Monitoring Of Production Processes Research Articles

Closed-Loop Manufacturing with AI-Enabled Digital Twin Systems

This research provides an in-depth analysis of recent literature on Closed-Loop Manufacturing with AI-Enabled Digital Twin Systems, focusing on the integration of Artificial Intelligence (AI) and Digital Twin technologies within modern manufacturing environments. Through a systematic literature review of studies sourced from leading academic databases such as Scopus, Web of Science, IEEE Xplore, Science Direct, Springer Link, and Google Scholar, this research examines how these advanced technologies are being used to optimize production processes, improve operational efficiency, and reduce costs. The review synthesizes key findings related to real-time data collection, predictive maintenance, and quality control, highlighting the role of AI in enabling self-regulating and self-improving manufacturing workflows. The application of AI and Digital Twin systems in closed-loop manufacturing facilitates enhanced decision-making through continuous feedback loops. These systems allow manufacturers to simulate, predict, and monitor production processes in real-time, enabling proactive maintenance, process optimization, and better-quality control. Moreover, the integration of AI allows for the dynamic adjustment of manufacturing parameters, reducing waste and improving resource utilization. This research identifies and highlights the potential of AI and Digital Twin technologies in driving sustainability and flexibility in manufacturing operations. However, the research also identifies several challenges in implementing AI-enabled Digital Twin systems, including data integration issues, high initial investment costs, and cybersecurity risks. The shortage of skilled professionals capable of managing these advanced systems further hinders widespread adoption. Based on the synthesis of current literature, the research concludes with recommendations for overcoming these obstacles, offering insights into the future of closed-loop manufacturing systems and their potential to transform industrial production processes.

DEVELOPING WORK ORDER MANAGEMENT SYSTEM WITH CIRCULAR ECONOMY APPROACH USING ASAP METHOD

CV PG STTT is a garment manufacturing company that specializes in producing chemical laboratory coats and technical PDL (Personal Duty Line) shirts. The company operates on a make-to-order system. However, its production processes remain entirely manual, resulting in challenges related to production monitoring, tracking, and recording. Furthermore, the company still adheres to a linear economy model, where production waste is discarded without being repurposed. To address these issues, the implementation of an extended work order management system using a circular economy approach offers a viable solution. This approach not only minimizes production waste but also enhances the monitoring of production processes. The extended work order management system is developed using the Accelerated SAP (ASAP) methodology. The research follows several stages, including observation, interviews, analysis, and the proposal of a system design through configuration and adjustment of manufacturing modules, followed by testing the manufacturing modules. The outcome of this study is an ERP system on Odoo with customized module to align with the company's business processes and proposed workflows. This system simplifies production processes at CV PG STTT while promoting sustainability and supporting the transition to a circular economy.

Digital Twins: Strategic Guide to Utilize Digital Twins to Improve Operational Efficiency in Industry 4.0

The Fourth Industrial Revolution, known as Industry 4.0, has transformed the manufacturing landscape by integrating advanced digital technologies, fostering automation, interconnectivity, and data-driven decision-making. Among these innovations, Digital Twins (DTs) have emerged as a pivotal tool, enabling real-time monitoring, simulation, and optimization of production processes. This paper provides a comprehensive exploration of DT technology, offering a strategic framework for its effective implementation within Industry 4.0 environments to enhance operational efficiency. The proposed methodology integrates key enabling technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning to create accurate digital replicas of manufacturing systems. Through a detailed case study, this work demonstrates how DTs can optimize production processes, reduce downtime, and improve maintenance strategies. The findings highlight DTs’ transformative potential in achieving continuous improvement, competitiveness, and operational excellence. This research aims to provide organizations with actionable insights and a roadmap to leverage DT technology for sustainable industrial innovation.

Automated on-line monitoring of a lithium hydroxide production process using Micro-Discharge OES

The fast, precise and continuous on-line analysis of highly saline process solutions is challenging for conventional laboratory techniques like ICP-OES or ICP-MS due to the necessity of high plasma gas...

Semi-supervised learning based on temporal-spatial adaptive algorithm and its recognition mechanism for carbonate ester production process monitoring

Semi-supervised learning based on temporal-spatial adaptive algorithm and its recognition mechanism for carbonate ester production process monitoring

STRUCTURAL AND FUNCTIONAL PROTOTYPING OF THE EXECUTIVE PART OF THE ANGULAR MANIPULATOR ROBOT

In today's conditions of total digitization and informatization of all spheres of activity, more and more agro-industrial enterprises direct their resources to the development and implementation of modern technical and technological solutions, to modernize their own production capacities, with the prospect of ensuring competitiveness on domestic and foreign markets. In turn, such technological renewal of the agricultural sector of the economy is usually based on the use of high-tech systems of management, monitoring, control, management and automation of production processes, with the use of highly integrated SMART systems and the involvement of artificial intelligence technologies. Taking into account the significant volumes of cargo processing, agricultural enterprises, in particular in warehouses, a promising direction and reserve for ensuring high labor productivity is the use of robotic manipulators, and research is devoted to increasing their functionality and maneuverability, including through the use of intelligent control systems and optimization of the structure of executive mechanisms. relevant and will have practical value. The article substantiates the conditions for ensuring functional and structural similarity and proposes criterion equations of similarity for a large-scale transition from a natural sample of an industrial manipulator to a physical model-prototype. On the basis of the obtained criteria and using the scaling factors, the design parameters of the prototype were determined, a computer 3D model was developed, and based on the analysis of the trajectories, corrections were made to the linear dimensions of the manipulator and the ranges of angular movements of the links, as well as the compliance of the created prototype with the conditions of physical modeling was checked, according to the criteria of similarity. Also, the article presents the process of manufacturing an angular manipulator by using additive technologies of layer-by-layer build-up of parts on a 3D printer.

CUSUM Control Chart for Symlets Wavelet to Monitor Production Process Quality.

CUSUM Control Chart for Symlets Wavelet to Monitor Production Process Quality.

Research on resistivity characteristics of grain-coating and pore-filling hydrates in different sediment packings based on voxelated 3D models

Natural gas hydrate is a clean energy resource with abundant reserves. Before conducting large-scale exploitation, understanding its physical properties, such as electrical properties, is crucial for gas reserve evaluation and production process monitoring. Due to the complexity of the pore space geometry and different hydrate growth morphology, electrical resistivity may vary for different samples at the same hydrate saturation. This study considered three packings of unconsolidated marine sediment grains (simple cubic, body-centered cubic, and face-centered cubic) and two end distribution patterns of hydrates in pores (grain-coating and pore-filling). The original 3D geometries of hydrate-bearing models were replaced with voxelated meshes to achieve better stability and versatility. The resistivities of models were calculated using the finite element method and compared with the results from Archie's law. The simulation results show that the resistivity curve's shape is jointly affected by the hydrate distribution pattern and the initial pore space geometry. A rational function can be used to fit the saturation exponent curves to give critical brine saturations. The grain-coating hydrate prefers blocking the throats and has a critical brine saturation above 0. In contrast, the pore-filling hydrate has resistivities lower than the Maxwell-Garnett equation's lower boundary due to a bypassing effect, which partially compensates for the conductivity loss of the pore fluid and is more remarkable for models distributed by bigger hydrate particles, and has a critical brine saturation equal to 0. The findings are helpful in understanding the non-Archie phenomenon of hydrate-bearing sediments.

CUSUM charts utilizing reparametrized Birnbaum-saunders model for fault detection and process control

Statistical process control is always intrigued by the design of effective control charts for monitoring production processes and determining assignable causes of variations. It can be challenging to keep track of a positive asymmetric response variable while considering the impact of the input variables. The current work incorporates the Reparametrized Birnbaum Saunders (RBS) model to develop more effective cumulative sum (CUSUM) control charts for analyzing the mean of such a process. We perform a simulation study to evaluate the effectiveness of existing and derived approaches in terms of run length characteristics. The findings showed that when the underlying process distribution is positively asymmetric, the proposed charts provide stronger protection against process alterations as compared to existing methods. Moreover, the suggested control charts are implemented using actual data from a combined cycle power plant.

Diagnosis and prognosis of soybean roaster failures using particle swarms

In industry, the monitoring and diagnosis of production processes are crucial issues in ensuring plant reliability, performance and quality. In particular, food processing operations, such as coffee roasting, are subject to numerous risks of failure that can impact on productivity and the quality of the final product. In this context, the main objective of this study was to develop an innovative method for the diagnosis and prognosis of failures in a coffee roasting process. The proposed method differs from standard approaches by using the particle swarm optimization (PSO) algorithm applied to the analysis of signatures of key process variables. This new approach has improved fault detection, with a recognition rate of over 90% for the main types of fault identified, such as heating problems, air obstructions or leaks. In addition to diagnostics, the method has also demonstrated its effectiveness in prognosticating the state of health of the process, with an average error on the prediction of remaining service life reduced to 15%, compared with 35% for fixed-threshold methods. This work has therefore enabled us to develop an innovative method offering superior performance to standard approaches for the diagnosis and prognosis of failures in the roasting process.

A deep learning framework for predicting slab transverse crack using multivariate LSTM-FCN in continuous casting

Accurate and timely predictions of transverse cracks in slabs are crucial for ensuring efficient and high-quality production in continuous casting. However, the accumulation of substantial amounts of complex, multi-dimensional time series data during the casting process considerably affects the modeling performance. In order to increase the prediction accuracy, this paper introduces a novel prediction framework for slab transverse cracks (PF-STC), which comprises three stages: data preparation, data processing, and model training. First, a synchronized scheme is developed for real-time data tracking to achieve one-to-one matching between the process data in the data preparation phase. Second, the data preprocessing stage tackles the multidimensionality of time series data through normalization and mean aggregation downsampling. Finally, in the model training phase, a multivariate long short-term memory-fully convolutional network (MLSTM-FCN) algorithm is utilized to improve the predictive performance. Experimental results on a real dataset from a steel manufacturing company demonstrate that the PF-STC framework outperforms recent state-of-the-art methods, reducing the number of parameters by 49.97% and training time by 33.83%. The PF-STC achieves an AUC score of 0.9435, with accuracy, precision, recall, and F1 scores of 0.9163, 0.8657, 0.8286, and 0.8467, respectively. The proposed PF-STC is poised to provide support for online transverse crack monitoring, with the application demands of monitoring production processes for the steel industry.

Integrated optimization for X-bar control chart, preventive maintenance and production rate

There are close relationships among statistical process control, maintenance, and production that have not fully explored in existing studies. In this study, we propose an economic model for jointly optimizing the X-bar control chart, preventive maintenance, and production rate. Unlike conventional studies relying on known failure rate functions, we employ a Gamma process to describe the system's deterioration. This approach allows us to consider not only the relationship between the production rate and the deterioration rate but also a broad range of deterioration scenarios and failure modes in the integrated optimization. After calculating the failure probability at each time point, we propose a recursive method to derive the production revenue, process control costs, and quality losses. This approach explores the close inner relationship between the production rate, deterioration, process monitoring and maintenance. Subsequently, we establish an optimization model aimed at maximizing the profit rate within a renewal cycle. The decision variables of this model include the production rate, control chart coefficient, sample size, length of the first sampling interval, and number of sampling intervals. Finally, we use a case study to illustrate the effectiveness and validity of our proposed model by identifying and explaining the interactions between production rate, deterioration, process control and maintenance.

INTEGRATION OF SENSOR TECHNOLOGIES INTO THE STRUCTURAL SCHEME OF AUTOMATED GREENHOUSE CONTROL

The development of wireless sensor network technologies has opened up new opportunities for monitoring and controlling greenhouse parameters in precision agriculture. Over the past decades, significant advances in agricultural technology have been made, which has contributed to higher yields. With the uneven natural distribution of rainwater, it is important for farmers to ensure that water is distributed evenly to all crops on the farm or according to the needs of individual crops. There is no one-size-fits-all irrigation method that is suitable for all weather conditions, soil types, and different crops. Greenhouse technologies can be the best solution to this problem. Choosing the right method requires a detailed analysis of all greenhouse parameters. Farmers often face large financial losses due to incorrect weather forecasting and the wrong method of irrigating crops. The aim of the study is to create conditions for increasing the efficiency of growing vegetables in greenhouses by introducing highly integrated systems for control, monitoring, management and automation of production processes. This involves the development of a structural diagram of a mechatronic smart system for monitoring and dispatching technological processes. As a result of the research, a flowchart for automated greenhouse control was developed. In this scheme, the adaptation unit, based on the obtained values of environmental parameters, compares them with the standard values. If deviations are detected, the system sends information signals to adjust the agroclimatic conditions by activating the appropriate actuators - humidifiers, lighting lamps, internal irrigation and heating systems.

Smart Industrial Internet of Things Framework for Composites Manufacturing.

Composite materials are increasingly important in making high-performance products. However, contemporary composites manufacturing processes still encounter significant challenges that range from inherent material stochasticity to manufacturing process variabilities. This paper proposes a novel smart Industrial Internet of Things framework, which is also referred to as an Artificial Intelligence of Things (AIoT) framework for composites manufacturing. This framework improves production performance through real-time process monitoring and AI-based forecasting. It comprises three main components: (i) an array of temperature, heat flux, dielectric, and flow sensors for data acquisition from production machines and products being made, (ii) an IoT-based platform for instantaneous sensor data integration and visualisation, and (iii) an AI-based model for production process forecasting. Via these components, the framework performs real-time production process monitoring, visualisation, and prediction of future process states. This paper also presents a proof-of-concept implementation of the framework and a real-world composites manufacturing case study that showcases its benefits.

DECISION-MAKING SUPPORT TECHNOLOGIES IN THE FORMATION OF QUALITY MANAGEMENT SYSTEMS AT VEHICLE REPAIR ENTERPRISES

In recent years, the strategy of creating a safely functioning and efficient transport complex of Ukraine integrated into the world transport network has been fixed. Therefore, the creation of a progressive system of product quality management in the conditions of car repair enterprises is considered an urgent scientific and technical task. The quality management system is a management subsystem of an industrial enterprise. Modern production systems are based on TQM principles and widely use ISO 9001 certification. At the same time, certification is based on independent third-party audits (a conformity assessment organization). The wide spread of the standard is based on the fact that it contains generalized requirements (principles) for the organization of product quality control, which can be used at enterprises that differ significantly both in terms of specialization and the scale of production (depots or car repair plants). But this is the «bottleneck» of the standard – the lack of consideration of specialized production conditions makes it impossible to compensate for critical factors affecting the final quality of products. Currently, the accepted approaches to quality assessment do not fully take into account the factors that affect the final quality of products. In order to put all market participants - railway operators, manufacturers, rolling stock repairers and suppliers in mutually beneficial conditions, a single International Railway Industry Standard ISO / TS 22163 International Railway Industry Standart (IRIS) was developed.For the effective use of tools for ensuring the quality of rail car repair, it is considered necessary to use modern management decision support systems. They will allow real-time monitoring of production processes. It is possible to use the method of fuzzy sets to improve the quality management systems of wagon repairs based on the development of separate control algorithms with decision tables. In order to meet the requirements of international quality standards, it is necessary to significantly modernize management approaches based on the creation of progressive decision support systems.

Amine Switchable Hydrophilic Solvent Vortex-Assisted Homogeneous Liquid-Liquid Microextraction and GC-MS for the Enrichment and Determination of 2, 6-DIPA Additive in Biodegradable Film.

2, 6-diisopropylaniline (2, 6-DIPA) is a crucial non-intentionally organic additive that allows the assessment of the production processes, formulation qualities, and performance variations in biodegradable mulching film. Moreover, its release into the environment may have certain effects on human health. Hence, this study developed simultaneous heating hydrolysis-extraction and amine switchable hydrophilic solvent vortex-assisted homogeneous liquid-liquid microextraction for the gas chromatography-mass spectrometry analysis of the 2, 6-DIPA additive and its corresponding isocyanates in poly(butylene adipate-co-terephthalate) (PBAT) biodegradable agricultural mulching films. The heating hydrolysis-extraction conditions and factors influencing the efficiency of homogeneous liquid-liquid microextraction, such as the type and volume of amine, homogeneous-phase and phase separation transition pH, and extraction time were investigated and optimized. The optimum heating hydrolysis-extraction conditions were found to be a H2SO4 concentration of 2.5 M, heating temperature of 87.8 °C, and hydrolysis-extraction time of 3.0 h. As a switchable hydrophilic solvent, dipropylamine does not require a dispersant. Vortex assistance is helpful to speed up the extraction. Under the optimum experimental conditions, this method exhibits a better linearity (0.0144~7.200 μg mL-1 with R = 0.9986), low limit of detection and quantification (0.0033 μg g-1 and 0.0103 μg g-1), high extraction recovery (92.5~105.4%), desirable intra- and inter-day precision (relative standard deviation less than 4.1% and 4.7%), and high enrichment factor (90.9). Finally, this method was successfully applied to detect the content of the additive 2, 6-DIPA in PBAT biodegradable agricultural mulching films, thus facilitating production process monitoring or safety assessments.

Triple sampling X̄ control chart for gamma process

Monitoring production processes to provide potential customers with quality products while utilising fewer resources and reducing the cost of inspection is a challenge to quality and industrial engineers. Aiming at developing an improved design using reduced sample sizes, this study proposes a triple sampling (TS) X ¯ chart to monitor shifts in the process mean when the quality characteristic of interest follows the gamma distribution. Optimal parameters are determined by employing three optimisation procedures. The detection ability of the TS X ¯ chart is evaluated and contrasted with competing charts via the average number of observations to signal (ANOS), average run length (ARL) and expected ANOS (EANOS) criteria. When the TS X ¯ chart is designed for detecting small and large shifts, it is noticed that the sample sizes at the first, second and third sampling stages are larger for detecting small shifts. Compared to its competitors, the TS X ¯ chart requires fewer observations on the average to signal a shift; hence, it uses resources more efficiently and incurs lower inspection costs. To aid in the implementation of the TS X ¯ chart, an illustrative example of monitoring the wafer process is provided. Overwhelming evidence based on reported results supports the TS X ¯ chart.

INTELLIGENT MONITORING SYSTEMS IN MANUFACTURING: CURRENT STATE AND FUTURE PERSPECTIVES

Intelligent Monitoring Systems (IMS) have emerged as indispensable tools in modern manufacturing, offering real-time insights into production processes, equipment performance, and quality control. This review provides an overview of the current state and future prospects of IMS in manufacturing environments. The current state of IMS in manufacturing involves the integration of advanced technologies such as Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning (ML), and Big Data analytics. These systems collect vast amounts of data from sensors, machines, and production lines, enabling real-time monitoring and analysis of various parameters. By employing AI and ML algorithms, IMS can identify patterns, predict anomalies, and optimize production processes, leading to increased efficiency, reduced downtime, and improved product quality. IMS play a crucial role in predictive maintenance, where they can anticipate equipment failures before they occur, thereby minimizing unplanned downtime and maintenance costs. Moreover, IMS facilitate condition-based monitoring, allowing manufacturers to monitor the health and performance of machinery in real-time and schedule maintenance activities accordingly, optimizing resource allocation and prolonging equipment lifespan. Furthermore, IMS contribute to quality control by continuously monitoring production processes and detecting deviations from desired specifications. By leveraging AI-driven algorithms, IMS can automatically adjust process parameters to maintain product quality standards and minimize defects, thereby enhancing overall product reliability and customer satisfaction. Looking ahead, the future perspectives of IMS in manufacturing are promising, with advancements in areas such as edge computing, robotics, and augmented reality poised to revolutionize manufacturing operations further. Edge computing enables data processing and analysis to occur closer to the data source, reducing latency and enhancing real-time decision-making capabilities. Robotics integration with IMS facilitates autonomous manufacturing processes, while augmented reality technologies provide intuitive interfaces for operators to interact with IMS data in real-time. IMS represent a transformative technology in manufacturing, offering unprecedented levels of visibility, control, and optimization. As technology continues to evolve, IMS are poised to play an increasingly vital role in shaping the future of manufacturing, driving efficiency, productivity, and innovation..
 Keywords: Monitoring, System, Intelligent, Manufacturing, Review, Perspectives.

Sustainable manufacturing production process monitoring and economic benefit analysis based on IoT technology

Sustainable manufacturing production process monitoring and economic benefit analysis based on IoT technology

Decision support system for cost planning and implementation of production tasks of LW “Bogdanka” S.A. – the concept of integrating IT solutions towards the idea of an intelligent mine

The article presents the concept and specific activities undertaken by LW “Bogdanka” S.A. to build an IT system that integrates the current dispatching solutions of the mine with IT systems supporting the management of basic underground infrastructure facilities. This concept forms the substantive foundation of a solution that supports the decision-making system for conducting production activities using IT solutions and production monitoring, implemented under the slogan “Intelligent Mine”. The Intelligent Mine, as understood by LW “Bogdanka” S.A., involves a series of innovative technical solutions aimed at increasing mining effectiveness while ensuring the safety of underground workers and minimizing the environmental impact. The article addresses several key issues, including the practical experience of LW “Bogdanka” S.A. in innovative deposit management, scheduling access, preparatory and exploitation works, production resources management, and monitoring of production processes. Additionally, the article explores how these integrated IT systems contribute to improving operational efficiency, enhancing safety protocols, and promoting sustainable mining practices. By providing a comprehensive overview of these initiatives, the article highlights the significant advancements and practical implementations that LW “Bogdanka” S.A. has achieved in its journey toward becoming an Intelligent Mine.