Monitoring Of Production Processes Research Articles

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

ABSTRACT 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

Design and Implementation of an RFID System for Order Tracking in Smart Manufacturing: A Case Study of a Mechanical Factory

Six Sigma is a well-liked technique for raising productivity and quality of output in industrial processes and process improvement initiatives. In the Six Sigma methodology, abnormalities in the operational process are reviewed and examined using the Define, Measure, Analyze, Improve, and Control (DMAIC) cycle. Corrective action is then taken to reduce bottlenecks to enhance productivity, ensure stable product quality, and create a tightly connected production cycle. This study proposes the use of integrating statistical hypotheses into the DMAIC cycle of the Six Sigma method and proposes the use of Enterprise Resource Planning (ERP) modules to monitor production processes at a major mechanical processing company. Lean tools are also suggested by the author to be used in the analysis phase. The research results show a reduction in the rate of late product delivery to users from 6.28% to 0.02% per month. The lean tool application model, statistical hypotheses, and industry 4.0 system are integrated into the EPR system at the company's improvement department and are considered a model for manufacturing companies to refer to in implementing improvements for their products. Production processes in different companies.

Smart manufacturing system based on industrial internet of things

The primary challenge in the manufacturing industry today is the subpar productivity and efficiency in production processes. This predicament can be attributed to the widespread utilization of suboptimal production systems and the underutilization of advanced technology in manufacturing. Furthermore, the lack of accurate production data, limited employee involvement in production processes, and the high costs associated with production and machine maintenance have emerged as significant concerns in the contemporary manufacturing landscape. In response to these challenges, the adoption of Smart Manufacturing Systems (SMS) based on the Industrial Internet of Things (IIoT) has become crucial to address these issues andenhance overall production quality and efficiency. The SMS IIoT system empowers companies to gather real-time data from various industrial devices and machines. This data is subsequently analyzed to optimize production processes, resulting in increased efficiency and productivity. With this system in place, companies can closely monitor production processes in real-time and promptly detect and address defective or non-compliant products at the onset of production. The research results show an availability value of 88%, indicating that the machine operates 88% of the scheduled time. A Performance value of 100% indicates that the engine is operating at the desired maximum speed. OEE is calculated as the product of Availability, Performance, and Quality of Rate. With a value of 88%, OEE reflects the overall level of effectiveness of the machine in the production process. This paper highlights the significance of IIoT-based Smart Manufacturing Systems in addressing the prevailing challenges encountered by the manufacturing industry.

Dirençli Punta Kaynağı Prosesinin KNN ve CART Makine Öğrenimi Teknikleri ile Değerlendirilmesi

Spot welding, a type of resistance welding, is a welding application widely used in the production area and it is a common method for joining metal sheets. The spot-welding process is widely used in many production areas, especially in the automotive industry, radiator, and wire mesh production. Spot welding in car production lines is mainly performed by robotic applications. Industry 4.0 and digital transformation trends have led to unprecedented data growth. Nowadays, the manufacturing industry benefits from the power of machine learning and data science algorithms to monitor production processes and make predictions for quality, maintenance, and production optimization. Applying machine learning algorithms reduces the duration and cost of experiments. This study aims to confirm whether the spot welding, applied by robotic arms, is within the ideal spot-welding norms, in real production area. The ideal parameter norms were evaluated by using KNN and CART machine learning algorithms. To use real production data, this study was executed in the body production assembly line, which is selected as the pilot area, at TOFAŞ factory. The data set used in this research consists of the welding parameters of the current year, 2023. By running machine learning algorithms on the dataset, the performance evaluation of each algorithm was examined and the most appropriate estimation method was determined. In the experiments, the best F1-Score value was obtained by the CART model with 93%.

ON EXPONENTIALLY WEIGHTED MOVING AVERAGE CONTROL CHART FOR TIME TRUNCATED LIFE TEST USING INVERSE WEIBULL DISTRIBUTION

Statistical control charts are widely used in industry for process and measurement control to monitor production processes in order to discover any problem or issues that may arise during the production process and to help in finding solutions for these issues. This research proposes the exponentially weighted moving average (EWMA) control chart, comparing it’s performance to NP and MA control chart for monitoring the number of nonconforming with the truncated lifetime of a product when the lifetimes of products follows inverse Weibull (IW) distribution. The number of failures during the life test is used as an indicator of the quality of the product. EWMA control chart was proposed for this specific situation, thus extending the applicability of control charts methodology to situations involving truncated life tests. The performance of these control charts was measured with the average run length (ARL). The simulation shows that the results obtained from the EWMA chart using IW distribution is more sensitive in detecting small shift and performs much better to monitoring shifts as when compared to other control charts considered in the study.

Implementation of a Production Monitoring System Using IIoT Based on Mobile Application

Productivity is a key factor in the success of a company, and real-time monitoring systems are necessary to achieve this goal. Manual data collection is time-consuming and exhausting. Industrial Internet of Things (IIoT) technology has been rapidly advancing in monitoring and optimizing industrial processes. Production processes can be disrupted due to machine problems; hence, the need to analyze machine efficiency using the overall equipment effectiveness (OEE) method. This study implements a system that uses Industrial Internet of Things technology based on mobile application to monitor production processes, report production results, assess machine performance using the OEE method and provide maintenance notifications based on time-based maintenance. Research findings indicate that the production monitoring system implemented on a prototype press machine based on an interactive mobile application interface is capable of monitoring production processes and reporting production results. The system can also assess machine performance using the OEE method, with a calculation accuracy of 99.95% and maintenance notifications with a delay time of 1.04 seconds.

Immobilization of Enzyme Electrochemical Biosensors and Their Application to Food Bioprocess Monitoring.

Electrochemical biosensors based on immobilized enzymes are among the most popular and commercially successful biosensors. The literature in this field suggests that modification of electrodes with nanomaterials is an excellent method for enzyme immobilization, which can greatly improve the stability and sensitivity of the sensor. However, the poor stability, weak reproducibility, and limited lifetime of the enzyme itself still limit the requirements for the development of enzyme electrochemical biosensors for food production process monitoring. Therefore, constructing sensing technologies based on enzyme electrochemical biosensors remains a great challenge. This article outlines the construction principles of four generations of enzyme electrochemical biosensors and discusses the applications of single-enzyme systems, multi-enzyme systems, and nano-enzyme systems developed based on these principles. The article further describes methods to improve enzyme immobilization by combining different types of nanomaterials such as metals and their oxides, graphene-related materials, metal-organic frameworks, carbon nanotubes, and conducting polymers. In addition, the article highlights the challenges and future trends of enzyme electrochemical biosensors, providing theoretical support and future perspectives for further research and development of high-performance enzyme chemical biosensors.

Application routing algorithm based on the theory of fuzzy sets in the system of monitoring and management of complex production processes shipbuilding enterprise

Application routing algorithm based on the theory of fuzzy sets in the system of monitoring and management of complex production processes shipbuilding enterprise

Activated carbon@silver nanoparticles conjugates as SERS substrate for capturing malathion analyte molecules for SERS detection

AbstractMalathion is one of the commonly used organophosphate pesticides known to attack the central nervous system, posing a risk to humans and other animals upon exposure. The surface enhanced Raman spectroscopy (SERS) has been identified as an indispensable tool for chemical and biomolecular sensing. In this work, the facile fabrication of activated carbon (AC)‐based colloidal SERS active substrate dubbed AC@AgNPs was designed by trapping AgNPs on the surface of AC for detection of varying concentrations of malathion. Apart from the higher concentrations of malathion, the rest of the normal Raman spectra of malathion standard solutions exhibited weak Raman signals. The intensity of peaks for 0.47 mg L−1 were nearly non‐existent which is an indication that the malathion pesticide could only be detected up to 0.95 mg L−1 when using silica wafer. On the contrary, all the SERS spectra of malathion in wheat extracts adsorbed on AC@AgNPs substrate exhibited strong Raman signals. Quantitative analysis of malathion was performed by regression models developed using PLSR built with the raw spectra (no pretreatment), SNV‐PLSR, and SNV‐CARS‐PLSR. The model with the most remarkable performance was established by using SNV‐PLSR with r = 0.9869 and RPD = 4.61. This research shows that the proposed method can rapidly detect malathion residues in wheat, suggesting that it could be adopted for production process monitoring of other related food products to guarantee their safety for human and animal consumption.

Wireless, Material-Integrated Sensors for Strain and Temperature Measurement in Glass Fibre Reinforced Composites.

Fiber reinforced plastics (FRP) offer huge potentials for energy efficient applications. Special care must be taken during both FRP fabrication and usage to ensure intended material properties and behavior. This paper presents a novel approach for the monitoring of the strain and temperature of glass fibre reinforced polymer (GFRP) materials in the context of both production process monitoring and structural health monitoring (SHM) applications. The sensor is designed to be integrated into GFRPs during the production process, and the sensor concept includes possibilities of automated placement during textile layup. To minimize sensor impact on GFRP integrity and to simplify vacuum setup and part handling, the sensor operates without the need for either wires or a battery. In the first sections of this work, sensor concept, design and prototype fabrication are presented. Subsequently, it is shown how the sensors can be used for flow front monitoring and cure estimation during GFRP production by measuring local resin temperature. The resulting specimens are then characterized regarding strain measurement capabilities, mechanical influence on the host component and overall system limitations. Average strain sensor accuracy is found to be ≤0.06 mm/m, while a maximum operation temperature of 126.9 °C and a maximum reading distance of 38 mm are measured. Based on a limited number of bending tests, no negative influence of sensor presence on breaking strength could be found. Possible applications include structural components, e.g., wind turbine blades or boat hulls.

METHODOLOGICAL PRINCIPLES OF MONITORING INDICATORS OF ENERGY SECURITY IN THE ACTIVITIES OF BUSINESS STRUCTURES

The article proves that the energy security monitoring system of business structures is an effective tool for monitoring and ensuring the normal functioning of various types of economic objects and processes. This economic category has been used for a long time and quite widely in the field of production and business activity of industrial enterprises. In order to increase the efficiency of the economy, along with the reforms carried out at the macro level, it is necessary to reform the activities of individual enterprises as subjects of the micro level. At the same time, the role and significance of the management component of the functioning of industrial enterprises is determined, based on the analysis of the information received by the enterprise and the monitoring of production, sales and management processes. A methodical approach to monitoring the energy security of business structures has been developed. An analytical monitoring function is proposed, which can be sufficiently objective to reflect the existence of economic security at the enterprise. The theoretical and methodical foundations of energy security monitoring, which are based on production, commercial and financial performance indicators of the enterprise, are considered. Existing monitoring systems consider its possibilities extremely broadly, including anti-crisis, environmental, financial, innovative and other types of monitoring. It is proven that the theory and practice of economic monitoring does not sufficiently illuminate its possibilities in relation to industrial and commercial activity. A meaningful sequence of stages of monitoring financial and production and commercial activity of an industrial enterprise is proposed. It has been proven that an enterprise's assessment of its commercial opportunities, the state of its target market, economic changes in it, the determination of its position on the market in relation to similar goods and substitute goods, and the determination of possible prospects and directions of innovative development can be carried out quite effectively using the principles and methods of economic monitoring. Methodical provisions for monitoring the energy security of an industrial enterprise have been developed. with the use of monitoring functions. The proposed function is defined in the interval [-1; +1] and due to its non-linearity, it becomes possible to track the gradient of changes in the energy security results of the enterprise. The size of this change is the basis for monitoring the level of energy security of an industrial enterprise.

ECG-Based Stress Detection and Productivity Factors Monitoring: The Real-Time Production Factory System

Productivity and production quality have become primary goals for the success of companies in all industrial and manufacturing sectors. Performance in terms of productivity is influenced by several factors including machinery efficiency, work environment and safety conditions, production processes organization, and aspects related to workers’ behavior (human factors). In particular, work-related stress is among the human factors that are most impactful and difficult to capture. Thus, optimizing productivity and quality in an effective way requires considering all these factors simultaneously. The proposed system aims to detect workers’ stress and fatigue in real time using wearable sensors and machine learning techniques and also integrate all data regarding the monitoring of production processes and the work environment into a single platform. This allows comprehensive multidimensional data analysis and correlation research, enabling organizations to improve productivity through appropriate work environments and sustainable processes for workers. The on-field trial demonstrated the technical and operational feasibility of the system, its high degree of usability, and the ability to detect stress from ECG signals exploiting a 1D Convolutional Neural Network (accuracy 88.4%, F1-score 0.90).

Analytical separation of plasmid DNA isoforms using anion exchanging chromatographic monoliths with 6µm channels.

High-performance liquid chromatography (HPLC)-based analytical assays are used to effectively monitor purity and quantity of plasmid DNA (pDNA) throughout the purification process. However, the phenomenon of physical entrapment of open circular (OC) isoforms pDNA inside narrow channels of chromatographic support decreases its accuracy and precision and the effect increases with pDNA size. The purpose of the study was to develop a chromatographic method for accurate analytical separation between isoforms of <16kbp pDNA using weak anion exchanging monolithic column with large (6µm) convective channels. Purified samples of 4.7 and 15.4kbp large pDNA with known isoform composition were prepared and their isoforms separated in ascending salt gradient. Both OC and supercoiled (SC) isoforms were baseline separated at a flow rate below 0.5mLmin-1 in a guanidinium chloride (GdnCl) gradient with a ≥95% OC pDNA elution recovery. However, these chromatographic conditions increased 2 times the peak width for linear (LIN) pDNA isoform compared to the results using monoliths with 1.4µm channel size. If other chaotropic agents, such as urea or thiocyanate (SCN), were added to Gdn ions, the elution volume for LIN isoform decreased. Optimization of combined GdnCl/GdnSCN gradient for pDNA elution resulted in a simple and robust chromatographic method, where OC-LIN and LIN-SC pDNA (up to 15kbp size) were separated with resolution above 1.0 and above 2.0, respectively. The accessibility and general acceptance of anion exchange chromatography for pDNA analytics give the newly developed method a great potential for in-process control monitoring of pDNA production processes.

Time Series Prediction Method of Industrial Process With Limited Data Based on Transfer Learning

Industrial time series, as a kind of data that responds to production process information, can be analyzed and predicted for effective monitoring of industrial production processes. There are problems of data shortage and algorithm cold start in industrial modeling process caused by complex working conditions, change of data acquisition environment and short running time of equipment. As a result, the accuracy of the existing data-driven industrial time series prediction algorithm is greatly limited. To address the above problems, we propose a new time series prediction method for industrial processes under limited data based on dynamic transfer learning in this work. This method aims to effectively use historical data of similar equipment or working conditions rather than discard them to help establish an industrial time series prediction model with limited target data. In this method, first, historical data are divided into multiple batches, and then a new multi-source transfer learning framework with dynamic maximum mean difference (MMD) loss is established according to the distribution distance between each batch of historical data and the limited target data at the current moment. The framework also combines multi-task learning methods to establish multi-step prediction model for online learning in industrial processes. Compared with other commonly used methods, experiments on two real-world datasets of solar power generation prediction and heating furnace temperature prediction demonstrate the effectiveness of the proposed method.

Evaluating Novel Quantification Methods for Infectious Baculoviruses.

Accurate and rapid quantification of (infectious) virus titers is of paramount importance in the manufacture of viral vectors and vaccines. Reliable quantification data allow efficient process development at a laboratory scale and thorough process monitoring in later production. However, current gold standard applications, such as endpoint dilution assays, are cumbersome and do not provide true process analytical monitoring. Accordingly, flow cytometry and quantitative polymerase chain reaction have attracted increasing interest in recent years, offering various advantages for rapid quantification. Here, we compared different approaches for the assessment of infectious viruses, using a model baculovirus. Firstly, infectivity was estimated by the quantification of viral nucleic acids in infected cells, and secondly, different flow cytometric approaches were investigated regarding analysis times and calibration ranges. The flow cytometry technique included a quantification based on post-infection fluorophore expression and labeling of a viral surface protein using fluorescent antibodies. Additionally, the possibility of viral (m)RNA labeling in infected cells was investigated as a proof of concept. The results confirmed that infectivity assessment based on qPCR is not trivial and requires sophisticated method optimization, whereas staining of viral surface proteins is a fast and feasible approach for enveloped viruses. Finally, labeling of viral (m)RNA in infected cells appears to be a promising opportunity but will require further research.

A Feasibility Study towards the On-Line Quality Assessment of Pesto Sauce Production by NIR and Chemometrics.

The food industry needs tools to improve the efficiency of their production processes by minimizing waste, detecting timely potential process issues, as well as reducing the efforts and workforce devoted to laboratory analysis while, at the same time, maintaining high-quality standards of products. This can be achieved by developing on-line monitoring systems and models. The present work presents a feasibility study toward establishing the on-line monitoring of a pesto sauce production process by means of NIR spectroscopy and chemometric tools. The spectra of an intermediate product were acquired on-line and continuously by a NIR probe installed directly on the process line. Principal Component Analysis (PCA) was used both to perform an exploratory data analysis and to build Multivariate Statistical Process Control (MSPC) charts. Moreover, Partial Least Squares (PLS) regression was employed to compute real time prediction models for two different pesto quality parameters, namely, consistency and total lipids content. PCA highlighted some differences related to the origin of basil plants, the main pesto ingredient, such as plant age and supplier. MSPC charts were able to detect production stops/restarts. Finally, it was possible to obtain a rough estimation of the quality of some properties in the early production stage through PLS.