) MSPC based on individual process observation models, where multiple local PCA models are built considering the sole information in each observation point; b) Fixed Size Moving Window – MSPC, in which local PCA models are built considering a moving window of the current and few past observation points; and c) Evolving MSPC, where local PCA models are built with an increasing window of observations covering all points since the beginning of the process until the current observation. Performance of different approaches has been assessed in terms of sensitivity to fault detection and number of false alarms. The outcome of this work will be of general use to define strategies for on-line process monitoring and control and, in a more specific way, to improve quality control of petroleum derived fuels and other substances submitted to automatic distillation processes monitored by NIRS.

Sparse probabilistic principal component analysis model for plant-wide process monitoring

In the industrial monitoring process, probabilistic principal component analysis (PPCA) is a popular algorithm for reducing the dimension. However, the principal components (PCs) are not easy to interpret and its preserved number cannot be determined automatically. In this paper, we propose a sparse PPCA (SPPCA) to improve the interpretability by adding a prior and introducing sparsification to the loading matrix of PPCA. An expectation-maximization (EM) algorithm is used to obtain the parameters of the probabilistic formulation, and the dimensionality of the latent variable space can be automatically determined during the iterative process. With the sparse representation, a process monitoring strategy is then developed with the construction of several partial PPCA models. Case studies of SPPCA to a numerical case and Tennessee Eastman (TE) benchmark process demonstrate its feasibility and efficiency.

Distributed model projection based transition processes recognition and quality-related fault detection

In this paper, a novel transition process identification algorithm based on distributed model projection (DMP) is proposed for clustering nonlinear transition data and monitoring the variations in the transition process. Compared to several alternative identification methods, the DMP algorithm considers both the correlations between variables and correlations between samples. Also, a framework is proposed to combine DMP algorithm and hierarchical clustering to derive an optimal clustering results through a large amount of individual trials of the DMP algorithm. Based on the offline classification results, a transition process is divided into several sub-segments and each of them can be characterized by a stable model. Then the online identification and monitoring methods are carried out based on the sub-models established in those segments. Finally, the Tennessee Eastman (TE) benchmark process is utilized to demonstrate the performance of the proposed process identification and monitoring strategy. Compared to previous works, the proposed algorithm is shown to be superior both in identification and monitoring.