Process Monitoring Control Research Articles

MXene-Graphene Oxide Heterostructured Films for Enhanced Metasurface Plasmonic Biosensing in Continuous Glucose Monitoring.

Non-invasive biosensors have attracted attention for their potential to obtain continuous, real-time physiological information through measurements of biochemical markers, such as one of the most important-glucose, in biological fluids. Although some optical sensing materials are used in non-invasivedevices for continuous glucose monitoring (CGM), surface or localized plasmon sensing material are seldom applied in CGM owing to modest sensitivity and bulk sensing apparatus. Herein, a metasurface (MGMSPR) biosensor based on the metasurface plasmon resonance chip modified with heterostructured Ti3C2 MXene-Graphene oxide (MG) is reported, which potentially enables ultra-sensitive glucose detection. The sensor consists of a dual-channel microfluidic device integrated with silver mirror enhanced MGMSPR chips. Not only does it promote the entry of glucose oxidase (GOD) into the internal pores and enhance the stable fixation of GOD in the membrane, but also the integration of MG material provides a high specific surface area and unique electronic properties, thereby significantly enhancing the sensitivity of the MGMSPR sensor. The detection limit of MGMSPR biosensor is 106.8µM. This pioneering approach opens new avenues for monitoring physiological parameters and process analytical technology on an optical platform, providing continuous health monitoring and production process control through optical sensors.

Design and Investigation of a High-Performance Quartz-Based SAW Temperature Sensor.

In this work, a surface acoustic wave (SAW) temperature sensor based on a quartz substrate was designed and investigated. Employing the Coupling-of-Modes (COM) model, a detailed analysis was conducted on the effects of the number of interdigital transducers (IDTs), the number of reflectors, and their spacing on the performance of the SAW device. The impact of the transversal mode of quartz SAWs on the device was subsequently examined using the finite element method (FEM). The simulation results indicate that optimizing these structural parameters significantly enhances the sensor's sensitivity and frequency stability. SAW devices with optimal structural parameters were fabricated, and their resonant frequencies were tested across a temperature range of 25-150 °C. Experimental results demonstrate that the SAW temperature sensor maintains high performance stability and data reliability throughout the entire temperature range, achieving a Bode-Q of 7700. Furthermore, the sensor exhibits excellent linearity and repeatability. An analysis of the sensor's response under varying temperature conditions reveals a significant temperature dependency on its Temperature Coefficient of Frequency (TCF). This feature suggests that the sensor possesses potential advantages for applications in industrial process control and environmental monitoring.

Integrative approach to fluorescence-based oxygen sensing in polymer optical fibers with surface plasmon resonance

This study demonstrates a technique to enhance oxygen (O2) concentration measurement by combining surface plasmon resonance (SPR) and fluorescence-based sensor on polymer optical fiber (POF). The SPR was generated using silver (Ag) and the fluorescence was generated by organic dye. Three fluorescence dyes were used, Tris (4, 7-diphenyl-1, 10-phenanthroline) ruthenium (II) dichloride ([Ru(dpp)3]2+), platinum octaethylporphyrin (PtOEP) and 5,10,15,20-tetrakis (pentafluorophenyl) 21 H,23H-porphine palladium (II) (PdTFPP). The sensor was tested in a gas chamber with different concentration level of O2. Sensitivity values for the fluorescence dyes for [Ru(dpp)₃]²⁺, PtOEP, and PdTFPP were 0.0099 a.u/%, 0.0343 a.u/% and 0.05 a.u/% respectively. When SPR was implemented, the sensitivity values for Ag/[Ru(dpp)₃]²⁺, Ag/PtOEP, and Ag/PdTFPP were 0.0589 nm/%, 0.0345 nm/%, and 0.118 nm/% respectively. PdTFPP and Ag/PdTFPP exhibit highest sensitivity in each experiment. Therefore, the further analysis for the sensing performance of PdTFPP and Ag/PdTFPP were held. As a result, Ag/PdTFPP exhibits lower limit of detection (LOD), and limit of quantification (LOQ) with 3.054 % and 9.254 %, respectively, with higher R2 of 0.9971 and lower mean standard deviation of 0.174, compared to PdTFPP 16.496 % and 49.988 % respectively, with R2 of 0.9211 and higher mean standard deviation of 2.003. This improvement can benefit researchers and professionals in medical diagnostics, environmental monitoring, and industrial process control by providing a more sensitive and accurate method for measuring O2 levels.

Manganese Oxide Applications in Sulfonamides Electrochemical, Thermal and Optical Sensors: A Short Review

AbstractIn recent years, the development of highly sensitive and selective electrochemical sensors has been a pivotal area of research, driven by the growing demand for environmental monitoring and industrial process control. Among various materials investigated for sensor applications, manganese oxide (MnO2) nanoparticles have garnered significant attention due to their excellent electrochemical properties, environmental friendliness, and natural abundance. Critical analyses of the synthesis of MnO2 using different techniques such as hydrothermal method, chemical precipitation, and sol–gel process which allows for the fine-tuning of particle size and morphology while enhancing the electrochemical sensing capabilities have been reviewed. The review also provides a comprehensive overview of the recent advancement evaluation of manganese oxide-based electrodes for detecting sulfonamides and other analytes in water across diverse matrices. This paper sets the stage for a comprehensive exploration of the synthesis methods and application areas of MnO2 nanoparticles in electrochemical sensors, highlighting their role in advancing sensor technology and their impact on various sectors. Graphical Abstract

Optimizing Quality Control on Electric Vehicle Production Lines with AI and Machine Learning

The study explores the use of AI and ML technologies to optimize quality control in EV production lines. By applying neural networks and machine learning algorithms, the research achieved significant improvements: a 74.4% reduction in torque deviation, a 75.9% enhancement in speed consistency, and a 70.8% decrease in defect rates. These gains also resulted in a 16.0% reduction in production cycle time and a 50.0% decrease in downtime, leading to an 8.4% increase in Overall Equipment Effectiveness (OEE). The methods employed included AI-driven predictive maintenance, real-time monitoring, and statistical process control (SPC). Despite the clear benefits, challenges such as integrating these technologies with existing systems and ensuring robust data infrastructure remain. Future research should focus on refining these approaches and extending their application across the automotive industry.

Cybersecurity and Compliance in Healthcare: A Study on Key Management and Other Regulatory Requirements

Typically, WSNs are implemented in several applications with various topologies. Nodes use wireless mode of communication, and unattended areas are typically selected for WSN deployment. Attack risk is higher in WSNs. Furthermore, the WSN nodes have severe resource constraints. It is quite difficult to provide security in such a setting. Numerous protocols for key management chores and encryption strategies for maintaining security in the WSN environment have been documented in literature. The criticality of the data being transferred over the network determines how complex the assault will be. Applications that monitor agriculture, for example, are safe from attacks. However, applications related to the military and healthcare could attract attackers who are far more skilled. Applications that involve process control and habitat monitoring attract attackers with medium to low skill levels. There isn't a single protocol available right now that could be flexible enough to meet the different requirements of the apps, which typically have variable security requirements. For these different applications, there are differences in the key management task, which is strongly related to the security requirements. As a result, the precise kind and complexity of the key management technique must be chosen and built in accordance with the necessary level of security, the capabilities of the hardware devices available, and the network topology that is used.

Accurate Ultrasonic Thickness Measurement for Arbitrary Time-Variant Thermal Profile.

Ultrasonic thickness measurement of mechanical structures is one of the most popular and commonly used nondestructive methods for various kinds of process control and corrosion monitoring. With ultrasonic propagation speed being temperature-dependent, the thickness measurement can be performed reliably only when the thermal profile is completely known. Most conventional techniques assume the temperature of the test structure is uniform and at room temperature across its thickness. Such assumptions may lead to large errors in the thickness measurement, especially when there are significant temperature variations across the thickness. State-of-the-art techniques use external temperature measurements or implement iterative methods to compensate for the unknown thermal profiles. However, such techniques produce unsatisfactory results when the heat distribution is complex or varies rapidly with time. In this work, we propose a two-sensors technique, using both compressive and shear excitations, with a non-iterative rapid data processing method for accurate thickness measurement under arbitrary time-variant thermal profile. The independent behavior of shear and compressive waves is used to formulate a real-time thickness estimation technique. The developed technique is experimentally validated on a steel plate with fixed acoustic sensors. Test results show that the error in thickness estimation can be reduced by up to 98% compared to conventional thickness gauging methods.

Generalized collaborative relevance vector regression for soft sensors

Soft sensors often struggle to adapt to changing conditions due to their overreliance on single data sources, severely undermining the generalizability and applicability of models. This paper proposes a novel generalized collaborative relevance vector regression (GCRVR) framework to substantially improve the accuracy and adaptability of soft sensors under varying conditions. The core innovation of GCRVR lies in an adaptive collaborative strategy designed to combine complementary predictive distributions based on analyzed variance differences, enabling the acquisition of more reasonable and reliable results. The GCRVR approach leverages both smoothed and unsmoothed process data to train independent relevance vector regression models, generating probabilistic predictions. A theoretical analysis provides insight into ensuring the stability of the model. In an evaluation conducted across four sophisticated industrial processes, the proposed GCRVR method outperformed a suite of conventional methods—namely, linear regression, support vector regression, Gaussian process regression, a stacked autoencoder, long short-term memory, attention-based long short-term memory, and extreme gradient boosting. The GCRVR method exhibited substantial predictive accuracy enhancements, as evidenced by average reductions of 39% in the root mean square error (RMSE), 43% in the mean absolute error (MAE), and 42% in the mean absolute percentage error (MAPE). This work addresses the generalization and adaptability limitations of soft sensors through an integrated framework that capitalizes on the collaborative use of complementary models. GCRVR constitutes a major advancement in real-time monitoring and optimization for industrial process control.

(Invited) Nanoarray Configured Monoliths for Self-Monitored Multi-Phase Reactive Processes

The incorporation of multiple functions synergistically into one device or platform can enable additions of application merits such as light weight, ease for system integration, reduced system complexity, cost-effectiveness, and programmability. This is especially true for usually complex energy, environmental, and biological systems with the integration need of many active or passive devices and components of various functionalities. In this work, we demonstrated a compact nanoarray configured monolith design with the ability to function as both a catalyst and a sensor to enable the self-monitoring of the complex multi-phase catalytic reaction processes during operation. A bimodular approach has been realized using both electrical and electrochemical interrogation modes for reactive gas and solid phase monitoring directly using the built-in catalysts. This new type of compact and built-in monolithic sensor configuration can lower the catalytic energetics and detect gas/solid composition semi-quantitatively at room temperature up to elevated temperature. Robust calibration curves of built-in sensor responses can be achieved with facile data processing and analyzing. The built-in monolith sensor shows good sensitivity and good selectivity sufficient for the self-monitoring of the reaction process. Besides, the nanoarray based monolith device has shown good environmental tolerance toward practical applications. Such compact monolithic device can serve as a self-monitored reactor, filter, and other function component that can potentially allow in-situ and real time monitoring and functional process control in complex and reactive physical and chemical environments. With further refining of the design and functional integration, this new type of smart monolithic device can provide necessary data and knowledge for future computation-driven situational awareness prediction and intelligent control of various complex dynamic processes and systems.

Colorimetric IPN hydrogels embedded with colloidal photonic crystals: A novel approach for the detection of ethanol and Ba2+ ions in water

A critical bottleneck in sensor technology is the rapid and precise detection of specific analytes in complex matrices, hindering advancements in environmental monitoring, healthcare, and industrial process control. This study addresses this challenge by introducing a novel composite hydrogel sensor designed for rapid and selective detection of ethanol and barium ions (Ba2+) in aqueous environments. The sensor integrates interpenetrating network (IPN) hydrogels with embedded colloidal photonic crystals (CPCs), synthesized via a solution-based polymerization approach. This innovative configuration allows CPCs to dynamically adjust their photonic bandgap in response to environmental changes, manifesting as a visible, colorimetric shift. This response stems from the synergy between the mechanical properties of the IPN hydrogel and the optical sensitivity of CPCs. Upon exposure to analytes such as ethanol and Ba2+, the sensor exhibits a rapid and reversible color transition that is directly proportional to their concentration. Notably, ethanol (0 vol%–80 vol%) and Ba2+ (5–17.5 mM) induce a distinct blueshift in the photonic bandgap and trigger a color change from red–orange to green due to the alteration in the swelling behavior of the IPN hydrogel, affecting its lattice constant. The IPN hydrogel–CPC composite demonstrates exceptional operational stability and facilitates rapid detection, making it ideal for on-site applications without the need for complex equipment. These characteristics make the composite hydrogel sensor a promising candidate for environmental monitoring, industrial process control, and public health diagnostics, paving the way for the development of next-generation responsive sensor materials.

Development of an optode-type chloride ion sensor utilizing optical fiber with silver film

This study delves into the development and assessment of a new chloride ion sensor employing optical fiber technology. It investigates the response characteristics of Ag-coated optode sensors, with a specific focus on film thickness and concentration dependency. Experimental results indicate that while film thickness offers sensitivity advantages, they are susceptible to fluctuations and detachment, thereby impacting reliability. To address this, the study suggests methods for correcting intensity fluctuations and improving sensor performance through calibration curves and spectrum measurements. Moreover, experiments demonstrate potential for enhancing sensitivity and resolution at low concentrations. These results highlight the superiority of the optode sensor method over existing techniques, particularly in sensitivity and adaptability for chloride ion monitoring. The study significantly contributes to sensor technology advancement, offering improved precision and reliability for environmental monitoring and industrial process control.

Extracting powder bed features via electron optical images during electron beam powder bed fusion

Electron beam powder bed fusion offers the unique opportunity to observe the process by measuring scattered electrons on a metal detector. This technique is the state of the art in generating electron optical images of the build area after melting using single- or multi-detector setups. The images enable the detection of surface defects like porosity or material transport by reconstructing the surface topography. Internal defects such as layer-bonding defects cannot be identified. Many of these defects, particularly layer-bonding defects, often originate from an irregular distribution of the powder bed.This work introduces an additional process step by recording an electron optical image after the distribution of the powder bed. Combining this with an electron optical image after melting the previous layer enables extraction of powder bed features such as the current powder bed height. The underlying method bases on the correlation of experimental measurements and numerical simulations of the intensity of the electron optical signal for different powder bed heights. With this approach, it is possible to identify irregular powder distributions, such as uncovered areas of previously molten material or locally varying powder bed heights. This information is crucial for online monitoring and real time process control. Exemplary, this opens the opportunity of healing the powder bed by an additional raking step.

Statistical parameterized physics-based machine learning digital shadow models for laser powder bed fusion process

This paper presents a statistical physics-based machine learning model for predicting defects, such as surface roughness and lack-of-fusion porosity, in the laser powder bed fusion of metals (PBF-LB/M) additive manufacturing process. The statistical physics-based model is calibrated and validated against controlled single-track experiments and used for statistical prediction for multi-layer and multi-track cases for PBF-LB/M defects. A mechanistic reduced-order-based stochastic calibration process is introduced to capture the stochastic nature of the melt pool. The calibrated physics-based digital shadow model is demonstrated for predicting the surface roughness of the National Institute of Standards and Technology (NIST) overhang part X4, with a difference of 9.3% compared to the experimental results. By leveraging data obtained from both the physics-based model and experiments, a machine learning model has been trained for fast predictions (inference time of 0.4 ms) with high accuracy (error bound of 6.7%). This model can predict melt pool geometries under various processing conditions, offering a control strategy for the PBF-LB/M process. Further, the trained machine learning model is showcased to demonstrate a control application of melt pool geometries (width and depth) for specific processing parameters. These developed models (physics-based and machine learning) serve as a digital shadow of the PBF-LB/M process, offering predictive capabilities to build a digital twin model for process control, optimization, and online monitoring.

Trends, progress and future directions of nanomaterial based sensors: a bibliometric overview

PurposeNanosensors have a wide range of applications because of their high sensitivity, selectivity and specificity. In the past decade, extensive and pervasive research related to nanosensors has led to significant progress in diverse fields, such as biomedicine, environmental monitoring and industrial process control. This led to better and more efficient detection and monitoring of physical and chemical properties at better resolution, opening new horizons in the development of novel technologies and applications for improved human health, environment protection, enhanced industrial processes, etc.Design/methodology/approachIn this paper, the authors discuss the application of citation network analysis in the field of nanosensor research and development. Cluster analysis was carried out using papers published in the field of nanomaterial-based sensor research, and an in-depth analysis was carried out to identify significant clusters. The purpose of this study is to provide researchers to identify a pathway to the emerging areas in the field of nanosensor research. The authors have illustrated the knowledge base, knowledge domain and knowledge progression of nanosensor research using the citation analysis based on 3,636 Science Citation Index papers published during the period 2011 to 2021.FindingsAmong these papers, the bibliographic study identified 809 significant research publications, 11 clusters, 556 research sector keywords, 1,296 main authors, 139 referenced authors, 63 nations, 206 organizations and 42 journals. The authors have identified single quantum dot (QD)-based nanosensor for biological applications, carbon dot-based nanosensors, self-powered triboelectric nanogenerator-based nanosensor and genetically encoded nanosensor as the significant research hotspots that came to the fore in recent years. The future trend in nanosensor research might focus on the development of efficient and cost-effective designs for the detection of numerous environmental pollutants and biological molecules using mesostructured materials and QDs. It is also possible to optimize the detection methods using theoretical models, and generalized gradient approximation has great scope in sensor development.Research limitations/implicationsThe future trend in nanosensor research might focus on the development of efficient and cost-effective designs for the detection of numerous environmental pollutants and biological molecules using mesostructured materials and QDs. It is also possible to optimize the detection methods using theoretical models, and generalized gradient approximation has great scope in sensor development.Originality/valueThis is a novel bibliometric analysis in the area of “nanomaterial based sensor,” which is carried out in CiteSpace software.

Effect of overheat and direct flow loading on the MEMS bistable flow sensor

We present the findings from an experimental study of a MEMS flow sensor in which an initially curved, double-clamped bistable microbeam is the primary sensing element. Our research explores how the overheat ratio, direct flow loading, and turbulence-induced vibration affect the sequential snap-through (ST) buckling and snap-back (SB) release of an electrostatically actuated beam heated by an electric current. The sensor is fabricated from highly doped single-crystal silicon using a silicon-on-insulator (SOI) wafer. Positioned at the chip’s edge, the microbeam is exposed to airflow, enabling concurrent dynamic response measurements with a laser Doppler vibrometer and a video camera.Our research demonstrates that the overheat ratio can be significantly lower for this sensing principle than conventional thermal sensing elements, pointing to the potential for substantial energy savings. We also emphasize the significant impact of flow angles and vibrations on the critical ST and SB voltages, which are vital for the flow sensor’s output. Additionally, we introduce the first direct experimental observation of the beam profile’s time history during the snap-through/snap-back transition.The potential impact of this research lies in developing more robust MEMS flow sensors with enhanced sensitivity and a better understanding of their response to environmental factors, which could have broader applications in fields such as aerospace, environmental monitoring, and industrial process control.

Characterization and Calibration of Cold-Alcohol Plasma Fractionation Intermediates by Kjeldahl Digestion and Digital Refractometry

Background Almost 80 years after implementation of the industrial ethanol fractionation process, based on the pioneering work of Edwin J. Cohn's research group, it has not lost its importance in providing life-saving biotherapies to patients. The focus has shifted from albumin, which was first used to treat intensive care patients, to immunoglobulin G preparations. Nowadays the latter enables effective, life-long treatment of patients suffering from immunodeficiencies. Ethanol concentration, pH, temperature, protein, and salt concentration are diligently varied during the Cohn fractionation process to finally yield fractions in which the main plasma proteins are enriched at adequate purity. Total protein concentration of the intermediates, probably not as important as ethanol levels and pH, has nevertheless an indisputable influence on the process’ performance Objectives Total protein measurement is therefore a valuable in process-control for versatile process performance monitoring. For this purpose, we investigated the application of analytical digital refractometry. Design For our pilot study, we used six consecutive batches of nine relevant intermediates covering Takeda's whole industrial-scale Cohn Fractionation process. Methods Total protein concentrations of the intermediates were measured with the method of Kjehldal to obtain reference data and digital analytical refractometry, using 10-kDa ultrafiltration to obtain a protein free permeate serving as a sample-specific blank. Results and Conclusions Specific refractive index increment dn/dc values, allowing the calculation of the total protein concentrations by refractive index measurement, were obtained for the Cohn Fractionation intermediates investigated. Thus, analytical digital refractometry, a simple, fast, and robust technique, was shown to be fit for this purpose.

Advanced Process Monitoring Through Fault Detection and Classification for the Process Development of Tantalum Nitride Thin-Film Resistors

This paper discusses the optimization of an advanced process monitoring scheme with interdicting fault detection and classification (FDC) capabilities that improved the control over the process development of Tantalum Nitride thin film resistors (TaN TFR). Its implementation in a high-volume manufacturing environment resulted in a reduction of misprocessed wafers, shorter equipment downtime, higher throughput, enhanced process visibility, and yield improvement. Along with optimizing FDC capabilities, implementing a short loop sampling plan reduced the turnaround time for early electrical characterization by a factor of four; this allowed for timely inline adjustments within the fabrication process to tighten the statistical process control (SPC) over the distribution of the process control monitor (PCM) TaN resistor value and improve thin film uniformity.

Multisensor data fusion and machine learning to classify wood products and predict workpiece characteristics during milling

The wood industry demands advanced methods for material classification and workpiece characteristic modelling to enhance process monitoring and adaptive process control. This paper presents a sensor fusion approach that integrates data from acoustic emissions, airborne sound, and power consumption during the milling of solid wood and wood-based composites. The aims are to achieve accurate material classification and to model workpiece characteristics such as surface roughness or density. A design matrix was generated by extracting relevant features from the multimodal signals to serve as an input for the classification and regression algorithms. The tested classification approaches to differentiate between workpiece type demonstrated high precision with an average validation accuracy of 92.16 %. Regression models for predicting the surface roughness showed R2 values between 0.79 and 0.97. The density could be predicted with R2 values between 0.84 and 0.98. As a conclusion, workpiece types could be classified and important workpiece properties during machining, such as surface roughness and density, could be well described by using information from multiple sensors during machining.

Research on Visualization Technology of Production Process for Mechanical Manufacturing Workshop

The visualization of workshop information can affect production management and efficiency. Information can be presented both graphically and non-graphically (for example, in the form of data lists or tables). Graphical representations are intuitive and clear, but currently, most of them are based on statistical data, which makes it difficult to convey logical linkages between information and cannot help managers make decisions effectively. With the aim of designing the workshop production system with visual processes in small-sized enterprises, the key visualization technologies of the process flow chart, including the visual design of process flow chart, process card management, process flow chart release, process control, and production schedule monitoring, were all addressed in detail. On this basis, the mechanical manufacturing workshop production management system was created using C#.NET as the programming language. The main contribution of the research is that the system designed used the process flow chart as the main line through all functional modules and integrated all process data on the process nodes of the process flow chart to realize the graphical monitoring of workshop production schedule. The visualization technology of the process flow chart makes the system simple to use and easy to understand, which significantly improves information management and work efficiency in the workshop. Additionally, it provides the technical foundation for flow-driven production information transfer in the workshop and can serve as a universal standard for the process module in workshop production management systems.

Statistical process control versus deep learning for power plant condition monitoring

This study compares four models for industrial condition monitoring including a principal components analysis (PCA) approach and three deep learning models, one of which is a new, lightweight version of another. We also propose a simple attention mechanism for enchancing deep learning models with better predictions and feature importance. Two datasets are used, one simulated from the Tennessee Eastman Process, the other from two feedwater pumps at a Danish combined heat and power plant. Our final results show evidence in favour of the PCA-based approach as it has detection ability comparable to the deep learning approaches as well as faster training time, fewer hyperparameters, as well as robustness to changing operating conditions. We conclude the paper by putting into perspective the importance of building up complexity incrementally with a recommendation to start modelling with simpler and well-tested models before the adoption of more advanced, less transparent models.