Industrial Operations Research Articles

Variance‐driven security optimisation in industrial IoT sensors

AbstractThe Industrial Internet of Things (IIoT) has transformed industrial operations with real‐time monitoring and control, enhancing efficiency and productivity. However, this connectivity brings significant security challenges. This study addresses these challenges by identifying abnormal sensor data patterns using machine learning‐based anomaly detection models. The proposed framework employs advanced algorithms to strengthen industrial defences against cyber threats and disruptions. Focusing on temperature anomalies, a critical yet often overlooked aspect of industrial security, this research fills a gap in the literature by evaluating machine learning models for this purpose. A novel variance‐based model for temperature anomaly detection is introduced, demonstrating high efficacy with accuracy scores of 0.92 and 0.82 on the NAB and AnoML‐IOT datasets, respectively. Additionally, the model achieved F1 scores of 0.96 and 0.89 on these datasets, underscoring its effectiveness in enhancing IIoT security and optimising cybersecurity for industrial processes. This research not only identifies security vulnerabilities but also presents concrete solutions to improve the security posture of IIoT systems.

Closed-Form Solutions of Injection-Driven Flow and Heat Transfer Inside an Inclined Horizontal Filter Chamber

Abstract The closed-form solutions of models provide operators and designers with a better understanding of how the systems perform practically, thus improving critical industrial production operations. Due to this importance, the case study at hand seeks to find closed-form solutions of the internal momentum and temperature variation during the filtration process to advance fluid purification. Lie group analysis is used to transform a system of equations representing the flow and heat transfer into a solvable system without changing the dynamics of the case study. The transformed solvable system is then integrated to find closed-form solutions of the internal velocity (momentum) and temperature variation. The obtained closed-form solutions are then used to analyze the effects of physical parameters arising from the process dynamics to find combinations of parameters that yield maximum permeates outflow. The analysis conveys that the internal fluid velocity increases when enhancing the permeation parameter and minimizing Reynolds number, wave speed parameter, and chamber height.

CLAMPING TOOLS PRINTED FROM ONYX AND USED IN INDUSTRIAL MANUFACTURING PROCESS

The publication verifies the theoretical possibilities of reducing production costs and production time for machine clamping jaws. The primary idea involves maintaining the design for conventional production while replacing the steel alloy 1.7225 with the ONYX plastic composite material reinforced with carbon fiber. The study was first verified computationally and then experimentally in industrial operations. The results of the study were recorded and carefully analyzed. The usability of the ONYX composite, in conjunction with carbon fiber, does not support its use in industrial operations. Although the rotary system was significantly lighter, the clamping elements exhibited relatively high elasticity values. Therefore, the results were unsatisfactory but highly beneficial for the practice and usability of plastic composites.

Principles for Sustainable Integration of BIM and Digital Twin Technologies in Industrial Infrastructure

As industries evolve towards greater digitalization, integrating Building Information Modeling (BIM) and digital twin technologies presents a unique opportunity to enhance sustainability in industrial infrastructure. This paper formulates a comprehensive set of principles aimed at guiding the sustainable integration of these technologies within the context of modern industrial facilities, often referred to as “Factories of the Future”. The principles are designed to address critical sustainability challenges, including minimizing environmental impact, optimizing resource efficiency, and ensuring long-term resilience. Through a detailed examination of lifecycle management, data interoperability, and collaborative stakeholder engagement, this work provides a strategic framework for leveraging digital technologies to achieve sustainability goals. The principles outlined in this paper not only promote greener industrial practices but also pave the way for innovation in the sustainable development of industrial infrastructure. This framework is intended to serve as a foundation for future research and practical application, supporting the global shift towards more sustainable industrial operations.

Advances in volatile organic compounds detection: From fundamental research to real-world applications

Volatile organic compounds (VOCs) play a crucial role in affecting health, environmental integrity, and industrial operations, from air quality to medical diagnostics. The need for highly sensitive and selective detection of these compounds has spurred innovation in sensor technologies. This editorial introduces a special collection of articles in Applied Physics Reviews, exploring the latest advancements in VOC detection technologies. The featured works cover a range of innovations, including electrostatically formed nanowires, chiral liquid crystals, and graphene-based sensors enhanced by machine learning. Together, these articles highlight the dynamic progress in VOC detection, striving for improved sensitivity, selectivity, and real-world applicability. This special collection not only showcases pioneering research but also provides valuable insights into future trends and potential applications in the field.

Fault detection of RV reducer cycloidal gear under robot working conditions based on optimization improved envelope spectrum sideband energy ratio

Abstract Under industrial robot operating conditions, the cycloidal gear in the rotary vector(RV) reducer in the robot joint runs in a compound rotation motion with a low-speed and incomplete cycle, which complicates the fault detection for the cycloidal gear. Until now, no effective detection method for the cycloidal gear tooth fault under industrial robot operating conditions has been reported. Consequently, a method based on optimization improved envelope spectrum sideband energy ratio is proposed in this paper. Firstly, encoder signals are acquired and used to estimate the instantaneous angular speed (IAS) signals, which are then separated into two sets of data based on rotational directions. Then, the data corresponding to the approximately stable speed is truncated and connected to form a constructed signal. Next, the coherence of the constructed signal is calculated using the fast spectral coherence algorithm. Subsequently, the improved envelope spectrum sideband energy ratio is used as the objective value, and a global optimization algorithm is employed to determine the integration frequency band of the improved envelope spectrum (IES). Finally, the IES of this frequency band is calculated to obtain the fault-relatedorder characteristics. An RV reducer test rig was used for the experiment, and the analysis results from the proposed approach, cyclic spectral coherence (CSCoh), and improved envelope spectrum via feature optimization-gram (IESFOgram) were contrasted. Experiment results show that the proposed method is valid for the detection of tooth faults in the cycloidal gear of the RV reducer in robot operating conditions.

Enhancing manual inspection in semiconductor manufacturing with integrated augmented reality solutions

On-site routine inspection often remains a manual operation in the semiconductor manufacturing industry because implementing automated solutions can be costly and technically challenging in such a highly controlled and complex environment. The manual inspection is prone to errors due to the impact of demanding physical and mental workloads. This paper presents an integrated Augmented Reality (AR) solution developed to assist manual inspection tasks in the supporting areas of semiconductor manufacturing, referred to as the sub-fab. The solution is accessible to a human worker wearing an AR headset during the inspection process at the location. We propose a system framework to deploy computational intelligences of varying granularity provided by the solution across cloud, edge, and device levels, accommodating constraints within the sub-fab. A machine maintenance module helps estimate and monitor the health condition of running scrubbers. Incorrect intentions performed by the worker on the scrubber control panel are detected through hand gesture recognition. This instantly prompts warning messages in the AR headset to prevent subsequent wrong actions. The solution can also identify abnormal device states through 6D pose estimation of objects enabled by machine learning models. A test scenario demonstrates how these functional features enhance the inspection efficiency and quality by reducing human workloads. This work demonstrates that semiconductor manufacturing may require AR-assisted functions different from those needed or common in other industrial sectors. It also highlights the potential of AR technology for reducing operational human errors in manual tasks.

Applications of integrating artificial intelligence and big data: A comprehensive analysis

Abstract The integration of artificial intelligence (AI) and big data technologies has the potential to revolutionize various industries, yet there are complexities and challenges associated with their implementation. This comprehensive study aims to investigate the combined impact of AI and big data on operational efficiency, precision, and security across multiple sectors. By utilizing a methodological analysis of 105 peer-reviewed articles sourced from reputable databases, we systematically explore the diverse applications, key innovations, and transformative potential of these technologies. Our findings uncover significant advancements in healthcare diagnostics, drug discovery, personalized education, and smart farming, highlighting how AI enhances big data analytics to drive notable improvements. Specifically, the study reveals the accuracy of AI in healthcare diagnostics, the efficiency of big data in drug discovery, the personalization of learning experiences through AI in education, and the sustainability advancements in agriculture through smart farming. These results underscore a substantial shift toward more sophisticated data-driven decision-making and operational processes facilitated by the integration of AI and big data. This shift addresses the initial research problem and makes a significant contribution to both academic and practical understanding of the role these technologies play in shaping the future of industry operations. The study concludes that while AI and big data integration offers substantial benefits, addressing associated challenges is crucial for maximizing their impact.

Employee Safety in the Conditions of Industry 4.0

Abstract The safety of employees during the implementation of work processes is one of the important conditions for the efficient implementation of production tasks. The situation does not change with the use of automation and robotization. On the contrary, it may be a source of hazards in new areas, requiring specific actions to limit the possibility of accidents occurring or to limit their consequences. These actions should be taken in a manner appropriate to the nature of the non-conformities. The study, based on the analysis of literature and a case study conducted in a production organization, showed the most important factors that may disturb the efficient implementation of production tasks. The study presents the key conditions for the development and operation of Industry 4.0, in accordance with the recommendations of the European Union and guidelines indicated in the scientific literature. The obtained results are addressed to the information of people responsible for the development of I4.0, taking into account “the safety aspects”, in industrial enterprises where the implementation of tasks requires cooperation between humans and machines. Statistical analysis was not included in the study. The author attempts to identify relationships that justify further research conducted to establish the existing correlations regarding supporting organizational development professionals, and in particular helping them identify potential problems in order to provide their organization with an advantage in the global competitive environment.

ANALYSIS OF THE CAUSES OF ACCIDENTS IN THE OIL AND GAS INDUSTRY

In the modern world, the oil and gas industry plays a key role in ensuring the energy security of countries. An important problem of this industry is the high level of accidents on main pipelines. The importance of issues of ensuring the safety of production processes and working conditions of personnel is obvious: accidents that occur at enterprises can paralyze work for a long time, attracting the attention of the media and regulatory authorities, causing not only financial but also significant image damage. In connection with the above, the analysis and statistical study of accidents in this area have become relevant in recent years. Occupational safety measures include risk assessment at workplaces, briefings, medical examinations, provision of personnel with personal protective equipment, special assessment of workplaces, occupational safety days, etc. In order to restore the technical and economic indicators of the oil and gas industry, increase reliability and trouble-free operation, complexes of measures are carried out to repair, reconstruct and modernize the components of the main gas pipeline. Ensuring industrial safety during repair work depends on both the reliability of the process equipment and the reliability of the personnel, since a significant number of accidents and incidents occur due to reasons related to the influence of the human factor. Many studies have been devoted to the problem of management in the field of labor protection and ensuring safety at work. Despite the significant number of works, a practical lack of developments on the issues of managing the processes of ensuring safety and labor protection at enterprises in the oil and gas industry has been discovered, which indicates the need for a more in-depth study of this topic.

Advancing smart city factories: enhancing industrial mechanical operations via deep learning techniques

In the contemporary realm of industry, the imperative for influential and steadfast systems to detect anomalies is critically recognized. Our study introduces a cutting-edge approach utilizing a deep learning model of the Long-Short Term Memory variety, meticulously crafted for real-time surveillance and mitigation of irregularities within industrial settings. Through the careful amalgamation of data acquisition and analytic processing informed by our model, we have forged a system adept at pinpointing anomalies with high precision, capable of autonomously proposing or implementing remedial measures. The findings demonstrate a marked enhancement in the efficacy of operations, with the model’s accuracy surging to 95%, recall at 90%, and an F1 score reaching 92.5%. Moreover, the system has favorably impacted the environment, evidenced by a 25% decline in CO2 emissions and a 20% reduction in water usage. Our model surpasses preceding systems, showcasing significant gains in speed and precision. This research corroborates the capabilities of deep learning within the industrial sector. It underscores the role of automated systems in fostering more sustainable and efficient operations in the contemporary industrial landscape.

Industrial Symbiosis Practices: A Case Study of Türkiye and Denmark

The term "industrial symbiosis (IS)" refers to a cooperative strategy where industrial actors share services and exchange physical resources. This method improves resource efficiency and lessens the environmental effects of industrial operations within the network. It is a cooperative method in which businesses trade and reuse physical resources such as by-products, water, and energy, as well as share services such as waste management and infrastructure. It has several advantages, such as (i) lowering greenhouse gas emissions through energy, process, input, disposal, and transportation savings, as well as fuel substitution; (ii) cutting energy expenses; (iii) diverting waste from landfills; (iv) avoiding the use of virgin materials; (v) lowering transportation and logistics costs; (vi) lowering pollution to land, water, and air; (vii) lowering industrial water use; (viii) lowering hazardous waste; (ix) creating economic value from waste material; and (x) simplifying environmental legislation and regulation compliance. In Türkiye and Denmark, the number of industrial companies is rapidly increasing. These industries consume many resources (such as raw materials, water, electricity, natural gas, coal, etc.) and generate a large amount of solid, liquid, and gaseous waste. IS is gaining popularity as an approach for improving the sustainability of industrial systems. The efforts to develop IS have emerged as a critical issue over the last decade in these two European countries. The paper presents the current implementation of IS in Türkiye and Denmark’s process sectors, focusing on well-known IS practices.

Comparing Feature and Trajectory-Based Remaining Useful Life Modeling of Electrical Resistance Heating Wires

Industrial heating significantly contributes to global greenhouse gas emissions, accounting for a substantial portion of annual emissions. The transition to fossil-free operations in the heating industry is closely linked to advancements in industrial electrical heating systems, especially those using resistance heating wires. In this context, Prognostics and Health Management is crucial for enhancing system reliability and sustainability through predictive maintenance strategies. The integration of machine learning technologies into Prognostics and Health Management has significantly improved the precision and applicability of Remaining Useful Life modeling. This improvement enables more accurate predictions of component lifespans, optimizes maintenance schedules, and enhances operational efficiency in industrial heating applications. These developments are essential for reducing greenhouse gas emissions in the sector. This paper serves as a guide for conducting Remaining Useful Life modeling for industrial batch processes. It evaluates and compares two methodologies: deep learning-based approaches using full time-series data, such as recurrent neural networks and their variants, and feature-engineering-based methods, including random forest regression and support vector machines. Our results show that the feature-oriented approach performs better overall in terms of predictive accuracy and computational efficiency. The study includes a detailed sensitivity analysis and hyperparameter estimation for each method, providing valuable insights into developing robust and transparent Prognostics and Health Management systems. These systems are crucial in supporting the heating industry’s move towards more sustainable and emission-free operations. The findings reveal that feature-oriented methods are both performant and robust, particularly excelling in handling outliers. The random forest regression model, in particular, demonstrated the highest performance on the test dataset according to the chosen evaluation metrics. Conversely, trajectory-oriented methods exhibited less bias across varying levels of degradation, a helpful characteristic for Prognostics and Health Management systems. While feature-oriented methods tend to systematically underestimate Remaining Useful Life at high true values and overestimate it at low actual values, this issue is less pronounced in trajectory-oriented models. Overall, these insights highlight the strengths and limitations of each approach, guiding the development of more effective and reliable predictive maintenance strategies.

Digital twin for decision support system of industrial utility management

Manufacturing and industrial operations rely heavily on energy that is generated mostly from fossil fuels, such as coal, oil, and natural gas, which harm the environment and contribute to climate change. Thus, renewable energy is being integrated into industrial processes to lessen environmental effects and reduce fossil fuel usage. However, the renewable source performance process can be greatly affected by disturbances and constraints, such as ambient air temperature and relative humidity, minimum utility consumption, and the total energy required, making effective control difficult. This study proposes a digital twin built with machine learning regression techniques for load demand forecasting as a decision-support system for industrial utility management. From the results, the ensemble tree (ET) model produced the highest accuracy, based on validation and test dataset root mean squared error values of 23.164 and 27.558, respectively, and R2 values of 0.96 and 0.95, respectively. The digital twin and load demand forecasting approaches effectively created an efficient operating schedule for industrial utility management. The ET model had a total error of 23.86%, substantially lower than the average load demand's total error of 65.29%. Therefore, the ET model with weather conditions in four scenarios could be recommended to optimize energy utilization when creating the operating schedule.

Improving Safety in High-Altitude Work: Semantic Segmentation of Safety Harnesses with CEMFormer

The symmetry between production efficiency and safety is a crucial aspect of industrial operations. To enhance the identification of proper safety harness use by workers at height, this study introduces a machine vision approach as a substitute for manual supervision. By focusing on the safety rope that connects the worker to an anchor point, we propose a semantic segmentation mask annotation principle to evaluate proper harness use. We introduce CEMFormer, a novel semantic segmentation model utilizing ConvNeXt as the backbone, which surpasses the traditional ResNet in accuracy. Efficient Multi-Scale Attention (EMA) is incorporated to optimize channel weights and integrate spatial information. Mask2Former serves as the segmentation head, enhanced by Poly Loss for classification and Log-Cosh Dice Loss for mask loss, thereby improving training efficiency. Experimental results indicate that CEMFormer achieves a mean accuracy of 92.31%, surpassing the baseline and five state-of-the-art models. Ablation studies underscore the contribution of each component to the model’s accuracy, demonstrating the effectiveness of the proposed approach in ensuring worker safety.

Knowledge and data integrated paradigm for industrial operation completion time prediction

Knowledge and data integrated paradigm for industrial operation completion time prediction

Carbon Emission under Industry 5.0

Industry 5.0 integrates people and machines into production to achieve more personalization, possibilities, and sustainable development. Industry 5.0 has three core elements: Humancentric, sustainability, and resilience. Humancentric emphasizes human-machine collaboration. Talent is the core of industrial operation. Technology is to serve humans better, not to replace humans. The sustainability of Industry 5.0 includes economic, environmental, social, and technological sustainability. In these aspects, Industry 5.0 has new requirements and development directions for sustainability. The carbon footprint runs through every link of the industry. Reducing carbon emissions is the requirement of Industry 5.0 for environmental sustainability. The technological innovation and progress of Industry 5.0, as well as the concept of sustainability, all affect and reduce carbon emissions to varying degrees.

Research on Integrated Optimization of Design Parameters and Process Simulation for Throughput Efficiency

This thesis presents a comprehensive approach to enhance the production throughput of aluminium bumper beams, a critical component in automotive manufacturing. Using CATIA, an innovative bumper beam design was developed, focusing on simplicity of design and manufacturability. The design parameters considered included material selection, cross-sectional area optimization, and welding techniques to ensure both performance and ease of manufacturing. Transitioning to Tecnomatix Plant Simulation, a virtual environment was constructed to simulate the bumper beam production process. The simulation incorporated the identified design parameters, process routes, layout considerations, and machine arrangements to optimize throughput without inflating costs. By leveraging advanced simulation techniques, the study aimed to identify the most efficient production strategies while maintaining design integrity and minimizing resource consumption. Through systematic scenarios experimentation and analysis, the research yielded insights into the complex relationship between design decisions and manufacturing processes. The results highlight the potential for significant throughput enhancements achievable through collaborative optimization of design and production parameters. The optimization was able to achieve an increase in throughput of about 12.64% of its initial throughput. Moreover, the study underscores the importance of holistic approaches integrating design engineering with manufacturing simulation to drive continuous improvement in industrial operations. This thesis contributes to advancing automotive manufacturing practices by offering a methodology for enhancing production efficiency while preserving product quality and cost-effectiveness. The findings provide valuable guidance for engineers and practitioners seeking to optimize manufacturing processes through the use of simulation and design parameters

Designing real-time safety monitoring dashboards for industrial operations: A data-driven approach

Designing real-time safety monitoring dashboards for industrial operations: A data-driven approach

Effects of mercury and magnetic fields on the activity of proteinases and glycosidases in the intestine of common carp Cyprinus carpio.

Aquatic ecosystems are increasingly affected by anthropogenic pollution, including heavy metals like mercury, which accumulate in organisms and cause harmful effects. At the same time, human activities such as industrial operations and the use of electric power lines also alter the magnetic background in natural water bodies. However, the interaction between mercury exposure and magnetic fields remains poorly understood. This study aimed to investigate the combined effects of mercury and magnetic fields on the digestive enzyme activity of common carp (Cyprinus carpio), focusing on the relevance of magnetic fields due to their increasing presence in industrialized environments. Two groups of fish were fed diets with low (0.02mg/kg) or high (0.27mg/kg) mercury content for 6months and exposed to extremely low-frequency magnetic fields or hypomagnetic conditions. Results showed significant differences in mercury accumulation between groups, with higher levels in carps fed with high-mercury content diets. These fish also exhibited increased body length and weight compared to those on a low-mercury diet. The amylolytic activity (total activity of enzymes hydrolyzing starch: α-amylase, glucoamylase, and maltase) and proteolytic activity (total activity of serine proteinases) in the fish intestine were assessed. Magnetic exposure had contrasting effects on enzyme activity, depending on mercury levels in the diet. Fish fed the low-mercury diet exhibited decreased amylolytic activity following magnetic field exposure, while fish on the high-mercury diet showed increased activity. Proteolytic activity followed a similar pattern, with opposite effects observed between the two dietary groups. These findings suggest that mercury accumulation alters the biological response to magnetic fields, possibly through compensatory biochemical mechanisms. Understanding the interactions between toxic substances and magnetic fields is critical for improving environmental risk assessments.