Monitoring Of Production Processes Research Articles

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.

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

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

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.

ЗАБЕЗПЕЧЕННЯ ЕКОНОМІЧНОЇ БЕЗПЕКИ ПІДПРИЄМСТВА І КОНКУРЕНТОСПРОМОЖНОСТІ НА ЗАСАДАХ ІННОВАЦІЙНОГО РОЗВИТКУ

The article examines the key components of economic security of the enterprise, including financial, production, marketing, information and personnel security, as well as innovative approaches to their provision. In particular, the introduction of the latest technologies, such as automation, digitalization, the Internet of Things (IoT), 3D printing and energy-efficient technologies, significantly affect the economic security of the enterprise, contributing to increased efficiency, reduced costs and improved product quality. It has been determined that the automation and robotics of production processes reduce the human factor, which leads to a reduction in errors and an increase in accuracy. The use of digital technologies allows for constant monitoring of production processes in real time, which ensures prompt response to any deviations and breakdowns. The introduction of the latest materials and technologies, such as 3D printing, allows you to create more complex and high-quality products, reducing production costs. Innovations in the field of energy efficiency help reduce energy consumption and resource costs, increasing the company's resilience to environmental challenges. It is substantiated that innovations determine the ability of the enterprise to remain competitive and adaptive to changes in market conditions. Development of new products and services, improvement of quality and differentiation of products, flexibility and speed of response, as well as expansion of sales markets through the development of new distribution channels and globalization of activities allow enterprises to maintain and expand market positions. Thus, ensuring the economic security of the enterprise on the basis of innovative development is an extremely important task in the modern world. The use of innovative approaches allows enterprises not only to protect their economic interests, but also to ensure longterm growth and competitiveness. Enterprises that actively implement innovations have more chances to succeed in a dynamic and competitive environment, ensuring stable development and protection from economic threats.

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.