Comprehensive approach toward IIoT based condition monitoring of machining processes

Laghari, Rashid Ali; Mekid, Samir

doi:10.1016/j.measurement.2023.113004

Cited by 12 publications

(4 citation statements)

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“…In the evolutionary context of Industry 4.0, studies regarding the application of enabling technologies and, in particular, the IoT are useful elements in order to obtain a greater understanding of monitoring the performance of production processes [11]. The OEE indicator is recognized in the scientific literature as a useful KPI to monitor the performance of production processes [13,14].…”

Section: Discussionmentioning

confidence: 99%

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A Hybrid Architectural Model for Monitoring Production Performance in the Plastic Injection Molding Process

Luisi,

Di Pasquale,

Pietronudo

et al. 2023

Applied Sciences

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Monitoring production systems is a key element for identifying waste and production efficiency, and for this purpose, the calculation of the Key Performance Indicator (KPI) Overall Equipment Effectiveness (OEE) is validly recognized in the scientific literature. The collection and analysis of the cause of the interruption of the plants is particularly useful in this sense. The use of Internet of Things (IoT) technology in order to automate data collection for the purpose of calculating the OEE and the causes of interruption is effective. Furthermore, the existing literature lacks research studies that aim to improve the data quality of important process data that cannot be collected automatically. This study proposes the use of IoT technologies to request targeted and intelligent information inputs from the operators directly involved in the process, improving the completeness and accuracy of the information through the real-time and smart combination of manual and automated data. The Business Process Model and Notation (BPMN) methodology was used to analyze and redesign the collection data process and define the architectural model with a deep knowledge of the specific process. The proposed architecture, designed for application to a plastic injection molding production line, comprises several elements: the telemetry of the injection molding machine, an intervention request system, an intervention tracking system, and a human–system interface. Furthermore, a dashboard was developed using the Power BI software, 2.122.746.0 version, to analyze the information collected. Reducing the randomness of manual data makes it possible to direct production efficiency efforts more effectively, helping to reduce waste and production costs. Reducing production costs appears to be strongly linked to reducing environmental impacts, and future studies will be able to quantify the benefits obtained from the solution in terms of environmental impact.

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Section: Discussionmentioning

confidence: 99%

“…The enabling technologies of Industry 4.0 and, in particular, the IoT have turned out to be useful elements for monitoring the performance of production processes [11]. In the context of monitoring the performance of production plants, many publications in the scientific literature mention the Overall Equipment Effectiveness indicator.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Architectural Model for Monitoring Production Performance in the Plastic Injection Molding Process

Luisi,

Di Pasquale,

Pietronudo

et al. 2023

Applied Sciences

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“…Предпринимаются попытки анализа и прогнозирования шероховатости поверхности на основе регрессионных моделей и методологии поверхностного отклика (RSM) [16][17][18]. Большое количество современных исследований направлено на прогнозирование шероховатости поверхности с помощью искусственных интеллектуальных систем и имитационных моделей [19][20][21][22][23]. В работах [21][22][23] Y. Altintas и др.…”

Section: Introductionunclassified

Influence of dynamic characteristics of the turning process on the workpiece surface roughness

Gvindjiliya,

Fominov,

Moiseev

et al. 2024

Metal Working and Material Science

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Introduction. The formation of the surface of a part when processing it on a metal-cutting machine is based on properly selected cutting modes. Complex methods of ensuring the specified quality of the part surface also take into account the tool geometry, its condition, and include corrections for tool deviation from the trajectory set by the CNC system under the influence of kinematic disturbances and spindle wavering. Subject. The paper analyzes the relationship between cutting modes and dynamic characteristics of the turning process, and its mapping into surface roughness. The aim of the work is to evaluate the influence of technological cutting modes taking into account the vibration activity of the tool on the roughness of the machined surface by means of simulation modeling. Method and methodology. Mathematical simulation of the dynamics of the cutting process is given, on the basis of which a digital simulation model is built. A methodology of using the simulation model for determining optimal cutting modes and predicting surface roughness taking into account tool vibrations is proposed. By means of experiments and analysis of the frequency characteristics of tool vibrations, the created model is validated, parameters of the cutting forces model subsystem and dynamic tool subsystem are specified, and geometrical topologies of the part surface are constructed. The calculated cutting forces are compared with experimental forces, and similar patterns and levels of characteristics are observed. An assessment of the optimality of the selected cutting modes is proposed based on the analysis of the tool vibration spectrum relative to the workpiece and the results of the numerical model simulation. Results and Discussion. A comparison of the results of digital modeling of the geometrical surface of the workpiece and the real surface obtained during the field experiment is given. It is shown that the roughness of the real surface obtained by machining with constant cutting modes varies relative to the surface roughness of the simulation model within the limits of not more than 0.066 µm.

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“…The Industrial Internet of Things (IIoT) is a network of multiple devices (such as sensors, instruments, computers, and machines) interconnected via the Internet to collect, monitor, and analyze data to streamline industrial production, aiding decision-making with real-time information, and reducing the need for human intervention [1]. The connectivity provided by IIoT networks enables the monitoring and optimization of industrial processes [2], better production quality control [3], as well as the automation of data collection and the reduction of data entry-related errors [4], more precise and real-time asset tracking and inventory monitoring [5], and facilitating the adoption of predictive maintenance methods aimed at minimizing downtime [6].…”

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confidence: 99%

GPT and Interpolation-Based Data Augmentation for Multiclass Intrusion Detection in IIoT

Melícias,

Ribeiro,

Rabadão

et al. 2024

IEEE Access

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The absence of essential security protocols in Industrial Internet of Things (IIoT) networks introduces cybersecurity vulnerabilities and turns them into potential targets for various attack types. Although machine learning has been used for intrusion detection in the IIoT, datasets with representative data of common attacks of IIoT network traffic are limited and often imbalanced. Data augmentation techniques address these problems by creating artificial data in classes with fewer samples. In this work, we evaluate the use of data augmentation when training intrusion detection models based on IIoT traffic data. We compare Generative Pre-trained Transformers (GPT) and variations on the Synthetic Minority Over-sampling TEchnique (SMOTE) and evaluate their capability to enhance intrusion detection performance. We examine the performance of five intrusion detection algorithms when trained with augmented datasets to models trained with the original non-augmented dataset. To ensure a fair comparison, we evaluated the algorithms' performance in the different scenarios using the same test dataset, which does not contain synthetic data. The results show the need for a systematic evaluation before employing data augmentation, as its impact on classification performance depends on the algorithm, data, and used technique. While deep neural networks benefit from data augmentation, the eXtreme Gradient Boosting (XGBoost), which achieved superior performance in intrusion detection between all evaluated classifiers (with F1-Score over 91%), didn't have any performance improvement when trained with augmented data. The evaluation of data generated by GPT-based methods shows such methods (especially GReaT) generate invalid data for both numerical and categorical features in a way that leads to performance degradation in multiclass classification.

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Comprehensive approach toward IIoT based condition monitoring of machining processes

Cited by 12 publications

References 169 publications

A Hybrid Architectural Model for Monitoring Production Performance in the Plastic Injection Molding Process

A Hybrid Architectural Model for Monitoring Production Performance in the Plastic Injection Molding Process

Influence of dynamic characteristics of the turning process on the workpiece surface roughness

GPT and Interpolation-Based Data Augmentation for Multiclass Intrusion Detection in IIoT

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