EAI Endorsed Transactions on Digital Transformation of Industrial Processes
Digital transformation has given rise to a series of new innovative production processes, integration of supply chains for decision making, performance evaluation, optimization and adaptation, as well as greater complexity in the development of innovative products, simulation of complex behaviors and development of sophisticated equipment, on using advanced techniques and tools, as machine learning, artificial intelligence, Big Data And Analytics, Autonomous Robots, Industrial Internet Of Things (IIoT), Design of Cyber-Physical Production Systems (CPPS), Simulation/ Digital Twin, e-Maintenance, Augmented Reality, Additive Manufacturing and Systems Interoperability. All the papers developed on the mentioned above topics are welcome to this journal. Indexing: CrossRef, Semantic Scholar, The Lens eISSN: 3106-0536
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Organisational Continuity and Human-Centred AI: Rethinking Governance in Ageing Digital Economies
INTRODUCTION: Digital transformation is often associated with technological sophistication, data availability, automation, and Artificial Intelligence ( AI)-mediated coordination. However, organizations may preserve more information while losing the human and institutional capacity to interpret, govern, and use that information over time. This risk is especially relevant for small and medium-sized enterprises (SMEs), where tacit knowledge, contextual judgment, and governance memory are often concentrated in experienced professionals. OBJECTIVES: This paper reframes digital transformation as an organizational continuity governance problem. It aims to explain how AI-mediated coordination, demographic aging, distributed work, and concentrated tacit knowledge may generate continuity asymmetry, understood as the gap between increasing technological coordination capacity and insufficient hu man-centered mechanisms fo r preserving interpretive continuity. METHODS: The study develops an integrative conceptual analysis based on selective theoretical synthesis. It connects digital transformation, organizational memory, knowledge transfer and knowledge loss, sociomateriality, human-centered AI, AI governance, aging workforce research, and productive longevity to build a sociomaterial continuity perspective. RESULTS: The paper distinguishes informational continuity from interpretive continuity and conceptualizes experienced professionals as continuity actors who sustain tacit knowledge, relational memory, contextual judgment, and governance understanding across time. It proposes the Sociomaterial Continuity Governance Framework, organized around four mutually reinforcing governance functions: knowledge risk and memory mapping, continuity actors and intergenerational transfer, human oversight of AI-mediated coordination, and modular continuity routines and audits for SMEs. The paper also formulates theoretical propositions to guide future empirical research. CONCLUSION: Sustainable digital transformation depends not only on expanding technological capability, but also on preserving the interpretive conditions through which organizational knowledge remains meaningful, transferable, and governable. The article contributes by positioning organizational continuity as a sociomaterial governance achievement and by offering a conceptual framework for reducing continuity asymmetry in AI-mediated and aging SME environments.
Copyright (c) 2026 Teresa Dieguez, Jackson Barreto https://creativecommons.org/licenses/by-nc-sa/4.0
A Two-Phase Hybrid Metaheuristic Framework for Engineering Optimization
Population-based optimization metaheuristic algorithms generate a pool of candidate solutions in the “Initialization” phase and these approximate solutions are iteratively refined further in the subsequent “Improvement” phase(s) towards the optimal/near-optimal solution. Any population-based algorithm may have a single or multiple “Improvement” phase(s). This paper analyses the impact of having two improvement phases. Different updating expressions are considered in each phase of the algorithm. In one case, the “C-Sine” algorithm, two new untested expressions are used, and performance is analysed. In the other case, the better performing Grey Wolf Optimizer (GWO) is applied in the first phase, and a new updating trigonometric expression is used in the second phase (termed as GWO:SineL algorithm) and analysis is carried out. The second phase applies the trigonometric "Sine" function over the random numbers generated using the Levy Flight Strategy. Mathematical functions, the CEC2019 dataset and a few real-world engineering problems are used for the analyses. Finally, the application of the “C-Sine” algorithm for solving multi-objective problems and the “GWO:SineL” algorithm for supply chain problems are studied. Codes are generated in MATLAB and run on an i5 PC with 4 GB RAM.
Constrained and Unconstrained Optimization
Copyright (c) 2026 Baskar A, Justine Yasappan, M. Anthony Xavior, Anna Burduk, Suthep Butdee https://creativecommons.org/licenses/by-nc-sa/4.0
Bridging Epidemiological Modelling and Quality Engineering: Optimal Mitigation of Viral Contagion
Industry 5.0 is based on the digital technologies of Industry 4.0. However, it shifts the main focus from pure efficiency to a sustainable, resilient, human-centred approach. Since quality control and waste reduction are essential to sustainability, this exploratory study proposed an innovative approach: the adaptation of epidemiological models to quality control. Specifically, this paper investigates how the classical Susceptible-Infected-Recovered (SIR) model, usually applied to biological disease and information transmission, can be also adapted to determine the optimal moment to apply decisions of quality control and, or, predictive maintenance interventions. The methodology formulated an Optimal Control (OC) problem which is first validated using an empirical case of disinformation spread and solved numerically through an indirect method (using the CasADi software). Results show that OC strategies were able to minimise the system's global cost as well as reduce the number of infected individuals by 8.46\%, when comparing to a non controlled scenario. It is concluded that, by transferring this mathematical framework to physical manufacturing production lines, conceptualizing the propagation of defects in processes or components as an infectious phenomenon, engineers and managers can be equipped with a quantitative, data-driven tool. This allows the optimisation of timely interception of defects at their source, ensuring a sustainable reduction of industrial waste as well as the value of operators’ decisions within production centres in the context of Industry 5.0.
Copyright (c) 2026 Daniel Botelho, M. Teresa Monteiro, Senhorinha Teixeira https://creativecommons.org/licenses/by-nc-sa/4.0
Integrating Industry 4.0 Technologies into Project Management: A Framework Proposal
INTRODUCTION: The integration of Industry 4.0 technologies and Project Management represents a paradigm shift from traditional, experience-based practices to data-driven and intelligent systems. By leveraging advanced tools such as Artificial Intelligence, Big Data analytics, Internet of Things and Digital Twins, Project Management processes become increasingly predictive, adaptive and efficient. OBJECTIVES: The objective of this study is to understand the main impacts of Industry 4.0 technologies and tools on the improvement of Project Management. There is limited research on the impacts and improvements brought by Industry 4.0 in Project Management, making it an interesting gap to study. It is also intended to propose a conceptual framework to guide the integration of Industry 4.0 tools into Project Management practices. METHODS: A Systematic Literature Review was conducted using a mixed methodological approach, combining an inductive investigation approach, an exploratory research nature and grounded theory as the research strategy. RESULTS: The results indicate that the application of Industry 4.0 technologies and tools improves Project Management. Regarding the technologies and tools of Industry 4.0 applied to enhance Project Management outcomes, Artificial Intelligence, Big Data and Building Information Modelling emerged as the key ones. Among the main impacts and improvements resulting from the integration of these technologies into Project Management, the enhancement of the decision-making process and tasks automation stands out. A correlation matrix was developed between Industry 4.0 technologies and the impacts/improvements generated in Project Management. Additionally, a framework for integrating Industry 4.0 and Project Management was proposed, based on the principles of continuous improvement. CONCLUSION: This framework is relevant for researchers, organizations, project managers and their respective teams who aim to enhance Project Management in the Industrial Revolution paradigm.
Copyright (c) 2026 Lucas Gomes, Ana Cristina Ferreira https://creativecommons.org/licenses/by-nc-sa/4.0
Regression analysis of the effect of Ge and Si on the torsional strength and fracture angle of aluminium alloys using one-way ANOVA
INTRODUCTION: Aluminium alloys represent a highly sought-after category of structural materials, ranking just below iron-carbon alloys in their application within the manufacturing sector. In this study, the effect of germanium as an oxide (GeO2) form and Si, introduced as modifying additives into aluminium casting alloys on their torsional strength was investigated. OBJECTIVES: Aluminium alloys ZL201 and AA5056 were selected as a study objective which are widely used in the production of shaped castings. METHODS: During the study, germanium as an oxide (GeO2) form and silicon were introduced into the composition of selected aluminium alloy grades in various combinations, after which their torsional strength was tested using WP 500 testing equipment. Specimens melted in an induction furnace and cast at 720 °C into sand-clay moulds. The moulds were prepared as standardized specimens. During the study, the torsional failure angle and torsional strength of the aluminium specimens were determined. These parameters were calculated using a computer program linked to the WP 500 testing equipment. The effect of adding germanium and silicon on torsional strength and fracture angle of ZL201 and AA5056 aluminium alloys was investigated. One-way ANOVA was used to verify the reliability of the experimental tests. RESULTS: It was found that the torsional strength of the alloy increased under the influence of germanium and silicon; however, when the germanium content exceeded 2%, the torsional strength of the studied aluminium alloys decreased. Based on the obtained results, graphs of the fracture angle and torsional strength as a function of the germanium and silicon content in the alloy were constructed. CONCLUSION: Obtained results demonstrated that the addition of GeO2 with Si led to significant grain refinement, dispersion of eutectic and intermetallic phases, and improvement in structural homogeneity. The findings highlight the existence of a well-defined optimal Ge% concentration around 2%, at which both frictional and mechanical properties are maximized.
Copyright (c) 2026 Umidjon Mardonov, Sarvar Tursunbaev, Nigora Rizaeva, Nuritdin Tadjiev, Nargiza Mahkmudova https://creativecommons.org/licenses/by-nc-sa/4.0
Application of Artificial Neural Networks for Quality Classification in Electromobility Manufacturing Processes
INTRODUCTION: Modern manufacturing requires proactive, data-driven quality control methods that support process stability, reduce non-conformities and improve product reliability. OBJECTIVES: The objective of this paper is to evaluate the applicability of selected artificial neural network models for quality classification in an electromobility-related manufacturing process. METHODS: The study used empirical production data, SIPOC process analysis and five neural network models evaluated with accuracy, error rate, validation cost, operational indicators, confusion matrices and ROC/AUC analysis. RESULTS: The analysed models achieved validation accuracy above 95%, with the Narrow Neural Network obtaining the best overall result of 97.0% accuracy, 3.0% error rate and the lowest validation cost. CONCLUSION: The results confirm that artificial neural networks can effectively support quality classification and proactive quality management in electromobility manufacturing processes.
Copyright (c) 2026 Paula Czarnik, Katarzyna Antosz, Jose Mendes Machado https://creativecommons.org/licenses/by-nc-sa/4.0
Modern Approaches to Monitoring the Technical Condition of Heavy-Duty Gears
INTRODUCTION: Gear transmissions of heavy-duty drive reducers operate under variable loads that cause fatigue damage (pitting and tooth fracture), therefore effective condition monitoring is essential to prevent failures.. OBJECTIVES: To conduct a review of the directions and methods for monitoring the technical condition of gearbox gears, to select an appropriate monitoring approach for the gears of high-power machine drives, and to propose the most effective method taking into account operating conditions and gear tooth control zones. METHODS: The research employs an instrumental monitoring approach based on measuring the surface hardness of gear teeth using portable hardness testers. The methodology involves comparative analysis of metal hardness in two critical zones: the tooth end faces and the working surfaces. RESULTS: Based on the conducted review, a method for monitoring the technical condition of gearbox gears in high-power drives has been developed. This method can be applied both in workshop and field operating conditions, enabling inspection of the working and non-working tooth surfaces with or without gearbox disassembly. CONCLUSION: The developed monitoring method for gearbox gears of high-power drives will serve as a foundation for the development of a method for predicting the remaining service life of gearbox gears in high-power machine drives
Methods of Technical Condition Monitoring
Copyright (c) 2026 A. V. Gaydamaka, V. V. Klitnoi, V. V. Klitnoy, O. I. Naumov https://creativecommons.org/licenses/by-nc-sa/4.0
Digital Product Passport for Machine Tools as an Information Core of Sustainable Manufacturing Engineering
INTRODUCTION: The transition toward Industry 5.0, sustainable manufacturing, and the European Digital Product Passport (DPP) concept creates new requirements for machine-building enterprises and engineering education. Modern machine tools are no longer considered isolated manufacturing units but complex cyber-physical systems generating continuous technological, operational, and diagnostic information throughout their lifecycle. In this context, the integration of Digital Product Passports into manufacturing systems becomes a key factor in ensuring traceability, sustainability, adaptability, and efficient lifecycle management. OBJECTIVES: The objective of this paper is to develop a conceptual and practical framework for a Digital Product Passport of machine tools as an integrated information core for sustainable manufacturing engineering, lifecycle-oriented traceability, and technological decision-making within Industry 5.0 environments. METHODS: The study is based on system analysis, lifecycle-oriented engineering approaches, digital manufacturing concepts, and information-flow modeling. A structured mathematical representation of technological information exchange between machine tool modules, technological parameters, monitoring systems, and decision-making units is proposed. The framework integrates CAD/CAM/CAE environments, QR-based identification, sensor data acquisition, cloud-oriented information support, and Alicona-based surface metrology according to ISO 25178. RESULTS: A hierarchical Digital Product Passport architecture for machine tools was developed and experimentally validated using QR-linked engineering information structures and high-resolution surface metrology. The proposed model enables continuous synchronization of geometric, technological, operational, and diagnostic information throughout the machine lifecycle. Alicona InfiniteFocus G5 measurements confirmed the formation of stable microrelief structures suitable for reliable QR-code readability and engineering traceability. The developed framework additionally integrates Digital Twin structures, lifecycle-oriented monitoring, and educational engineering datasets within the Erasmus+ MechDiTS project, supporting Industry 5.0-oriented engineering education. CONCLUSION: The proposed Digital Product Passport framework transforms machine tools into information-centric cyber-physical systems capable of supporting sustainable manufacturing, lifecycle traceability, predictive maintenance, and adaptive technological management. The integration of industrial engineering datasets, Digital Twins, and lifecycle-oriented information environments into engineering education creates new opportunities for preparing specialists capable of operating within data-driven Industry 5.0 manufacturing ecosystems.
Copyright (c) 2026 Sergey Dobrotvorskiy, Yevheniia Basova, Paweł Zawadzki, Mikołaj Kościński, Ruslan Mygushchenko, Oleksandr Permiakov, Brita Pyttel, Houssem Badreddine https://creativecommons.org/licenses/by-nc-sa/4.0
Study of the influence of increased productivity on the efficiency of separation and dewatering of polydispersed coal enrichment sludges in a modular installation with filtration and sedimentation centrifuges
Coal enrichment processes are accompanied by the formation of a significant amount of polydisperse sludges, the effective separation and dewatering of which is an important technological and environmental problem. The aim of this work was to study the influence of increasing the module productivity (increasing the solid phase flow rate by three times – up to 5.523 t/h on a dry basis) on the efficiency of separation and dewatering of polydisperse anthracite sludges of class 0–3 mm in the FGU-600 + OGS-469 modular installation. It was established that at increased load, the filtration centrifuge ensures the extraction of granular fraction with a moisture content of 8.8 % and ash content of 16 %, while the sedimentation centrifuge provides dewatering of fine fractions to a moisture content of 21.35–24 % at a solid phase concentration in the effluent of 2.5 g/L. Comparative analysis with previous results showed the preservation of high efficiency of the technological scheme, a slight improvement in the moisture content of the granular product and an acceptable increase in the loss of fine fractions. The obtained data confirm the possibility of scaling the proposed technology to industrial conditions.
polydisperse sludges; coal enrichment; filtration centrifuge; sedimentation centrifuge; separation; dewatering; modular installation.
Copyright (c) 2026 A. Shkop, A. Ponomarova, O. Shestopalov, A. Bosiuk, V. Babenko https://creativecommons.org/licenses/by-nc-sa/4.0
Study of the efficiency of additive manufacturing for clamping mechanisms in technological equipment
INTRODUCTION: This paper investigates the applicability of modern additive manufacturing (AM) technologies to the production of components for clamping and locating (workholding) devices used in mechanical engineering. Using a modular machine-tool fixture as a case study, the feasibility of replacing metallic parts with engineering polymers, including fiber reinforced grades, manufactured by AM is assessed. STUDY OBJECT: A two arm workpiece clamping lever was selected for analysis as the most highly loaded element of the fixture used for machining a “crosspiece” part. METHODS: Based on recommended cutting conditions, the cutting force components were determined, and the required clamping forces were calculated for several scenarios representing a potential reduction in holding capacity (clamping weakening). These loads served as input for strength and stiffness calculations. Equivalent stresses and deflections were evaluated for the baseline steel lever and for alternative designs made from the most common AM polymers. RESULTS: The study shows that polymer levers with the original geometry do not satisfy allowable stress limits and stiffness requirements. Therefore, the lever geometry was refined by modifying the cross section to achieve the required strength and rigidity under operating loads. Among the materials considered, the optimized lever made from PA6 GF30 provides material savings and lower manufacturing cost while maintaining adequate strength and deformation limits. CONCLUSIONS: The results confirm the feasibility of using fiber reinforced engineering plastics in additive manufacturing for producing components of clamping and locating fixtures. Further work is required to assess AM fabrication of other, less highly loaded parts and to quantify the influence of print induced anisotropy and process parameters on mechanical performance.
Copyright (c) 2025 Ihor Yakovenko, A. V. Pokhil https://creativecommons.org/licenses/by-nc-sa/4.0
Analysis of Temperature Distribution During Grinding with Variable Wheel Feed
INTRODUCTION: Creep-feed grinding of ultrahard cutting tool materials such as cermet presents significant thermal challenges due to extended contact arc lengths between wheel and workpiece combined with low thermal conductivity of the workpiece material. These conditions lead to dramatic temperature rises and potential burnout phenomena that compromise grinding quality and wheel life. OBJECTIVES: This study aims to experimentally investigate grinding temperature distribution within the contact arc during creep-feed grinding of cermet workpieces, identify critical burnout conditions, and determine optimal grinding parameters to enhance cooling effectiveness and prevent thermal damage. METHODS: Grinding temperature measurements were conducted using an embedded thermocouple method during creep-feed grinding of Ti(C,N) cermet alloy with hybrid-bonded diamond wheels. Temperature distributions along the contact arc were measured across various combinations of worktable speeds (40–80 mm/min), wheel speeds (14.7–62.9 m/s), and wheel diameters (140 mm and 200 mm). The onset and progression of burnout phenomena were systematically documented and analyzed. RESULTS: Experimental results revealed that burnout occurs when grinding temperature reaches approximately 150°C, causing abrupt temperature increases exceeding 400°C. Burnout initiates at the rear of the contact arc and progressively extends throughout the grinding zone. A critical worktable speed exists above which burnout occurs under constant depth of cut and wheel speed conditions. Smaller wheel diameters and optimized wheel speeds significantly improve cooling effectiveness. At wheel speed of 41.9 m/s, burnout was nearly eliminated with maximum temperatures maintained below 150°C along the entire contact arc, resulting in reduced grinding forces and wheel wear. CONCLUSION: The research demonstrates that strategic control of grinding parameters, particularly wheel speed optimization and wheel diameter selection, effectively prevents burnout and enhances cooling performance in creep-feed grinding of difficult-to-grind cermet materials. Higher wheel speeds improve grinding fluid circulation and cooling effectiveness up to an optimal threshold, beyond which excessive heat generation and reduced fluid supply negate these benefits.
Copyright (c) 2025 Roman Strelchuk, I. Riabenkov https://creativecommons.org/licenses/by-nc-sa/4.0
Industry 4.0 and Sustainability: A Systematic Review on Advanced Technologies and Collaborative Models
INTRODUCTION: Industry 4.0 (I4.0) technologies demonstrate strong potential to enhance sustainable industrial practices; however, their holistic and integrated application across sustainability dimensions remains insufficiently explored in the literature. OBJECTIVES: This paper aims to analyse how Industry 4.0 technologies contribute to sustainability across environmental, economic, and social dimensions, identifying dominant technologies, sectoral adoption patterns, existing gaps, and alignment with the United Nations Sustainable Development Goals (SDGs). METHODS: A systematic literature review of 32 peer-reviewed articles published between 2015 and 2025 was conducted using the PRISMA framework. The study combines bibliometric analysis with qualitative content analysis to assess technological contributions, sectoral applications, and sustainability outcomes. RESULTS: The results show that IoT and Artificial Intelligence are the most frequently adopted I4.0 technologies, predominantly contributing to environmental (78% of studies) and economic sustainability. Social sustainability remains underrepresented, appearing in only 25% of the analysed studies. Manufacturing and energy sectors lead adoption, while agri-food and construction sectors lag despite their high potential. Major challenges include data privacy risks associated with IoT and scalability limitations of Blockchain technologies, with mitigation strategies focusing mainly on SME-oriented adoption models. CONCLUSION: The findings highlight the need for a more balanced deployment of Industry 4.0 technologies that equally addresses environmental, economic, and social dimensions of sustainability. Future research should prioritize the development of measurable social impact indicators and integrated technological–policy frameworks to support inclusive and sustainable industrial transformation aligned with the UN SDGs.
Copyright (c) 2025 Leonel Patrício, Leonilde Varela https://creativecommons.org/licenses/by-nc-sa/4.0
Assumptions for the Application of Internet of Things Solutions in a Smart Factory Demonstrator
The article presents the role and applications of the Internet of Things (IoT) within the concept of a smart factory, with particular emphasis on the Smart Factory laboratory demonstrator at Poznan University of Technology. Key IoT functions are discussed, including the integration of sensors, information systems, and actuators in order to fully digitise production processes. The demonstrator enables replication of real industrial processes, such as production flow control, material inventory supervision, and monitoring of technical equipment operations. The article presents the architecture of the IoT infrastructure, the use of RFID technology, and the implementation of predictive maintenance based on sensor data analysis. The results indicate that IoT implementation significantly increases operational efficiency, improves production quality, and reduces downtimes, although it requires overcoming organisational and technological barriers. The Smart Factory demonstrator serves as a practical platform for research and education in the area of Industry 4.0.
Copyright (c) 2025 Marta Białek, Krzysztof Żywicki https://creativecommons.org/licenses/by-nc-sa/4.0
Experimental Investigation of Localised Laser Heat Treatment Effects on 6XXX Aluminium Alloys: Critical Insights into Numerical Modelling
To meet global emission reduction targets, high specific strength materials, such as 6XXX-series aluminium alloys, are integrated into automotive structures. Although self-piercing riveting (SPR) is a highly adopted joining technique in the referred industry, the low cold formability of these alloys can lead to failure in the sheet material. Thus, a promising solution is the application of a localised laser heat treatment that will promote the local softening of the material and, therefore, prevent its failure. A prior numerical study introduced localised laser heat treatment to solve the bottom sheet failure problem but simplified it by assuming homogeneous hardness throughout the treated thickness and strict confinement of the softened zone. However, these simplifying assumptions lack rigorous experimental validation. To address this gap, this work aims to assess the validity of these simplifications. For this purpose, an experimental analysis was conducted on four samples of AA6063-T6 subjected to a localised laser heat treatment applied to an annular region defined by internal and external diameters of 4 and 7.5 mm. The resulting hardness profiles were subsequently analysed. Results demonstrate that the laser-induced softening was not confined to the intended annular region and did not occur uniformly through the sheet thickness. These findings reveal discrepancies between the real softening behaviour and the assumptions adopted in the numerical model.
Copyright (c) 2025 Margarida Fernandes, Senhorinha Teixeira, Nuno Peixinho https://creativecommons.org/licenses/by-nc-sa/4.0
Future of the Internet: Perceptions, Content, and Emerging Facilities
INTRODUCTION: The Internet has transformed communication, content sharing, and expectations for digital futures. This study explores current practices and perceptions regarding online content and emerging Internet facilities. OBJECTIVES: To identify sharing patterns across content types, assess perceptions of the Internet’s role, and evaluate attitudes toward future technologies. METHODS: A survey was administered to Internet users. Descriptive statistics and k-means clustering were applied to responses on sharing behaviours and perceptions of future Internet developments. RESULTS: The study shows that online sharing practices are largely private, with public sharing concentrated in visual media, and three distinct user profiles identified through clustering. Perceptions of the Internet emphasized its professional consequences, global connectivity, and a shift toward predictive, video-centered environments. Attitudes toward emerging technologies revealed support for AI and connectivity but caution toward biometrics and immersive interfaces, with three attitudinal groups—skeptics, realists, and enthusiasts—capturing this heterogeneity. CONCLUSION: Content sharing is context-driven, reflecting both privacy concerns and social expression. Users anticipate a video-centered, AI-enhanced, and globally unifying Internet, while remaining wary of technological overreach.
Copyright (c) 2025 Helena Sofia Rodrigues https://creativecommons.org/licenses/by-nc-sa/4.0
A Block chain and Neural Network Approach to Enhancing Reverse Logistics of Electronic Gadget Life Cycle Tracking
INTRODUCTION: The significance of handling Reverse Logistics (RL) within the electronic goods sector has increased due to the emphasis on eco-friendly product disposal and the retrieval of components, driven by environmental concerns and regulatory demands. The refurbished and used mobile phone market size is valued at US$ 53.81 billion in 2022 and is expected to grow at a CAGR of 10.8% during the forecast period, reaching US$ 120.96 billion by 2030. With a lot of emphasis given on the RL aspects. OBJECTIVES: This paper proposes a framework that uses blockchain based Hyperledger that records the life cycle history of the electronic gadget on an immutable ledger. Additionally, a neural network helps to calculate the quality index of the gadget and also the price. METHODS: Quality Index (QI) considers various sensory data into account and estimates the status of the gadget with certain accuracy. Smart contract provides automated transaction options between targeted stakeholders that helps to mitigate the security issues that happened among the stakeholders of the RL process. The integrated frameworks act as a decision maker for evaluating the condition of the mobile product or further movement into the RL process. RESULTS: In all, the traceability of product life cycle history assists in ensuring the quality of the returned products therefore optimizing the traditional quality inspection processes involved in reverse logistics of the electronic goods sector. The integration of blockchain and neural networks creates a robust and private ecosystem for stakeholders involved in reverse logistics. CONCLUSION: The stakeholders of the entire mobile electronic gadget experience a transparent system that helps to achieve sustainable reverse logistics principles.
Copyright (c) 2025 Veera Babu Ramakurthi, Sarita Prasad, Ankit Kumar Rai, Vijaya Kumar Manupati https://creativecommons.org/licenses/by-nc-sa/4.0
The Role of Numerical Simulation in Fire Door Testing and Certification
Passive fire protection is indispensable when designing and constructing safer buildings, particularly in industrial and public facilities, where consequences of a fire can be catastrophic. Although their purpose is not to extinguishing a fire, passive fire protection aims to delay flame and smoke propagation, gaining valuable time to save lives and goods. Fire doors are amongst the most common protection devices, and they must undergo strict certification tests to ensure they meet fire resistant standards. Such tests are expensive and time consuming, often carried out at longer distances in specialized facilities. Computational tools could provide a cost effective alternative to direct the design towards a viable product. Thus, with the objective of assessing the effectiveness of computational tools in the certification processes, a numerical model of a single-leaf sandwich door was developed. The simulations were carried out in Ansys Mechanical, using a one-way coupling approach between transient thermal and transient structural analyses. A key aspect of the model is the temperature-dependent convective heat transfer coefficient on the unexposed face, which greatly influences the predicted temperature evolution. From the numerical results, one may infer the maximum temperature of the door, as well as its displacements at the upper and lower edges. Thus the numerical model highlights the potential of using this methodology by allowing not only the temperature evolution to be recorded throughout the test at specific positions of the assembly, according to the standards, but also the identification of potential gaps between the door and the frame, which is critical for fire and smoke containment. Future work includes conducting an experimental trial to validate the model and refine the heat transfer parameters for improved predictive accuracy.
Copyright (c) 2025 Margarida Fernandes, Ana Catarina Araújo, João Silva, Nelson Rodrigues, Senhorinha Teixeira, José Carlos Teixeira https://creativecommons.org/licenses/by-nc-sa/4.0
Influencing the SME of PVA films with integrated iron nanoparticles using ultrasound during production
In today's world, shape memory polymers (SMPs) have already found application in a wide range of domains, including medical engineering, control technology, and others. The ability of the polymers to "remember" a stored shape is provoked by different stimuli such as light, pH, temperature or other factors. A widely studied SMP is polyvinyl alcohol (PVA), which is characterised by biocompatibility, high hydrophilicity, non-toxicity, and efficient shape memory behaviour. The present study investigates the influence of energy input via ultrasound technology in the polymer mixture on the shape memory effect of PVA with integrated iron nanoparticles. In order to achieve this objective, the strain recovery capacity and strain fixity rate at varying levels of energy input were experimentally determined. Thermomechanical analysis (TMA) was the method of choice for gaining insights into this phenomenon. A decline in the strain fixity rate was observed in PVA films with integrated iron nanoparticles, as the energy input increased. This renders the process of storing "new" shapes more challenging. No alteration in strain recovery was observed when energy was introduced by ultrasound.
Copyright (c) 2025 Anette Lang, Alexandru Sover https://creativecommons.org/licenses/by-nc-sa/4.0
Integrating LeanDfX into PLM Tools to Enhance Efficiency and Effectiveness in Product Development
In an era of sharp competition and increasing customer expectations, optimizing the product design and development process is critical to deliver value-added solutions. This study explores the application of a PLM tool with a Lean (Design for Excellence) module integrated to enhance product development efficiency and effectiveness. The LeanDfX module incorporates lean principles with advanced design for X methodologies, enabling the systematic identification and incorporation of value-generating features into products. The proposed methodologies were embedded into a software platform based on versatility, ensuring its flexibility and adaptability to diverse industrial sectors and product categories. These features allow companies across various sectors to benefit from streamlined processes, enhanced collaboration, and reduced development cycle times. A baseline assessment of existing processes was then conducted, serving as a reference point to evaluate performance gaps and opportunities. Building on these insights, corrective and improvement actions were applied, directly informed by the baseline findings. In Case Study 1, the implementation of LeanDfX led to an increase in efficiency, achieving a 34.67% improvement in the displacement and 97.67% enhancement in mass, values that were critical in the baseline analysis. In Case Study 2, the implementation of LeanDfX demonstrated an increase of efficiency through a 98.84% improvement in the Gripping Area of 150 mm², an upgrade in Gripping Area of 550 mm² of 27.32% and a 67.02% enhancement in mass, values that needed to be addressed to ensure the proper functionality of the gripper. By incorporating baseline-driven evaluation and iterative refinement, the approach demonstrated that PLM serves both as a tool for continuous improvement in existing systems and as a strategic framework for guiding innovation during product conception and development.
Copyright (c) 2025 Teresa Gonçalves, Rita Ramalho, Bruno Sousa, Raul Campilho, Pedro Vieira, Rafael Cardoso, Inês Caetano, Alvaro Gouveia, Fillipe Ribeiro, Flávia Barbosa https://creativecommons.org/licenses/by-nc-sa/4.0
Integrating digital twins into smart city infrastructure: enhancing urban planning through real-time data analytics
INTRODUCTION: Digital twin technology is at the head of smart city innovations; rapid urbanization and the rising complexity of infrastructure management have driven this technology. Real time virtual replicas of the physical urban systems - digital twins - simulate urban scenarios, monitor conditions, and improve resource allocation more accurately than ever before. Rigorous empirical studies that quantify the impact of these technologies on the efficiency of urban planning are rare, curbing evidence-based policy making and large-scale adoption strategies. OBJECTIVES: This study estimates a panel fixed effects econometric model that quantifies the impact of integrating digital twins on urban planning performance of five European countries (Estonia, Germany, Portugal, the UK, Poland) over the period of 2020–2024. METHODS: Using data from the OECD Smart Cities database, the European Commission Urban Data Platform, the World Bank’s Worldwide Governance Indicators, and national municipal APIs, annual data were assembled on a composite urban efficiency index, a digital twin integration score, real time data usage, per capita infrastructure investment, governance quality metrics, and population density. The model employs city and time fixed effects and addresses heteroskedasticity and serial correlation with clustered robust standard errors. RESULTS: The efficiency index of Germany improved from 53,52 in 2020 to 62,57 in 2024; Poland from 45,51 to 58,73; and Estonia dropped sharply from 70,22 to 39,44. Coefficients on digital twin integration (β₁ = 12,00) and governance quality (β₄ = 8,00) are positive and statistically significant. Real-time data use (β₂ = 0,08) and infrastructure investment (β₃ = 0,004) increase efficiency, while population density has a slight negative effect (β₅ = –0,005). CONCLUSION: This underscores the importance of the network and governance relationships of digital twins in realizing their potential. Future research should include non-European cities, apply spatial econometric techniques to account for intercity spillovers, and use dynamic panel models to assess feedback loops from past efficiency gains and their influence on future technology investment.
Copyright (c) 2025 Olha Prokopenko, José Machado, Artem Koldovskiy, Marina Järvis, Anna Chechel, Tetiana Skibina https://creativecommons.org/licenses/by-nc-sa/4.0
Inclusive Monitoring in Dementia: Progress Towards a Wrist Wearable Based Solution
INTRODUCTION: The rapidly aging global population presents significant challenges, particularly in managing neurodegenerative diseases such as dementia. Addressing these challenges requires innovative solutions that ensure continuous care and monitoring of elderly individuals. Wearable technology offers a path to more inclusive and efficient care for older adults living with cognitive decline. OBJECTIVES: This paper presents the design, development, and refinement of an integrated wrist-worn wearable system to support the monitoring and care of elderly people living with dementia, expanding a previous proof of concept into a compact and energy-efficient prototype optimized for real-world usability. METHODS: The proposed system integrates advanced sensor technologies. It includes optical (MAX30101), inertial (BMI270), and GNSS (CAM-M10Q) sensors for monitor heart rate, blood oxygen levels, motion, and location. Data are transmitted via Bluetooth Low Energy (BLE) for indoor communication and LoRa for outdoor connectivity. A custom 4×4 cm PCB was designed to consolidate all components—microcontroller, sensors, power efficiency, communication modules, and display—into a single compact and robust layout. The design process also emphasized ergonomic comfort and usability through iterative prototyping and 3D modelling. RESULTS: The refined prototype significantly reduced the physical dimensions compared with the original proof of concept, while improving power efficiency, mechanical stability, and user comfort. The device layout and button interface were optimized to ensure intuitive interaction and safe daily use by elderly individuals. CONCLUSION: The developed prototype establishes the foundation for a fully functional, energy-efficient wearable system tailored to dementia monitoring. Future work will include firmware optimization, extended functional validation, and the integration of intelligent analytics for behaviour recognition and anomaly detection, aiming to provide caregivers with timely and actionable health insights.
Copyright (c) 2025 Marcelo Arantes, Inês Rocha, Mariana Carvalho, Ana Freitas, José Soares, Marta Pinto, Demétrio Matos, Pedro Morais, Vìtor Carvalho https://creativecommons.org/licenses/by-nc-sa/4.0
Organizational Maturity and its Impacts in Product Development
INTRODUCTION: Life-cycle extension strategies have become critical enablers of circularity in product development. By emphasizing product longevity, reusability, repairability, and recycling capacity, these strategies aim to minimize resource consumption and waste generation while fostering sustainable value creation. OBJECTIVES: This research paper aims to explore approaches for extending product life cycles across all phases, from design through manufacturing, use, and end-of-life. For companies seeking to extend the longevity of their products, it is essential to understand the main principles in product life cycle extension (PLCE) but also to evaluate their maturity before implementing such strategies, in order to be sure that the best decision will be made. In this context, this work introduces an innovative methodology designed to support decision-making process by helping companies identify critical aspects that influence successful PLCE. METHODS: The present study explores principles relevant to PLCE, including durability, modularity, repairability, reusability, and recyclability. In addition, the role of emerging technologies such as artificial intelligence and additive manufacturing in supporting PLCE strategies, particularly in applications such as predictive maintenance, is analysed. The PLCE maturity model was developed and an assessment was conducted using a structured questionnaire designed to capture and classify influencing factors across three domains: organizational, cultural, and technological. To validate the methodology, the assessment was applied to three manufacturing companies. RESULTS: The three use cases provided specific answers to the questionnaire that were reflected into different maturity levels. These results highlight the relevance in developing a methodology that support decision-making in product development focused on life cycle extension. The implementation of this structured methodology allowed to release specific guidelines that enable the improvement of PLCE through strategies that targets company’s needs. CONCLUSION: Through a maturity assessment applied in three manufacturing companies, it was possible to characterize the readiness level for PLCE of each one and identify which issues should be worked on and what aspects are already mature in the company. With this knowledge in consideration, the companies can now make informed decisions of their next steps in innovation and management of their products’ life cycle.
Copyright (c) 2025 Bruna Oliveira, Luís Aguiar, Álvaro Gouveia, Raul Campilho, Diogo Sousa, Marco Rodrigues, Flávia Barbosa https://creativecommons.org/licenses/by-nc-sa/4.0
Leveraging Statistical Thinking for Digital Innovation: Reframing Uncertainty in Engineering Decision-Making
INTRODUCTION: Contemporary engineering operates in a data-rich yet uncertainty-laden landscape, particularly under the technological shifts introduced by Industry 4.0. While foundational, deterministic models frequently fail to address the ambiguity, variability, and incompleteness inherent in real-world data, this paper examines the growing need to embed statistical reasoning within digital engineering decision-making processes to ensure robustness and interpretability. OBJECTIVES: The study aims to investigate how statistical thinking contributes to innovation, transparency, and adaptive decision-making in digitalized engineering systems. It identifies conceptual gaps and underexplored themes in current literature and emphasizes the strategic relevance of probabilistic reasoning in addressing uncertainty across complex industrial settings. METHODS: A hybrid scoping review methodology was applied, combining a semantic AI-driven search via Elicit with a structured bibliographic query in Scopus. The resulting corpus of 928 curated publications was analysed through bibliometric techniques and social network analysis using VOSviewer. This comprehensive process enabled the identification of co-occurrence patterns, thematic clusters, and evolving disciplinary linkages, ensuring the credibility and reliability of the findings. RESULTS: Five primary research clusters emerged: decision optimization, risk management and human factors, machine learning integration, digital information systems, and sustainability. These clusters represent key areas where probabilistic modelling and uncertainty quantification can significantly enhance engineering practices. Although AI and big data analytics are increasingly prevalent, the underrepresentation of probabilistic modelling and uncertainty quantification in these clusters reveals a disconnect between data-centric innovation and risk-aware engineering practice. CONCLUSION: A conceptual shift toward probabilistic reasoning is advocated as a necessary response to the complexity of modern digital engineering environments. Repositioning statistical thinking as a central enabler of digital transformation supports the development of resilient, interpretable, and future-ready engineering systems. Integrating these methodologies into engineering curricula, AI pipelines, and industrial decision-support infrastructures is essential for advancing strategic, uncertainty-aware innovation.
Copyright (c) 2025 Celina P. Leão, A. Manuela Gonçalves, M. Teresa Malheiro https://creativecommons.org/licenses/by-nc-sa/4.0
Selected Control Strategies for Nonlinear Technological Processes
Nonlinear dynamics are frequently encountered in industrial processes, where conventional linear controllers often fail to ensure the required performance or safety due to phenomena such as multiple equilibria, limit cycles, and bifurcations, all of which can negatively impact system stability. These challenges necessitate control strategies that can adapt to changing operating conditions and capture nonlinear behavior more effectively. Building on earlier research, this study identifies and evaluates two promising approaches to model-based adaptive control suitable for nonlinear technological processes. Within an established classification framework, two representative strategies are examined: one based on sequential linearization combined with polynomial control, and another employing a direct nonlinear method based on the Wiener model. Experimental validation is conducted on a two-tank benchmark and a continuous stirred-tank reactor, both representing typical nonlinear industrial systems. The results show that sequential linearization in combination with polynomial control achieves faster set-point tracking, quicker convergence, and greater robustness compared to Wiener model control. These findings underscore the advantages of structured sequential linearization techniques within adaptive control and demonstrate their potential as a practical and effective alternative to purely linear control designs in complex nonlinear environments.
Copyright (c) 2025 Eva Gavendová, Jiří Vojtěšek, František Gazdoš https://creativecommons.org/licenses/by-nc-sa/4.0
Breaking Barriers: Digital Personal Assistants for the Inclusion of Workers with Disabilities in Production
INTRODUCTION: This paper introduces Digital Personal Assistants (DPAs) as tools to support inclusive employment for workers with disabilities in industrial production. OBJECTIVES: The aim is to design a DPA that enables barrier-free access to work-related information. METHODS: DPAs are developed for multiple devices, focusing on accessibility, and task support—co-designed with workers with disabilities. RESULTS: A possible implementation of a DPAs is shown in this paper. CONCLUSION: User-centered DPAs can help reduce barriers and support inclusion in production.
Copyright (c) 2025 Markus Brillinger, I. Unterkircher, F. Lackner, C. Zwickl, P. Eisele, D. Peraković, M. Periša, I. Cvitić, P. Teskera, S. Teixeira, J. C. Teixeira https://creativecommons.org/licenses/by-nc-sa/4.0
Circular Economy and Its Place in a Modern Manufacturing Enterprise
The circular economy is a key tool for addressing problems associated with excessive consumption of natural resources and environmental pollution. In the context of a manufacturing enterprise, implementing circularity principles represents not only ecological responsibility but also an opportunity to enhance the enterprise's efficiency and competitiveness. Research article aims to examine how a specific enterprise uses these principles in practice and what impact they have on its production processes and costs. It presents an opportunity to explore how an enterprise reduces waste, recycles materials, and optimises production, thereby contributing to environmental protection and enhancing its competitiveness. In addition, the circular economy enables enterprises to plan more effectively and manage their resources, thereby reducing the need for new raw materials and energy inputs, which has a direct impact on costs and profitability. The implementation of these principles can also positively impact the enterprise's image on the market, as an increasing number of consumers and partners prefer companies that prioritise sustainability and responsible environmental behaviour.
Copyright (c) 2025 Lucia Knapčíková, Rebeka Tauberová, Ivana Klačková, Annamária Behúnová https://creativecommons.org/licenses/by-nc-sa/4.0
A Low-Cost Framework for Textile Yarn Characterization Using Image Processing
The textile industry increasingly demands innovative and cost-effective solutions for yarn quality assessment, as conventional equipment is costly and occupies substantial space. This work presents a compact, low-cost image processing framework to characterize key yarn parameters, providing a foundation for future automated quality control systems. The framework employs classical image processing techniques—smoothing, thresholding, segmentation, and morphological operations—implemented with open-source tools such as Visual Studio and OpenCV. An experimental setup using low-cost hardware enabled the acquisition of high-quality images under controlled conditions. The system extracted parameters including linear mass, average diameter, specific volume, defect quantification, hairiness coefficient, and twist direction and pitch. Tests on three yarn types (cotton and polyester) produced results comparable to the industrial reference Uster Tester 3, with error rates below 7%. The proposed solution offers an affordable alternative for small industries and research laboratories, with potential for future integration of advanced computer vision and artificial intelligence to enhance defect detection and classification.
Copyright (c) 2025 Filipe Pereira, Miguel Oliveira, Filomena Soares, Rosa Vasconcelos, Vítor Carvalho https://creativecommons.org/licenses/by-nc-sa/4.0
Identification of circulation effects in a closed loop of a hydrostatic-mechanical transmissions with a planetary mechanism on the input and output links
INTRODUCTION: Modern trends in the development of mobile machinery, particularly heavy tracked vehicles, tractors and military vehicles, focus on improving the energy efficiency, controllability and reliability of transmission systems. In this context, hydrostatic-mechanical transmissions (HMCVTs) with a closed energy transmission loop demonstrate significant potential as an alternative to traditional mechanical and hydrodynamic systems by combining smooth speed control with increased efficiency. A key feature of closed HMCVTs is circulation power transmission, which arises from the interaction of the hydraulic and mechanical circuits. Using planetary mechanisms on both the input and output links of the transmission creates internal closed power flows, or circulations, which affect torsional rigidity, energy losses and dynamic stability. Currently, the scientific literature pays insufficient attention to identifying circulation effects in transmissions with such a layout. This complicates the design and optimisation of HMCVTs that take internal circulation loads into account, as this can lead to the overloading of individual transmission elements, increasing the temperature in the circuit or reducing efficiency. Therefore, this study is relevant because it provides an in-depth analysis of circulation effects in closed HMCVTs with planetary mechanisms at the input and output of the transmission system. The results of such an analysis could inform the development of methods for identifying and compensating for harmful circulation flows. This would contribute to extending the service life, improving the energy efficiency and enhancing the control accuracy of HMCVTs. OBJECTIVES: This work aims to develop and analyse the mathematical basis for describing circulation effects in the closed loop of the HMCVT, with the planetary mechanism located on the input and output links. This goal can be achieved by identifying patterns and forming a mathematical model of the transmission's operation in a closed loop with the planetary mechanism located on the input and output links. The presented mathematical equations describe changes in the kinematic and power indicators of the transmission. These equations are used to structure the internal gear ratio equation of the closed loop of the HMCVT with the planetary mechanism located on the input and output links of the transmission. METHODS: The study used methods of differential and integral calculus to create a mathematical model of the operation of the hydrostatic-mechanical transmission. This model is based on fundamental equations that describe the kinematic, power and energy indicators of continuously variable transmissions with planetary mechanisms. Due to the means of digitization, namely three-dimensional (3D) technologies, it is possible to determine the patterns of distribution of the internal gear ratio by the hydraulic pump control parameter and the planetary mechanism design parameter. This increases the accuracy of the results obtained. RESULTS: The main results of this study are the development of a mathematical model for analyzing changes in the internal gear ratio of the closed loop HMCVT, based on the control parameter of the hydraulic machine and the design parameter of the planetary mechanism. In addition, the analysis of the obtained results using modern digitalization tools, namely spatial drawings, demonstrated the effectiveness of 3D technologies in determining the optimal combinations of transmission schemes that avoid circulation effects. CONCLUSION: This article concludes that, by using spatial illustration, dependencies of the internal gear ratio on the hydraulic pump control parameter and the design parameter of the planetary mechanism have been constructed. This provides the possibility of a broader analysis of patterns. Based on the analysis of circulation effects in the closed loop of the HMCVT, the range of changes to the planetary mechanism's design parameter has been obtained, which alters the internal gear ratio of the closed loop over a wide range.
Copyright (c) 2025 Ievgenii Veretennikov https://creativecommons.org/licenses/by-nc-sa/4.0
Integrating Human Factors into Protocol Design: Decision Strategies in Digitalized Simulation Environments
INTRODUCTION: In high-stakes industrial and operational contexts, such as aerospace, human factors heavily influence effectiveness under uncertain and dynamic conditions. Integrating simulation-based environments and digital tools in protocol development provides a valuable lens through which to assess cognitive and behavioural responses. As digitalization and Industry 4.0 accelerate the shift toward cyber-physical and human-in-the-loop systems, it becomes essential to understand how decision-making is affected in such digitalized settings. OBJECTIVES: This study aims to analyse the impact of uncertainty, time pressure, and cognitive complexity on human decision-making during protocol implementation in a simulated space mission. It also explores the implications of these insights for the design of digitalized, human-centred protocols pertinent to industrial transformation scenarios. METHODS: A mixed-methods approach was employed. Participants took part in a simulated space mission scenario using pre-defined protocols within a controlled, uncertainty-driven environment. Behavioural responses, timing, deviations, and verbal feedback were recorded and analysed. The quantitative data were supplemented with qualitative insights from participant interviews and session logs. RESULTS: Findings indicate operational uncertainty and time constraints significantly influenced participants’ decision pathways. Deviations from scripted protocols revealed how stress and perceived role clarity influence real-time choices. Furthermore, the analysis contextualizes these findings within Industry 4.0 frameworks, highlighting parallels with decision-making challenges in smart manufacturing, digital twin monitoring, and automated safety systems. CONCLUSION: The results underscore the necessity of embedding human-centred principles in protocol design, particularly when deploying digital technologies in complex, mission-critical environments. The study provides strategic recommendations for integrating adaptive, resilient, and cognitively compatible decision support tools into digitally transformed industrial processes.
Copyright (c) 2025 Mariana Pereira, Celina P. Leão, Susana Costa https://creativecommons.org/licenses/by-nc-sa/4.0
A Structured Methodology for Synthesizing Parameters and Architecture of Robotic Technological Systems in the Digital Transformation of SME Engineering Production
INTRODUCTION: Robotic automation has become a key driver of digital transformation in the engineering sector, especially for small and medium-sized enterprises (SMEs), which face increasing demands for flexibility, efficiency, and cost optimization. However, most classical automation frameworks do not address the structural and economic limitations specific to SMEs. Despite recent advances in modular automation, a gap remains in methodologies tailored to low-volume, high-mix environments typical of SMEs. OBJECTIVES: This paper aims to develop a structured methodology for synthesizing the parameters and architecture of robotic technological systems adapted to the production realities of SME engineering environments. The goal is to balance automation effectiveness with practical investment constraints. METHODS: The proposed approach integrates a multi-level automation model with production system analysis, considering object-specific constraints, part characteristics, and process parameters. The methodology was validated through an expert- and data-driven case study of a Ukrainian SME engaged in serial plastic part machining. Functional-cost analysis and feasibility modeling were used to evaluate automation options. In addition, investment-efficiency mapping was introduced to support strategic planning of implementation phases. RESULTS: The implementation of the proposed system, based on a six-axis robotic manipulator with digital control and vacuum clamping devices, led to a 50% reduction in auxiliary processing time, improved consistency, and reduced labor intensity. The workplace-level automation enabled flexible part handling without the need for major structural changes or high capital investment. The system demonstrated high adaptability to part variations and required minimal operator intervention. CONCLUSION: The developed methodology provides a scalable and economically viable path to robotic automation for SMEs. It supports gradual implementation and can be further enhanced by integrating artificial intelligence tools for decision-making during system design and optimization. This structured framework contributes to the digital resilience of SME manufacturing and aligns with Industry 4.0 principles.
Copyright (c) 2025 Ihor Yakovenko, Yevheniia Basova, Alexander Permyakov, Andrii Pokhil, Victor Sotnychenko, Luís Freitas https://creativecommons.org/licenses/by-nc-sa/4.0