Process Monitoring Research Articles

The development of low-cost spin coater with wireless IoT control for thin film deposition

A low-cost spin coater with a wireless remote system that can deposit thin films of uniform thickness and quality at a significantly lower cost than traditional methods. The system consists of three main parts, a motorized spindle, a spin-coating head, and a control system connected to the network. The mechanical design on the mechanical part, spin coater system design with ESP32, and implementation of wireless control through visual basic. The network-enabled control system allows for real-time monitoring and adjustment of the deposition process, which can improve efficiency and reproducibility. This low-cost spin coating system represents a promising solution for organizations seeking to access thin film deposition technology at a fraction of the cost of traditional systems. By integrating wireless IoT control into low-cost spin coaters, the impact of this technology on coating uniformity will provide valuable insights for future advancements in this field.

Personalized feature extraction for manufacturing process signature characterization and anomaly detection

Manufacturing process signatures reflect the process stability and anomalies that potentially lead to detrimental effects on the manufactured outcomes. Sensing technologies, especially in-situ image sensors, are widely used to capture process signatures for diagnostics and prognostics. This imaging data is crucial evidence for process signature characterization and monitoring. A critical aspect of process signature analysis is identifying the unique patterns in an image that differ from the generic behavior of the manufacturing process in order to detect anomalies. It is equivalent to separating the “unique features” and process-wise (or phase-wise) “shared features” from the same image and recognizing the transient anomaly, i.e., recognizing the outlier “unique features”. In state-of-the-art literature, image-based process signature analysis relies on conventional feature extraction procedures, which limit the “view” of information to each image and cannot decouple the shared and unique features. Consequently, the features extracted are less interpretable, and the anomaly detection method cannot distinguish the abnormality in the current process signature from the process-wise evolution. Targeting this limitation, this study proposes personalized feature extraction (PFE) to decouple process-wise shared features and transient unique features from a sensor image and further develops process signature characterization and anomaly detection strategies. The PFE algorithm is designed for heterogeneous data with shared features. Supervised and unsupervised anomaly detection strategies are developed upon PFE features to remove the shared features from a process signature and examine the unique features for abnormality. The proposed method is demonstrated on two datasets (i) selected data from the 2018 AM Benchmark Test Series from the National Institute of Standards and Technology (NIST), and (ii) thermal measurements in additive manufacturing of a thin-walled structure of Ti–6Al–4V. The results highlight the power of personalized modeling in extracting features from manufacturing imaging data.

Wasserstein Local Slow Feature Analysis and Its Application for Process Monitoring

Abstract Complex industrial processes are commonly characterized by dynamics, which results from the fact that there exists compensation of the closed-loop control and complex reflux during process operations. In this work, we propose a new method termed Wasserstein local slow feature analysis approach (WLSFA) used for monitoring the dynamic process, which can learn slowly varying information to capture the trend of process variations and characterize dynamics. Specifically, Wasserstein graph embedding based on optimal transport is proposed to preserve the local geometrical structure of raw data so that the reduced-dimensional representations can more faithfully reveal the underlying information of data, enhancing monitoring performance. Moreover, the l_2 -norm orthogonal constraint is incorporated into the objective function to improve generalization ability and alleviate the false alarm rate. On the basis of WLSFA model, a contribution plot in the sense of reconstruction is developed to locate the fault variables, which is beneficial to regulate abnormal conditions towards normal levels. Finally, the efficiency of the proposed approach is illustrated by a benchmark process and the application to a real-world fractionation process.

A lightweight sensor ontology for supporting sensor selection, deployment, and data processing in forming processes

AbstractIn the era of smart manufacturing, modern manufacturing systems face high demands for enhancing process performance and reducing machine downtime. Sensors and process data are essential for successfully implementing data-driven approaches to guarantee robust and reliable process monitoring, tool conditioning, or quality assurance. However, the accuracy and performance of such approaches are highly dependent on the quality of the gathered sensor data and influenced by the implemented data acquisition and processing methods. For this purpose, this work proposes a lightweight sensor ontology to provide a comprehensive overview to characterize underlying relationships between the physical environment and the quality of the data sets. The extended sensor ontology, in combination with domain knowledge, aims to support engineers in fully exploiting the potential of sensor data to obtain trustworthy data sets in forming technologies. As a result, this approach can improve the implementation of automated and data-driven process monitoring of forming systems and tools.

Dielectric Monitoring of Microwave Extraction Processes

Our research investigated the dielectric traceability of microwave and ultrasound intensified extraction processes of plant by-products. For the experiments, I used plant by-products from beetroot, carrot and raspberry, which were extracted in 5% suspensions. The dielectric behaviour of the extracts was investigated in the frequency range 300-2400 MHz using an open-ended coaxial probe. For both the ultrasonic and microwave intensified procedures, the reported energy was 30, 45 and 60 kJ, respectively. Based on my research results, I found that the dielectric constant measured in the frequency range 700-900 MHz is closely related to the yield of total polyphenol and pectin regardless of the feedstock and treatment used, but the method has limitations due to the presence of components that affect the physical structure and the concentration of target components below the limit of detection.

Ultrasonic mode conversion for in-line foam structure measurement in highly aerated batters using machine learning

AbstractAn ultrasonic-based method was developed to enable in-line measurements of foam structure parameters for highly aerated batters by mode conversion. Biscuit batters were foamed to different degrees (density: 364–922 g/L) by varying the mixing head speed and pressure. Density and foam structure changes were detected by efficient offline analytics (nref measurement = 96). Ultrasonic signal data were recorded using two ultrasonic sensors attached to an industry-standard tube. Mode conversion effects in the ultrasonic signals were obtained to predict the rheological parameters of the batters. The frequency range in which surface waves are expected was particularly suitable for detecting rheological changes in highly aerated batters. An ultrasonic-based, online-capable method for process monitoring was implemented and established regarding feature selection in combination with machine learning and 5-fold cross-validation. The developed ultrasonic sensor system shows high accuracy for online density measurement (R2 = 0.98) and offers decent accuracy for measurements of foam structure parameters (Bubble count: R2 = 0.95, Relative span: R2 = 0.93, Sauter diameter: R2 = 0.83). The main benefit of this novel technique is that integrating ultrasonic signal features based on mode conversion leads to a robust foam structure analysis, which has the advantage of being retrofitable into existing processes.

Integrity monitoring for decentralized redundant IMUs/GNSS integrated navigation system with correlated measurements

In civil aviation systems, the inertial measurement unit (IMU)/Global Navigation Satellite System (GNSS) integrated navigation remains the most effective navigation scheme. Based upon this, the utilization of redundant IMUs and a multi-constellation system can significantly enhance navigation and integrity performance. However, redundant IMUs/GNSS integrated navigation systems are susceptible to more complex failure modes, including GNSS faults (satellite and constellation faults) and IMU faults. Additionally, dealing with correlated measurements between redundant constructions presents a challenging issue. Therefore, we propose an integrity monitoring method for decentralized redundant IMUs/GNSS integrated navigation systems. This method considers GNSS faults and IMU fault in the integrity monitoring process, specifically designed for decentralized filter construction with redundancy. Furthermore, due to the correlated measurements between redundant filters, optimal fusion coefficients are determined by considering the unbiased estimate and minimizing the trace covariance of the primary filter. An optimal allocation scheme for continuity risk is also established to improve navigation and integrity performance. Simulation results demonstrate the feasibility and effectiveness of the proposed integrity monitoring method. Moreover, in the context of correlated measurements, the integrated navigation system also exhibits superiority.

Optimizing Zakat Management to Improve Education Quality in Palu City, Central Sulawesi, Indonesia

This study aims to discuss the zakat management optimization in improving education quality at National Zakat Charity Agency (BAZNAS) of Palu. This study used a qualitative approach and the data was gathered through field observation, in-depth interviews, and written document analysis. The data analysis, then, was analysed through data reduction, data presentation, and conclusion drawing. The results of this study show that the process of optimizing zakat to improve education quality at Baznas Palu city was conducted according to Islamic zakat management principles which includes planning, implementation, reporting, and monitoring process in the program of collection, distribution, and utilization at National Zakat Charity Agency Palu. The agency also follows professional administration principles such as transparency, accountability, responsibility, independence, fairness, and Islamic shariah compliance. This study also found that the agency has focused zakat distribution for specific educational institutions, both formal and informal. The agency also provide scholarships for academic achievement, and educational cost assistance for underprivileged children. Furthermore, the agency provides capital support for productive businesses runned by disadvantaged families or communities to improve their incomes.

Optimization of the concentration of ozone generated by DBD using PSO algorithm for water treatment process

The water treatment process with ozone is influenced by various operating parameters and environmental factors that can impact its efficiency. In this study, experiments were conducted using a Venturi pumping frame to investigate the effects of three controllable variables: oxygen flow height, applied voltage level, and water flow rate. The tests aimed to develop a mathematical model that accurately represents the relationship between these input variables and the resulting ozone concentration in the treated water. The experimental data was analyzed using the MODDE 5.0 software, a specialized application for statistical modeling and design of experiments. By fitting the data to appropriate model equations, a mathematical model was obtained that quantifies the influence of each variable and their interactions on the ozone concentration response. To optimize the process performance, a particle swarm optimization (PSO) algorithm was employed to extract the best-fit parameters for the mathematical model. PSO is a computational technique inspired by the social behavior of bird flocks or fish schools, utilizing a population of candidate solutions that evolve iteratively to converge on the global optimum solution. In this case, PSO searched for the model parameter values that minimized the error between predicted and experimentally measured ozone concentrations, rapidly converging to an accurate solution. The optimized mathematical model enables predicting the ozone concentration under any combination of oxygen flow height, voltage, and water flow rate within the experimental range. This predictive capability facilitates identifying the optimum operating conditions to maximize ozone concentration, thereby enhancing the efficiency of the water treatment process. The model serves as a valuable tool for process control, monitoring, and optimization, ensuring consistent treatment quality while minimizing resource consumption and operational costs.

Tensile testing data of additive manufactured ASTM D638 standard specimens with embedded internal geometrical features

Additive manufacturing (AM) is now widely used for research and industrial production. The benchmark data for mechanical properties of additively manufactured specimens is very useful for many communities. This data article presents a tensile testing dataset of ASTM D638 size specimens without and with embedded internal geometrical features printed using polylactic acid (PLA) in a Fused Filament Fabrication (FFF) additive manufacturing process. The added features can mimic defects of various shapes and sizes. This work is a supplement to the published research article Assisted defect detection by in-process monitoring of additive manufacturing using optical imaging and infrared thermography (Additive Manufacturing, 2023, 103483). The printed specimens were tensile tested. Stress-strain graphs were developed and used to calculate the mechanical properties such as ultimate tensile strength (UTS) and strain at UTS. The mechanical properties, the correlations between mechanical properties and size, shape and location of geometrical features (defects), and the trends in mechanical properties can be useful in benchmarking the results of other researchers.

Prediction of mechanical properties of LPBF built part based on process monitoring and Gaussian process regression

As a highly promising technology in additive manufacturing, the laser powder bed fusion has only limited application due to its low reproducibility. In this study, the image information of the 316L specimen after laser scanning and powder paving of each layer was acquired by a complementary metal–oxide–semiconductor industrial camera. The important features were selected, extracted and quantificated by analyzing the tensile test results. Finally, combined with the laser power, the quantified features were as input of a Gaussian process regression model based on optimization algorithm of grid search to predict the tensile strength of 316L specimen. The results show that the quantized image features have a significant improvement on the regression effect, and the coefficient of determination (R 2) is improved from 63% to 90.57% compared to using only the laser power as input.

Design and implementation of a shrimp pond monitoring system using Internet of Things

In 2020, the volume of fishery exports will reach 1.26 billion kilograms (kg) with a value of US$ 5.2 billion. Shrimp became the commodity with the most exports, namely 239.28 million kilograms with a value of US$ 2.04 billion. In 2020 shrimp aquaculture production in Indonesia reached 911.2 thousand tons, shrimp also contributed to the total export volume of fishery products by 18.95%. Vaname shrimp production is also targeted to reach 2 million tons in 2024, some of which will be for export. In an effort to achieve this target, a system is needed that is capable of monitoring and controlling shrimp ponds using the Internet of Things (IoT). Because the monitoring process that is carried out traditionally will take time and be less efficient. Therefore, it is necessary to develop a system for monitoring and controlling shrimp ponds automatically using IoT. Sensors and microcontrollers are used to read and control input and output data such as pH, temperature, salinity, turbidity, light intensity, water level, and actuators for filling and draining pumps. This system will also be equipped with an expert system to be able to perform data analysis of measured parameters. This research was carried out within 2 years. In has been carried out the development and manufacture of hardware on the monitoring and control system. In the second year, hardware and software integration will be carried out to be able to monitor, control, and analyze data as decision support and implementation in the actual system. The purpose of this research is to design and build a monitoring and control system for shrimp ponds based on the Internet of Things.

Immersive Digital Twin under ISO 23247 Applied to Flexible Manufacturing Processes

Digital twin (DT) technology provides a path for implementing cyber–physical systems (CPS) and developing smart manufacturing because they are essential tools for monitoring and controlling manufacturing processes. It is considered a vital technology in smart manufacturing and is being widely researched in academia and industry. Furthermore, the combination of DTs and immersive environments has shown great potential for integrating novel capabilities into the new generation of CPS. This research presents an architecture for implementing immersive digital twins under ISO 23247 in flexible manufacturing processes. The proposed system is based on the integration of DT technologies in conjunction with augmented reality (AR) and gesture tracking, and validation was performed in the sorting station of the MPS 500 to increase the interaction and flexibility between physical and virtual environments in real time, thus enhancing the capabilities of the DT. The methodology used for the design and implementation of the DT includes (1) general principles and requirements; (2) models with functional views based on domains and entities; (3) attributes of the observable manufacturing elements; and (4) protocols for the exchange of information between entities. The results show that the integration of these technologies improves the monitoring, control, and simulation capabilities of processes using 3D resources and immersive environments, achieving a higher level of interactivity. In addition, error detection tests were carried out, where a reduction of time was observed in the resolution of errors that may be caused by internal or external disturbances of the process, thus avoiding production delays.

Statistical process monitoring creates a hemodynamic trajectory map after pediatric cardiac surgery: A case study of the arterial switch operation

AbstractPostoperative critical care management of congenital heart disease patients requires prompt intervention when the patient deviates significantly from clinician‐determined vital sign and hemodynamic goals. Current monitoring systems only allow for static thresholds to be set on individual variables, despite the expectations that these signals change as the patient recovers and that variables interact. To address this incongruency, we have employed statistical process monitoring (SPM) techniques originally developed to monitor batch industrial processes to monitor high‐frequency vital sign and hemodynamic data to establish multivariate trajectory maps for patients with d‐transposition of the great arteries following the arterial switch operation. In addition to providing multivariate trajectory maps, the multivariate control charts produced by the SPM framework allow for assessment of adherence to the desired trajectory at each time point as the data is collected. Control charts based on slow feature analysis were compared with those based on principal component analysis. Alarms generated by the multivariate control charts are discussed in the context of the available clinical documentation.

METHODS AND TOOLS FOR IN-DEPTH ANALYSIS OF ENERGY CHARACTERISTICS AND MANAGEMENT OF ENERGY CONSUMPTION IN EDUCATIONAL INSTITUTIONS

A factor of the state's national security is reducing energy dependence and taking measures to increase energy efficiency, especially in the budgetary sphere, because about 90% of such buildings do not meet modern energy efficiency requirements. Creating a reliable energy consumption management system, choosing measures to reduce consumption and diversifying energy sources is a difficult task that requires consideration of building energy consumption processes as a complex problem with the use of modern tools of the energy management system, modeling, and the use of modern approaches to automation and measurements. The object of research is energy consumption processes in educational institutions. The subject of the research is energy characteristics and methods of energy consumption management in educational institutions. Research methods. analytical methods, system analysis, synthesis, modeling, systematization, method of comparative and structural analysis. One of the tasks of this study is to review the capabilities of software products that can be used by energy managers of educational institutions to monitor, analyze and forecast energy consumption, as well as to create reports. Also in the work, an analysis of the state of affairs regarding energy consumption management was carried out on the example of KPI named after Igor Sikorskyi; proposals for the development of the automated energy monitoring system were provided, in particular, the architecture of the web application was developed taking into account the support of existing databases to improve the energy monitoring process.

An ecologically sound and participatory monitoring network for pan-Arctic seabirds.

In a warming Arctic, circumpolar long-term monitoring programs are key to advancing ecological knowledge and informing environmental policies. Calls for better involvement of Arctic peoples in all stages of the monitoring process are widespread, although such transformation of Arctic science is still in its infancy. Seabirds stand out as ecological sentinels of environmental changes, and priority has been given to implement the Circumpolar Seabird Monitoring Plan (CSMP). We assessed the representativeness of a pan-Arctic seabird monitoring network focused on the black-legged kittiwake (Rissa tridactyla) by comparing the distribution of environmental variables for all known versus monitored colonies. We found that with respect to its spatiotemporal coverage, this monitoring network does not fully embrace current and future environmental gradients. To improve the current scheme, we designed a method to identify colonies whose inclusion in the monitoring network will improve its ecological representativeness, limit logistical constraints, and improve involvement of Arctic peoples. We thereby highlight that inclusion of study sites in the Bering Sea, Siberia, western Russia, northern Norway, and southeastern Greenland could improve the current monitoring network and that their proximity to local populations might allow increased involvement of local communities. Our framework can be applied to improve existing monitoring networks in other ecoregions and sociological contexts.

In situ Electrical Impedance Tomography for Visualizing Water Transportation in Hygroscopic Aerogels.

The global water crisis demands immediate attention, and atmospheric water harvesting (AWH) provides a viable alternative. However, studying the real-time subtle relationship between water absorption, diffusion, and internal structure for hygroscopic materials is challenging. Herein, a dynamic visualization technique is proposed that utilizes an in situ electrical impedance tomography (EIT) system and a precise reconstruction algorithm to achieve real-time monitoring of the water sorption process within aerogels from an internal microstructural perspective. These results can be inferred that composites' pore sizes affecting the kinetics of their moisture absorption. In addition, the diffusion path of moisture absorption and the distribution of stored moisture inside aerogels exhibit intrinsic self-selective behavior, where the fiber skeleton of the aerogel plays a crucial role. In summary, this work proposes a generic EIT-based technique for the in situ and dynamic monitoring of the hygroscopic process, pointing to an entirely new approach regarding research on AWH materials.

Implementasi Whistleblowing System dalam Meningkatkan Efektivitas Pengaduan Masyarakat (Studi Pada ACRC Korea)

In disclosing allegations of wrongdoings or corruption, an effective whistleblowing reporting and handling system is required as a form of implementing good corporate governance in public services carried out by the government. By conducting a study on the implementation of the complaint system and whistleblowing system at the Anti-Corruption & Civil Rights Commission of Korea (ACRC), this research aims to determine and analyze the implementation of the whistleblowing system and the factors that influence the effectiveness of complaint management. Through a systematic literature review using PRISMA 2020 and organizational capacity analysis by using Douglas Horton’s theory, it is concluded that a complaints system must be supported by policies and strategies from top management, human resources, technological infrastructure support, monitoring and evaluation processes, and networks as an effort to increase capacity in order to create a complaint handling system and effective policy. In addition, a policy of reward and protection for whistleblowers is needed which supported both internally and through collaboration with other agencies so that whistleblowers feel safe in reporting suspected wrongdoings.

In-Process Monitoring of Inhomogeneous Material Characteristics Based on Machine Learning for Future Application in Additive Manufacturing

In-Process Monitoring of Inhomogeneous Material Characteristics Based on Machine Learning for Future Application in Additive Manufacturing

An Immersive Digital Twin Applied to a Manufacturing Execution System for the Monitoring and Control of Industry 4.0 Processes

The present research proposes the implementation of an architecture for industrial process monitoring and control for a manufacturing execution system (MES) using an immersive digital twin (DT). For the design of the proposal, cyber–physical systems (CPS), MES, robotics, the Internet of Things, augmented reality, virtual reality, and open platform communication-unified architecture (OPC UA) communication protocols were used to integrate these technologies and enhance the functionalities of the DT by providing greater performance. The proposed work is implemented in an Industry 4.0 laboratory that is composed of Festo Cyber–Physical Factory and CP-Lab stations. The implementation of the architecture is based on ISO 23247, where the following requirements were considered for the design of DTs: (1) observable attributes and 3D design and visualization of all physical production lines in all of their stages, (2) a communication entity through the OPC UA protocol for the collection of state changes of manufacturing elements, (3) a DT entity where digital models are modeled and updated based on the collected data, and (4) user entities through the use of AR and VR to make manufacturing more efficient. The experimental results showed that the architecture enables interoperability between different platforms and control subsystems. It allows for the detection and diagnosis of problems during the execution of the production line; in addition, the high-fidelity simulation and AR and VR environments provided by the DT with data obtained in real time can improve the accuracy and efficiency of manufacturing through a more detailed analysis of the process, providing advantages such as interactive creation for customized products and continuous innovation.