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
This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
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
Modular Internet of Things system for monitoring, control and alerting in refrigeration systems using ESP32 and Raspberry Pi 4
With the rapid development of Internet of Things (IoT) systems and the increasing need for smart monitoring and control, this paper presents a fully functional platform for collecting, analyzing and controlling data from an experimental setup based on a refrigeration system. The main goal is the development of a modular architecture that gathers data from multiple sensors in real time, processes it, and displays it through a web interface. The system detects when key parameters deviate from nominal parameters and automatically sends email alerts to prevent failures and reduce system downtime. The system uses embedded programming on the ESP32-S3 microcontroller to collect data from temperature and pressure sensors. Simultaneously, a Single Board Computer (SBC) Raspberry Pi 4 runs Python scripts that process the data and collect electrical parameters via a dedicated module. Communication between the microcontroller and the SBC is conducted through a Universal Asynchronous Receiver-Transmitter (UART) serial interface. The ESP32-S3 microcontroller handles data acquisition and sends it to the Raspberry Pi 4, which processes and stores the information in a local Stuctrured Query Language Lite (SQLite) database. The platform supports dynamic thermodynamics calculations using the CoolProp library and visualizes results through the web interface. The experimental setup includes a refrigeration system with R404A refrigerant, featuring a square coil evaporator and an air-cooled condenser. The implemented platform gathers real-time data from sensors and the electrical energy measurement module, processes it, and displays it on the web interface. The system enables dynamic monitoring and component control by running a logic-based algorithm that continuously checks values and automatically triggers corrective actions, including email notifications, based on system status. The ESP32-S3 microcontroller includes Pulse Width Modulation (PWM) pins reserved for future connection of frequency converters, designed to regulate the speeds of the compressor and condenser fan. This setup provides a solid basis for developing a smart control system capable not only of passive monitoring but also active intervention on operating parameters, aiming to optimize performance depending on different operating conditions. This method demonstrates not only the modularity of the proposed IoT platform, but also its ability to perform real-time applied thermodynamic analysis and enable direct regulation of the refrigeration installation, bridging the gap between conventional monitoring and intelligent adaptive control, where existing industrial solutions typically lack such thermodynamical calculation-based control.
Copyright (c) 2025 Mihai Florin Bizdadea, George Daniel Ghita, Valentin Gheorghe Apostol, Horatiu Lucian Pop, Bogdan Gramescu https://creativecommons.org/licenses/by-nc-sa/4.0
Reviewing Manufacturing Execution System in Industry 4.0: A Global Approach
INTRODUCTION: Manufacturing Execution Systems (MES) have evolved from basic production tracking tools into intelligent, AI-driven platforms integrated with IoT, cloud computing, and cyber-physical systems. In the context of Industry 4.0, MES play a key role in enabling real-time decision-making and operational efficiency. However, research in this field remains fragmented, with ongoing challenges related to interoperability, cybersecurity, and system integration. OBJECTIVES: This study aims to map the MES research landscape within Industry 4.0 through a systematic literature review and bibliometric analysis. It identifies core research themes, technological trends, and persistent challenges, offering insights to guide future developments in MES. METHODS: A Systematic Literature Review (SLR) was conducted using the Scopus database, applying specific search terms and strict inclusion/exclusion criteria. A total of 47 peer-reviewed articles (2010–2024) were analysed using VOSviewer to perform co-authorship, bibliographic coupling, and keyword co-occurrence mapping. RESULTS: The analysis revealed three main research clusters: MES core technologies, MES integration with Industry 4.0, and comparisons with ERP and SCADA systems. Key trends include the integration of AI, digital twins, and blockchain. Barriers such as interoperability, cybersecurity risks, and adoption costs—especially for SMEs—remain prevalent. CONCLUSION: MES research is advancing toward intelligent, scalable, and interconnected systems. Future work should prioritise standardisation, secure data exchange, and cost-effective models to support broader Industry 4.0 adoption. Cross-disciplinary collaboration will be essential to achieve sustainable and resilient MES implementations.
Copyright (c) 2025 Teresa Dieguez, José Machado https://creativecommons.org/licenses/by-nc-sa/4.0
Stress Factors in Technical Education: Digital Transformation as a Source of Emerging Challenges and Strategic Interventions
INTRODUCTION: Technical educators face both long-standing and emerging stressors amplified by the digital transformation of industrial processes. The introduction of Industry 4.0/5.0 technologies (AI tools, VR/AR labs, digital platforms) increases cognitive and administrative burdens. OBJECTIVES: This study aims to identify shared and technology-specific stressors in technical education and propose evidence-based strategies to mitigate them. METHODS: We performed a systematic review of peer-reviewed articles and conducted a focus groups with 60 technical educators. Data were analyzed using thematic coding. RESULTS: The most frequently reported stressors were administrative overload, curriculum volatility, safety responsibilities, and insufficient digital training. Digital tools like VR and AI systems present both stress-inducing and stress-reducing potential. CONCLUSION: Supporting educators through targeted digital interventions and training programs is vital to sustaining quality technical education during ongoing digitalization.
Copyright (c) 2025 Andrea Hlubuckova, Monika Dobiášová, Petr Valášek https://creativecommons.org/licenses/by-nc-sa/4.0
Research on the Monitoring of Selected Parameters of Technological Devices in the Conditions of a Digital Enterprise
Digitization is a key concept of the Fourth Generation Industrial Revolution. In industrial enterprises, digitization allows for the study of various influences on production processes without the need to intervene in real production. Thanks to technologies such as augmented reality, virtual reality, 3D printing, digital twins, and artificial intelligence, it is possible to conduct research activities monitoring selected parameters without interfering with the production process. Financial resources invested in transforming a traditional company into a digital enterprise ultimately save production costs. Simulation programs enable quick and easy planning of production changes, resulting in more efficient processes and savings in raw materials. The concept of digitization has gained greater importance, especially in connection with events like COVID-19, which affected daily life worldwide. Many companies realized the significant advantage of digitizing production. The pressure of events and economic impacts accelerated the implementation of digitization and intelligent automation. The use of artificial intelligence tools in production enables remote control of manufacturing.
Copyright (c) 2025 Rebeka Tauberová, Lucia Knapčíková https://creativecommons.org/licenses/by-nc-sa/4.0
A Practical Approach to Industrial Digitalization through Data Acquisition and Systems Integration for Predictive Maintenance
The digital transformation of industrial environments requires the ability to collect, process, and integrate data from production systems in real time. However, many manufacturing facilities operate with legacy equipment that is perfectly functional and operational but lacks native connectivity or standardized interfaces for data acquisition. This paper presents an approach to enable industrial digitalization through the implementation of a network architecture at the Operational Technology (OT) level that facilitates the collection of structured data from legacy and modern machines. The proposed solution ensures integration between production systems and supervisory platforms, supporting real-time monitoring through SCADA systems and providing relevant information for predictive maintenance strategies. The proposal is based on the implementation of a standardized and secure communication infrastructure between the shop floor and higher-level Information Technology (IT) systems, aligning with the principles of Industry 4.0. The solution has been implemented in a real industrial scenario, is fully operational and the results demonstrate significant benefits of integrating heterogeneous industrial assets into a unified data ecosystem, improving process insight, operational efficiency and supporting maintenance decision-making.
Copyright (c) 2025 Nikita Bocharov, Pedro Torres, João Matos https://creativecommons.org/licenses/by-nc-sa/4.0
Digitization of civil architecture objects during wartime using photogrammetry: A case study of Sumy State University
INTRODUCTION: In the context of the ongoing war in Ukraine, the preservation of architectural heritage has become critically important. In this regard, the documentation and preservation of architectural heritage have become crucial. OBJECTIVES: The study employs drone-based aerial imaging and terrestrial photography to generate accurate 3D models of a partially destroyed historic building. METHODS: A photogrammetric technique was employed to create an accurate 3D model of Campus K-2 of Sumy State University, destroyed by russian missiles on April 13, 2025, in Sumy, Ukraine. A comprehensive approach for involving photogrammetric reconstruction, texture processing, and building information modeling (BIM) integration was described. The research also outlines the challenges of restoring during wartime, discusses data acquisition and processing workflow, and evaluates the effectiveness of low-cost equipment and open-source software in achieving high-quality results. RESULTS: The resulting digital model captures structural deformations and facade details. The implemented approach enables documentation and monitoring of damaged structures. It also provides valuable fundamentals for the studied object’s future restoration, structural analysis, and memorialization. CONCLUSION: The research shows photogrammetry as a fast, reliable tool for documenting cultural heritage in wartime and emphasizes its social value for education and preservation. It also suggests adding protective architectural features to reconstructed heritage buildings to improve resilience in conflict zones.
sustainable reconstruction of war-damaged sites
digital documentation for historical memory
Copyright (c) 2025 Ihor Tytarenko, Ivan Pavlenko https://creativecommons.org/licenses/by-nc-sa/4.0
Study of the process of purifying potassium humate suspension from suspended particles using industrial centrifuges
Potassium humate suspensions obtained by alkaline extraction of peat using cavitation dispersion contain suspended solid particles that require effective separation to improve product quality. The separation of suspended solid particles larger than 20 μm is particularly important to ensure product purity. The main objectives of the study were to determine the possibility of removing suspended solid particles from potassium humate suspension and to evaluate the efficiency of the separation process on a laboratory and industrial scale. Laboratory tests included sedimentation under the action of Earth's gravity for two months, centrifugation in a laboratory cup centrifuge at different speeds, and analysis of solid content, particle size distribution, pH, and conditional viscosity. Industrial tests were carried out on an Ecomash SHS 311 centrifuge in both batch and continuous modes at different rotor speeds and flow rates. The purified product (fuga) was analysed by sieving through 20 and 40 μm sieves and measuring viscosity. Laboratory sedimentation showed the formation of a compact sediment layer, confirming the presence of suspended solids. Industrial tests demonstrated effective removal of suspended solids at a separation factor (Fr) ≥ 1550, with no visually detectable particles larger than 20 μm in the filtrate. Viscosity measurements correlated well with suspended solids content and were proposed as a rapid control method. Centrifugation effectively removes suspended solids from potassium humate suspensions in both laboratory and industrial conditions. The separation factor significantly affects the efficiency of purification. For operational quality control, sieving in combination with viscosity measurement is recommended. Further laboratory studies are needed to establish the dependence of viscosity on solids content and temperature to improve process control.
Copyright (c) 2025 A.O. Shkop, N.G. Ponomarova, Alona Bosiuk, O.V. Shestopalov https://creativecommons.org/licenses/by-nc-sa/4.0
Integrated Manufacturing Management Platform
INTRODUCTION: This paper presents a brief overview of the Integrated Manufacturing Management Platform (IMMP), designed to enable dynamic, real-time integration of supply chain and manufacturing management across multiple factories, workstations, and external business partners. The platform fosters interoperability and shared decision-making across strategic, tactical, and operational levels, leveraging integrated middleware comprising both hardware and software components, as well as associated technologies and tools. OBJECTIVES: The primary objective of this work is to describe the development and capabilities of the IMMP, highlighting how it supports collaborative and integrated manufacturing planning, scheduling, monitoring, and control. The platform aims to enhance joint manufacturing management by enabling information sharing and process coordination among a distributed set of stakeholders. METHODS: The IMMP was developed using Microsoft Visual Studio to facilitate dynamic updates to its information, tools, and functionalities. The underlying databases were implemented in SQL Server Management Studio and iteratively refined to accommodate evolving platform requirements. The system architecture is based on modular components deployed across participating factories and work centers, supported by smart objects and appropriate interfaces for seamless integration and communication. RESULTS: The resulting platform offers a fully integrated environment comprising multiple modules and functions provided by each collaborating entity. These modules support various tasks such as integrated manufacturing planning and scheduling, leveraging smart technologies and networked organizational structures. Data collected from the participating network of factories and facilities enabled the validation of the platform's core functionalities and guided successive improvements. CONCLUSION: The IMMP demonstrates strong potential to support real-time, collaborative manufacturing management across diverse stakeholders. Through its modular, scalable, and dynamic architecture, the platform enhances interoperability and decision-making across all management levels, laying the foundation for more responsive and integrated industrial operations.
Copyright (c) 2025 Gaspar Vieira, Miguel Ângelo Pereira, Leonilde Varela, Goran Putnik, Manuela Cruz-Cunha https://creativecommons.org/licenses/by-nc-sa/4.0
The Impact of generative artificial intelligence on students and teachers in the educational process
In the last 10–15 years, significant investments have been made in the integration of modern technologies into the educational process. These investments have been driven by the rapid development of the internet and information technologies, as well as global challenges such as the COVID-19 pandemic, which necessitated a shift toward online learning. The primary objective of these efforts is to improve the efficiencntialy of the educational process by enabling students to develop essential skills, such as problem-solving, teamwork, and analytical thinking, all supported by technology. When discussing modern technologies, special attention must be given to Artificial Intelligence (AI) and its subfield, Generative Artificial Intelligence (GAI), which are increasingly integrated into the educational environment. In this context, Generative Artificial Intelligence has the potential to significantly reshape teaching and learning by offering personalized learning experiences, dynamic instructional content, and tools that foster creative thinking. For educators, GAI offers new possibilities for interactive teaching, tracking student achievements, and analyzing academic performance. These tools, designed for both students and teachers, are easily accessible, intuitive to use, and constantly evolving. This paper presents a theoretical review of the implementation of GAI in education and examines how its application influences the work of students and teachers. Furthermore, the paper will explain the potential of GAI to enhance digital literacy, analytical thinking, and adaptive learning strategies necessary for competitiveness in the 21st-century labor market.
Copyright (c) 2025 Vladan Čolić, Enes Sukić https://creativecommons.org/licenses/by-nc-sa/4.0