Electrostatic painting is part of the metal surface treatment, coating and decoration industry and is carried out by paint application operations in paint booths equipped with automated and manual paint guns, respectively, by painting operators. Knowledge of the relationship between physiology and human psychology with painting techniques and equipment is important for the development of painting systems and installations designed to ensure the comfort and safety of operators in paint booths and to reduce the creation of nonconformities generated by the painting process. The present study reviews the theoretical and practical criteria for the application of ergonomics techniques and human factors engineering in electrostatic powder painting. The research is based on the study of specialized literature and case studies carried out at one of the most renowned electrostatic painting companies in Europe.
The purpose of this paper is to present extensions of the parallel line method from the real line to share the space Rn using p-norms with p ∈ [1,∞)
For optimal results, complex tibial plateau fractures require precise anatomical reduction and biological augmentation. Traditional approaches often separate these two objectives, leading to suboptimal results. Objective: To present a comprehensive design framework for an integrated patient-specific guide system that simultaneously addresses anatomical precision and biological enhancement through bone marrow aspirate (BMA) augmentation. Methods: A systematic design approach was developed, encompassing (1) virtual planning workflow, (2) guide architecture specifications, (3) material selection criteria, (4) BMA delivery integration, and (5) manufacturing protocols. Technical validation was performed through virtual stress analysis, dimensional accuracy testing, and simulated surgical workflow assessment. Results: The proposed system features a modular guide with: (a) fragment-specific reduction tabs achieving theoretical reduction accuracy of 0.8±0.3mm, (b) multi-level drilling arrays with 0.2° angular precision, (c) integrated BMA delivery channels enabling layered defect filling, and (d) radiolucent construction with tantalum markers for intraoperative verification. Virtual testing demonstrated mechanical stability under 150N loads with 0.35mm deformation. Simulated surgical workflows showed 25-35% reduction in procedural steps compared to conventional techniques. Conclusion: This design framework provides a comprehensive approach for developing integrated patient-specific guide systems that combine anatomical precision with biological augmentation. The proposed specifications offer a standardized methodology for institutions developing similar technologies, potentially improving outcomes in complex articular fracture management through simultaneous mechanical and biological optimization.
The increasing complexity of automotive electrical systems and the tightening of regulatory expectations have strengthened the need for robust product-integrity governance in the supply chain. This paper examines the role of the Product Safety and Conformity Representative (PSCR) in three Tier 1 suppliers producing safety-relevant electrical and electromechanical components. Based on documentation analysis, Failure Mode and Effects Analysis (FMEA) records and VDA 6.3 Line Walk audits conducted between January and December 2025, the study compares the situation before and after formal PSCR oversight was introduced in June 2025. The results indicate improved discipline in FMEA maintenance, higher on-time closure rates of audit findings and fewer repeated nonconformities. Integrated audits also show that traceability, documentation integrity and digital data management are central to both product integrity and emerging sustainability expectations. The paper discusses how the PSCR function can serve as an operational bridge between technical risk management and future Environmental, Social and Governance (ESG) requirements.
This paper presents the design and implementation of a highly specialized, dual-sided GEO Gripper for automotive body-in-white assembly operations. The study details the engineering of a custom end-effector developed to handle complex left and right-side vehicle components. Because standard handling with KUKA robots was insufficient for the required lateral insertion due to spatial constraints, a built-in sliding mechanism was developed. The proposed design features custom locators for precise part resting, as well as parallel grippers and pneumatic clamps tailored to secure specific component protrusions. To ensure process reliability, a dual-sensor system combining inductive and laser sensors was integrated to enable accurate part detection. A key feature of this design is the specialized docking system that interfaces directly with the assembly fixture. During operation, the gripper first docks securely into the station. Once docked, the integrated sliding mechanism actuates to laterally insert the component into its final position. The gripper remains in this locked configuration to hold the part with strict geometric accuracy while a secondary robot performs the final welding process. This custom solution significantly improves assembly precision and process stability in restricted workspaces.
This study explores the design and implementation of a control system integrating the antilock braking system (ABS) with proportional-integral-derivative (PID) control technology to enhance safety and performance in road vehicles. This research proposes and evaluates a fuzzy logic controller (FLC)-based ABS using a quarter-vehicle model and the Burckhardt tire–road interaction, implemented in Matlab-Simulink. The proposed system employs the proportional-integral-derivative (PID) and fuzzy logic (FLC) controller to improve braking efficiency and vehicle stability under diverse driving conditions. Simulation results showed significant enhancements in stopping performance across various road conditions. The integrated system exhibited a marked improvement in braking performance, achieving significantly shorter stopping distances across all evaluated surface conditions, including wet asphalt and icy roads, compared with scenarios without ABS controller. These results highlight the system’s ability to dynamically adapt braking forces to different road conditions, significantly improving safety and stability for road vehicles.
This paper analyzes the evolution and current trends in predictive maintenance, with a focus on the integration of artificial intelligence (AI), machine learning (ML), and Industrial Internet of Things (IIoT) architectures in modern industrial environments. The study highlights the transition from traditional corrective and preventive maintenance strategies to advanced condition-based monitoring and prediction models, enabled by the growth of data acquisition and processing capabilities. The main research directions related to anomaly detection, remaining useful life (RUL) estimation, optimization of equipment health indicators, and the integration of ML and DL algorithms into edge–cloud systems are analyzed. In addition, challenges associated with data quality, class imbalance, cybersecurity, and the scalability of IIoT architectures are discussed. The study synthesizes recent contributions from critical sectors such as the energy industry, automotive industry, and smart manufacturing, emphasizing the role of emerging technologies such as Digital Twins, explainable AI (XAI), and Deep Reinforcement Learning in the development of robust and efficient predictive maintenance systems. The analysis provides an integrated perspective on the current state of research and identifies future directions for optimizing industrial processes through advanced predictive solutions.
The integration of nanoparticles into Titanium-based metallic alloys for smart biomedical applications requires precise control over surface morphology and oxide‑layer integrity. Anodized Titanium discs are particularly vulnerable to mechanical abrasion, contamination, and handling‑related defects, which can compromise the reproducibility of surface‑dependent experiments. To address this challenge, a custom transport and storage system was developed using a fully iterative engineering workflow. The device was designed in CATIA V5 and fabricated through fused‑filament 3D printing, with eleven successive design iterations produced to optimize mechanical stability, printability, and sample protection. Each iteration resolved specific limitations related to disc fixation, dimensional tolerances, vibration resistance, and manufacturability. The final design provides a robust, low‑cost, and reproducible solution that prevents surface damage during laboratory handling and transport. This work demonstrates how iterative additive manufacturing can support advanced materials research by ensuring the integrity of anodized Titanium-based samples used in nanoparticle integration studies.
A critical problem of the industrial valves is the quality of the flat surface of the flanges which act as a sealing surface. Over time, exposure to aggressive fluids, forming of corrosion and erosion degrade the sealing surface. This phenomenon leads to the loss of flatness, triggering leaks and impairing valves. Industrial valves are dismantled during maintenance, and the sealing surfaces are restored by sanding. The operation is complex because of many factors such as location of the valves, valve geometry, safety of the workers and transport of the valves. The objective of this research is to design a new mobile device that ensures precise machining of sealing surfaces. The new device developed in this research is supported by a drilling machine, a worm gear mechanism and a rotating disc that assures precise machining of the sealing surface. It facilitates the improvement of the flanges of industrial valves which are altered by corrosion, loss of flatness, roughness and mechanical wear during service.
This article examines the Romanian metallurgical sector in the EU context, focusing on productivity, digitalization, innovation, and environmental sustainability. The results reveal a declining share of value added, persistently low productivity, weak business digitalization, limited R&D investment, and moderate GHG emissions per capita compared with the EU27, alongside heterogeneous sub-sector performance. The findings indicate that higher productivity is associated with greater digitalization, but does not necessarily lead to lower emissions. Therefore, strengthening enterprise digitalization and R&D capacity in Romania could enhance labour productivity while supporting more efficient, lower-emission production in the basic metals sector.
Measuring the complexity of a biomedical signal as Heart rate Variability (HRV), might be used as a diagnostic tool in medical investigations. This paper presents a study of HRV signal analysis using entropy metrics to estimate the HRV signals’ complexity. This study uses permutation and transfer entropies to characterize the signal. This work uses signals from specific databases, both normal sinus rhythm and diseased signals. The main purpose of this study is to find a relation between signal complexity and interpretable symbolic dynamics of Heart Rate Variability to help medical investigation.
The objective of this study is to research the defining aspects of Industry 5.0, how to reach it through the transition from Industry 4.0, the critical and central pillars of Industry 5.0 and the two components of the twin transition, digital and green. The methodology included research of the industry 5.0 as term, the critical pillars and central pillars that underpin all Industry 5.0 advancements, twin transition and challenges of the twin transitions. The results of the study address aspects related to the transition from Industry 4.0 to Industry 5.0, what Industry 5.0 means, its responsible practices. Next, the central pillars of Industry 5.0 are analyzed: human-centered industry, sustainable industry, resilient industry, how Industry 5.0 integrates in Industry 4.0 technologies, the specific concepts, and technologies through which this is achieved namely personalization, collaboration, data management, experiential tools, digital twins. Then tools for personalization are highlighted. It presents the twin transitions in industry which involves the merging of two transitions faced by industrial enterprises, namely the digital transition and the green transition. The benefits and challenges of twin transition are also analyzed. The conclusion reveals the emergence of Industry 5.0 as a complementary concept to Industry 4.0, a model that encourages industrial development through technological innovation and economic growth. It brings into focus commitments to environmentally responsible practices.
This paper studies the possibility of applying artificial intelligence (AI) in engineering calculations and how this particularly powerful and at the same time dangerous tool can support the design of mechanical elements. The advantages and disadvantages of using (AI) in the dimensioning of machine parts, in analytical calculations carried out by an engineer are presented. The usefulness in the situation of repetitive calculations, structural optimization and support in analysis can be observed provided that the interpretation of various parameters is constantly verified. The case study addressed, regarding the dimensioning of a parallel wedge, highlights the way in which (AI) can facilitate or not the selection of parameters and strength checks. The conclusions highlight the fact that (AI) represents a useful support for the engineer, provided that it is used critically and complementary to basic technical knowledge.
RF energy harvesting has emerged as a promising solution for powering low-power IoT nodes, wireless sensors, and portable electronics, offering an alternative to conventional batteries. This review provides a concise overview of rectenna-based RF harvesting systems, outlining their operating principles and summarizing recent advancements in wideband, multi-band, flexible, and wearable designs. Developments in rectenna arrays and metasurface-based architectures are also discussed, with emphasis on strategies to enhance harvested power. Key challenges and future research directions are highlighted, focusing on efficiency improvements and scalable integration.
This paper presents an advanced quality design process for a gear wheel used in an automotive speed reducer. The study is based on real production conditions and integrates methods such as Advanced Product Quality Planning, Failure Mode and Effects Analysis, and statistical process control. Through a detailed technological process plan, defect analysis, and continuous improvement proposals, the objective is to reduce the rate of nonconforming parts and optimize the production cycle. By implementing control plans and lean strategies, the research contributes to the increase of product quality and operational efficiency.
Most knowledge representation models in tutoring systems treat knowledge as a static resource. In real world scenarios, knowledge evolves over time as circumstances change. Consequently, the information embedded in a tutoring model may become outdated or require revision, especially in rapidly changing domains such as software development. The Evolving Knowledge Space Graph model addresses this issue by introducing abstract time to represent knowledge dynamics. This paper analyzes the temporal dependencies within the Evolving Knowledge Space Graph model, introducing “before” and “after” relations, and extending the model to capture knowledge aging. Additionally, it proposes metrics for assessing knowledge differences and complexity. Finally, the feasibility of the approach is demonstrated through a case study that transforms the publicly available OpenJDK JMC system’s knowledge into an Evolving Knowledge Space Graph.
Drug–drug interactions (DDIs) represent a persistent and clinically significant challenge, contributing to preventable adverse events and suboptimal therapeutic outcomes, especially among patients exposed to polypharmacy. While traditional Clinical Decision Support Systems (CDSS) provide rule-based DDI alerts, these mechanisms often suffer from low specificity, high override rates, and limited adaptability to patient context. In recent years, substantial progress in artificial intelligence (AI) has enabled more sophisticated prediction of DDIs based on molecular representations, deep learning architectures, and transformer driven extraction of textual evidence. However, most published AI models remain detached from operational CDSS workflows and lack standardized mechanisms for integration into electronic health record (EHR) environments. This article proposes an interoperable, multiagent CDSS architecture that integrates the author’s previously validated AI-based DDI prediction models into a clinically deployable workflow. The system incorporates a Graph– Vector Hybrid GCN–FFNN model for synergistic and antagonistic DDI prediction and a BiomedBERT–LoRA classifier for extracting polarity and mechanistic cues from biomedical literature. These previously validated models are the main parts of a network of autonomous agents that work together to do things like data preprocessing, inference routing, contextual adjustment, alert generation, explanation synthesis, and governance. Health Level 7 – Fast Healthcare Interoperability Resources (HL7 FHIR) is the interoperability backbone for the architecture. This means that it works with EHR systems in real time and uses DetectedIssue resources to show alerts in a standard way. This research shows that AI is beneficial for medication safety not due to its capacity for independent accurate predictions, but because it can be incorporated into a system that is transparent, scalable, clinically pertinent, and sustainable over time.
"Compressed air is one of the most expensive energy resources used in production environments, and its inefficient management can lead to significant energy losses and, implicitly, to increased operating expenses. The objective of this research is to analyze consumption and monitor the production of compressed air using a digital smart metering infrastructure. The research methodology consists of monitoring and evaluating the performance of three industrial compressors by real-time measuring the amount of compressed air produced over the course of a year. To obtain relevant results, raw data from digital flowmeter devices, which provide an accuracy of ±1.5% in measurements, are used. The information collected allows the determination of the operating efficiency of each compressor, the identification of any air losses, and the assessment of the degree of energy efficiency. The result of the research consists of practical solutions for optimizing the compressor system, which contribute to reducing energy consumption in industrial facilities, reducing costs, and increasing the sustainability of industrial processes. Through the proposed method, compressed air production is more rigorously controlled, and data collection becomes more reliable and accurate, thus enabling early detection of any deviations."
This article deals with the use of resin for the restoration of timber structural elements subjected to moment loads. Resin type 3PHV60 was used to repair the damaged wooden structural members, and its effect on stiffness was studied. The study deals with two cases regarding the position of the damage.
A key role in reinventing industries is innovation. But innovation needs graduates from upper-secondary and higher education in science, technology, engineering, and mathematics. An international consortium of universities and industrial partners from seven countries has developed an international project entitled “Shaping the Engineers of Tomorrow” (FuturENG). Its main objective is to develop innovative, multifaceted training for engineering students and educators designed in collaboration with the industrial ecosystem. The methodology consists of the creation of materials based on innovative learning and teaching practicesthat are focused on supporting digital and green capabilities of the higher education sectors. The results are focused on providing knowledge on important topics related to digital transformation: sustainable digitization, twin transition, deep tech, I4.0, directly addressing the E+ horizontal priority for boosting digital resilience. A set of Massive Open Online Courses consisting of 5 modules has been developed. The knowledge-triangle methodology (research-academia-business), being the first of its kind, stresses the necessity of future cooperation between academic, research, and business regarding the development of high-impact, relevant to the labor market needs engineering curriculums.
