Search Results (2,740)

Achieving optimal lubrication during machining processes, particularly turning of stainless steel, remains a significant challenge due to dynamic variations in cutting conditions that affect tool life, surface quality, and environmental impact. Conventional Minimum Quantity Lubrication (MQL) systems provide fixed flow rates and often fail to adapt to changing process parameters, limiting their effectiveness under fluctuating thermal and mechanical loads. To address these limitations, this study proposes an ambient-aware adaptive Auto-Tuned MQL (ATM) system that intelligently controls both nanofluid concentration and lubricant flow rate in real time. The system employs embedded sensors to monitor cutting zone temperature, surface roughness, and ambient conditions, linked through a feedback-driven control algorithm designed to optimize lubrication delivery dynamically. A Taguchi L9 design was used for experimental validation on AISI 304 stainless steel turning, investigating feed rate, cutting speed, and nanofluid concentration. Results demonstrate that the ATM system substantially improves machining outcomes, reducing surface roughness by more than 50% and cutting force by approximately 20% compared to conventional MQL. Regression models achieved high predictive accuracy, with R-squared values exceeding 99%, and surface analyses confirmed reduced adhesion and wear under adaptive lubrication. The proposed system offers a robust approach to enhancing machining performance and sustainability through intelligent, real-time lubrication control. Full article

Background/Objectives: Tooth wear (TW) is a prevalent multifactorial condition resulting from chemical erosion and mechanical forces, yet the contributions of risk-group status and salivary factors remain insufficiently characterized. This study aimed to investigate the relationship between salivary characteristics and the severity of chemical and mechanical TW in at-risk groups, including gastroesophageal reflux disease (GERD), sleep bruxism (SB), eating disorders (EDs) and gambling disorder (GD). Methods: This cross-sectional observational study enrolled adults categorized into the four mutually exclusive at-risk groups and an age and sex-matched healthy control group. Demographic information, medical history, oral hygiene, diet, stress, and parafunctional habits were obtained through questionnaires. TW was assessed by a single examiner using TWES 2.0. Maximum bilateral force and salivary pH, flow and buffer capacity was measured. Correlations, multivariate linear regression, and mediation models were used to explore the relationships between the different variables and TW. Results: In total, 147 participants, divided into five groups (34 with GERD, 28 with SB 20 with GD, 20 with ED, and 45 controls) were included. The lowest resting salivary pH was observed in the GERD and ED groups (GERD: 6.63 ± 0.61; ED: 6.62 ± 0.52). The GERD group also exhibited the highest chemical (1.51 ± 0.58) and mechanical (1.08 ± 0.58) TW. Chemical and mechanical wear were strongly correlated, and mechanical wear increased with age. Risk-group status and salivary pH explained 47% of chemical wear, while age and bite force explained 54% of mechanical wear. Conclusions: Chemical TW was strongly linked to risk-group status—particularly GERD/ED—and low salivary pH, while mechanical TW related to age and bite force. Further longitudinal studies with larger samples, employing standardized methodologies and criteria are needed. Full article

Francis turbines operating in sediment-laden flows experience efficiency loss and reduced service life due to abrasive wear. To enhance wear resistance, this study optimized the turbine at Mupo Hydropower Station in Sichuan Province. Using the Plackett–Burman design, three runner parameters were identified as most influential: blade number, inlet setting angle, and outlet setting angle. A central composite design based on response surface methodology was then applied to these factors. Multiple regression models linking the parameters to turbine head, efficiency, and wear rate were established, revealing a trade-off between hydraulic performance and wear resistance. Multi-objective optimization, a method that simultaneously addresses and balances multiple competing goals, was performed to minimize wear rate while maintaining the original head. The optimal parameter combination was obtained as follows: blade number Z₃ = 17, inlet setting angle ${\alpha }_1$ = 65°, and outlet setting angle ${\alpha }_2$ = 22°. Numerical results demonstrate a 32.3% reduction in runner wear under these parameters, with the head requirement satisfied, confirming a significant improvement in overall turbine performance. Full article

This review aims to synthesize current knowledge on the environmental contaminants N-(1,3-dimethylbutyl)-N′-phenyl-p-phenylenediamine (6PPD) and its quinone derivative (6PPDQ) derived from tire wear particles (TWPs), focusing on their environmental distribution, transformation, human exposure pathways, toxicological effects, and health risks to ecological and human health. A comprehensive literature review was conducted, compiling and analyzing data from environmental monitoring studies, toxicological assessments on aquatic and mammalian models, and emerging human biomonitoring research. Key findings on concentrations, toxicological endpoints (e.g., LC50, oxidative stress, genotoxicity), and exposure pathways were evaluated. 6PPD and its transformation product 6PPDQ are ubiquitous environmental pollutants found in air, water, soil, sediment, and dust. 6PPDQ is notably highly toxic to aquatic organisms, with an acute LC50 of 790 ng/L for coho salmon. Human exposure to these compounds occurs through inhalation, ingestion, and dermal contact, and their presence has been confirmed in human matrices including blood, urine, and cerebrospinal fluid. Toxicological studies, primarily on model organisms, indicate that 6PPD and 6PPDQ can induce oxidative stress, cause DNA damage, and disrupt metabolic and neurological functions. Adverse outcomes such as intestinal toxicity, reproductive impairment, neurobehavioral changes, and potential carcinogenicity have been observed. However, direct evidence of their health impacts on humans remains limited. 6PPD and 6PPDQ pose significant and widespread ecological risks, with 6PPDQ representing a particularly potent aquatic toxicant. While human exposure is confirmed, the full scope of human health implications is not yet well understood. The review highlights the need for longitudinal environmental tracking, mechanistic studies, and refined exposure models to inform regulatory actions and mitigate risks. Addressing these challenges is essential to mitigate the ecological and health burdens posed by 6PPD and 6PPDQ. This study underscores the global societal importance of addressing 6PPD-related pollution—a pervasive and transboundary environmental challenge stemming from universal tire wear. Full article

Due to its unique benefits over conventional subtractive manufacturing, additive manufacturing methods continue to attract interest in both academia and industry. One such method is called Cold Spray Additive Manufacturing (CSAM), a solid-state coating deposition technology to manufacture repair metallic components using a gas jet and powder particles. CSAM offers low heat input, stable phases, suitability for heat-sensitive substrates, and high deposition rates. However, persistent challenges include porosity control, geometric accuracy near edges and concavities, anisotropy, and cost sensitivities linked to gas selection and nozzle wear. Interdisciplinary research across manufacturing science, materials characterisation, robotics, control, artificial intelligence (AI), and machine learning (ML) is deployed to overcome these issues. ML supports quality prediction, inverse parameter design, in situ monitoring, and surrogate models that couple process physics with data. To demonstrate the impact of AI and ML on CSAM, this study presents a systematic literature review to identify, evaluate, and analyse published studies in this domain. The most relevant studies in the literature are analysed using keyword co-occurrence and clustering. Four themes were identified: design for CSAM, material analytics, real-time monitoring and defect analytics, and deposition and AI-enabled optimisation. Based on this synthesis, core challenges are identified as small and varied datasets, transfer and identifiability limits, and fragmented sensing. Main opportunities are outlined as physics-based surrogates, active learning, uncertainty-aware inversion, and cloud-edge control for reliable and adaptable ML use in CSAM. By systematically mapping the current landscape, this work provides a critical roadmap for researchers to target the most significant challenges and opportunities in applying AI/ML to industrialise CSAM. Full article

Due to the random factors that influence grinding stability, hardness distribution appears in inhomogeneity at different locations on the hardened layer in grind-hardening technology. It may affect the wear resistance of parts. Therefore, in order to explore the influence mechanism of hardness homogeneity on the wear resistance comprehensively, grind-hardening and friction experiments on AISI 1045 steel are carried out. Then, the causes of inhomogeneous hardness distribution are analyzed, and the influence of hardness homogeneity on wear resistance is also discussed. Combining the Archard wear model, the wear process of the hardened layer is simulated for analyzing the effect of contact stress distribution and action range on material loss in the worn area and finally realizing the prediction of the wear depth. The results show that the difference in microstructure distribution caused by the nonlinear variation in grinding force is the fundamental reason for the hardness inhomogeneity of the hardened layer. The hardness homogeneity results in the wear resistance of the cut-out end being superior to that of cut-in end. Additionally, the error between the predictive and the experimental value of the wear depth with different parameters is between 3.6% and 11.3%, thereby verifying the effectiveness of the theoretical research. Full article

In recent years, the rapid development of underground engineering projects has driven a significant increase in the variety and quantity of excavation equipment. The wear of excavation tools significantly increases construction costs and reduces construction efficiency. The wear rate of excavation tools is closely related to the abrasiveness of the rock. The Cerchar abrasivity index (CAI) is the most widely used index for estimating rock abrasiveness. The primary objective of this paper is to develop a novel prediction model for CAI, which is established based on the mechanical properties and petrographic parameters of rocks. These parameters include uniaxial compressive strength, Brazilian splitting strength, quartz content, equivalent quartz content, average quartz size, brittleness indices, rock abrasive index, and Schimazek’s F-abrasiveness. Correlation analysis was used to conduct a preliminary analysis between CAI and single-influence parameters. The results indicated that a single factor is not suitable for directly predicting CAI. In addition, multiple linear regression (MLR) and a non-linear algorithm, gene expression programming (GEP), were used to establish new prediction models for CAI. A statistical comparison was conducted between the prediction accuracy of the GEP-based model and the MLR-based model. In comparison to the MLR-based model, the GEP-based model demonstrates higher accuracy in predicting CAI. Full article

Adhesive wear has been identified as a significant cause of material loss, representing a substantial challenge across diverse industrial sectors. In order to address this issue, it is imperative to conduct studies with the aim of mitigating this degradation. The present study focuses on achieving a high-quality product with minimal mass loss during adhesive wear by utilizing gas nitriding treatment to optimize the wear parameters of AISI 4140 steel. The present study employed the Taguchi methodology and response surface methodology (RSM) in order to design the experiments. A comprehensive investigation was conducted into the key wear parameters, encompassing sliding speed (V), normal load (FN), and the microhardness of nitrided parts (HV). Furthermore, an artificial neural network (ANN) prediction model was developed to forecast the wear performance of 4140 Steel. The ANN model demonstrated an accuracy of approximately 99% when compared to the experimental data. In order to enhance the precision of wear estimation, prediction optimization was conducted using Bayesian and genetic algorithms. The findings demonstrated that the predicted R² values exhibited a reasonable alignment with the adjusted R² values, with a discrepancy of less than 0.2. The analysis demonstrated that the normal load is the most significant factor influencing wear, followed by hardness. In contrast, sliding speed was found to have the least significant impact. Full article

Turn-milling is a relatively new process which combines turning and milling operations, offering a number of advantages such as chip breaking and interrupted cutting, which improves tool life. In addition to providing the capability of producing eccentric forms or shapes, it increases productivity for difficult-to-machine material at lower cost. This study investigates the influence of cutting speed and feed on surface roughness and tool wear in conventional turning and turn-milling of H13 tool steel. The tests were conducted for longitudinal and face machining strategies. It was found that the range of surface roughness in turning is lower than in turn-milling. In longitudinal turning, face-turning, and face turn-milling operations, surface roughness is elevated in the higher feeds. However, the surface roughness in longitudinal turn-milling operations can be reduced by increasing the feed. Although the simultaneous rotation of the tool and workpiece in turn-milling could negatively affect the surface quality, this operation provides the advantage of an interrupted cutting mechanism that produces discontinuous chips. Also, the wear of the endmill in longitudinal and face turn-milling operations is lower than the wear of the inserts used in conventional longitudinal and face turning. Using Response Surface Methodology (RSM), mathematical models were developed for surface roughness and tool wear in each operation. The RSM models developed in this study achieved coefficients of determination (R²) above 90%, with prediction errors below 7% for surface roughness and below 3% for tool wear. The analysis of variance (ANOVA) revealed that the feed and cutting speed are the most influential parameters on the surface roughness and tool wear, respectively, with p-value < 0.05. The experimental results demonstrated that tool wear in turn-milling was reduced by up to 50% compared to conventional turning. Full article

Lubricants play a crucial role in improving the reliability of internal combustion engines. The deterioration of engine oil is influenced not only by mileage and usage time but also by subjective factors. Currently, engine oil is replaced in accordance with the manufacturer’s recommendations. At the time of a scheduled oil change, two situations may arise: the degree of lubricant wear may exceed normal levels, in which case the technical systems may also be damaged, with serious consequences for the environment and, implicitly, for human health; or the degree of wear may be low, consistent with normal engine operation, in which case prolonging oil use is recommended, thereby reducing consumption. In this paper, the authors propose a method for diagnosing the engine through periodic analysis of the physico-chemical properties of used engine oil, based on which appropriate vehicle maintenance strategies are issued. Also, recommendations are made for prolonged use of the oil on the condition of its periodic evaluation. Thus, for samples taken from 43 trucks the following physico-chemical properties were analyzed: kinematic viscosity, density, flash point, fuel content, water content, and metal content and their values, for all samples, were within the recommended limits. However, for some samples, more pronounced variations in the values of some properties were found and suitable preventive maintenance recommendations were issued. Full article

This study investigates the structure–property–performance (SPP) relationships of two thermoplastic polyurethanes (TPUs), FILAFLEX FOAMY 70A and SMARTFIL^® FLEX 98A, manufactured by fused filament fabrication (FFF). Disc specimens were produced with varying gyroid infill densities (10–100%) and Archimedean surface textures, and their tribological and surface characteristics were analyzed through Ball-on-Disc tests, profilometry, and optical microscopy. SMARTFIL^® FLEX 98A exhibited a sharp reduction in the coefficient of friction (μ) with increasing infill, from 1.174 at 10% to 0.371 at 100%, linked to improved structural stability at higher densities. In contrast, FILAFLEX FOAMY 70A maintained a stable but generally higher coefficient of friction (0.585–0.729) across densities, reflecting its foamed microstructure and bulk yielding behavior. Surface analysis revealed significantly higher roughness in SMARTFIL^® FLEX 98A, while FILAFLEX FOAMY 70A showed consistent roughness across infill levels. Both TPUs resisted inducing abrasive wear on the steel counterpart, but their stress-accommodation mechanisms diverged. These findings highlight distinct application profiles: SMARTFIL^® FLEX 98A for energy-absorbing, deformable components, and FILAFLEX FOAMY 70A for applications requiring stable surface finish and low adhesive wear. The results advance the design of functionally graded TPU materials through the controlled tuning of infill and surface features. Full article

Maintaining a high-quality urban environment remains a critical yet challenging issue in modern cities, particularly in densely built and historically significant central areas. In response, the European Green Deal initiative aims to promote sustainable urban development. This study presents a multi-criteria assessment methodology for evaluating urban environments, with a focus on prioritizing thermal renovations of buildings to achieve substantial environmental improvements. The research adopts a centrifugal strategy, targeting buildings with the poorest energy performance for phased renovation efforts. Using the model city of Gubin, Poland, as a case study, the assessment proceeds through five stages: evaluating technical wear (Stages I–II), estimating replacement values and renovation costs (Stages III–IV), and finally, quantifying environmental benefits from energy efficiency upgrades (Stage V). Findings reveal that buildings in the lowest energy class (Class G) require investments of 111–193% of their replacement value but can deliver CO₂ emissions reduced to 1/6.2 of the original level (an approximate 84% reduction). The primary contribution of this paper is the development and application of a novel multi-criteria assessment methodology for evaluating urban environments, specifically designed to prioritize thermal renovations in central urban areas to achieve significant environmental and economic benefits. The study provides valuable economic and environmental indicators that can guide the formulation of pro-environmental urban policies and support strategic decision-making in cities with dense populations and aging infrastructure. Full article

Nickel-based alloys, including Inconel 718 and alloy 625, are indispensable in industries such as aerospace, marine, and nuclear energy due to their exceptional mechanical strength, high-temperature performance, and corrosion resistance. However, these very properties pose severe machining challenges, such as accelerated tool wear, poor surface finish, and high cutting forces. Although several studies have investigated coatings, lubrication strategies, and process optimization, a comprehensive and up-to-date integration of these advancements is still lacking. To address this gap, a systematic review was conducted using Web of Science and Scopus databases. The inclusion criteria focused on peer-reviewed journal and conference articles published in the last eleven years (2014–2025), written in English, and directly addressing machining of nickel-based alloys, with particular emphasis on tool coatings, lubrication/cooling technologies, and machinability optimization. Exclusion criteria included duplicate records, non-English documents, papers lacking experimental or modeling results, and studies unrelated to tool life or coating performance. Following this screening process, 101 high-quality articles were selected for detailed analysis. The novelty of this work lies in synthesizing comparative insights across TiAlN, TiSiN, and CrAlSiN coatings, alongside advanced lubrication methods such as HPC, MQL, nano-MQL, and cryogenic cooling. Results highlight that CrAlSiN coatings retain hardness up to 36 ± 2 GPa after exposure to 700 °C and extend tool life by 4.2× compared to TiAlN, while optimized cooling strategies reduce flank wear by over 30% and improve tool longevity by up to 133%. The integration of coating performance, thermal stability, and lubrication effects into a unified framework provides actionable guidelines for machining optimization. The study concludes by proposing future research directions, including hybrid coatings, real-time process monitoring, and sustainable lubrication technologies, to bridge the remaining gaps in machinability and promote industrial adoption. This integrative approach establishes a robust foundation for advancing machining strategies of nickel-based superalloys, ensuring improved productivity, reduced costs, and enhanced component reliability. Full article

Tool wear detection is very important in CNC machine tool cutting. Once the tool is excessively worn, it is not only easy to cause the workpiece to be scrapped, but even to damage the machine. Therefore, common external turning tools of CNC machines are studied. The effect of tool nose wear on machining accuracy was analyzed by a building mathematical model. According to different wear conditions, a linear detection method based on edge images and input features was proposed to detect the main and secondary cutting edges, which helped determine the theoretical center of the tool nose and build a morphological visual model. For different error cases, the axial and radial error compensation strategies were proposed, respectively. By comparing the experimental data of four kinds of workpieces before and after compensation machining, the average errors of them were reduced separately, and the maximum value reached 79.2%, which verified the effectiveness of the compensation strategy. The intelligent compensation strategies will significantly improve the micro-machining accuracy and efficiency of the external turning tools in CNC machines. Full article

In the previous research that aimed to enhance the toughness and tribological properties of silicon nitride ceramics, a lignin precursor was added to the ceramic matrix, which achieved conversion through pyrolysis and sintering, resulting in a silicon nitride-based composite ceramic containing nitrogen-doped graphene quantum dots (N-GQDs). This composite material demonstrated excellent comprehensive mechanical properties and friction-wear performance. Based on the existing experimental results, the first-principles plane wave mode conservation pseudopotential method of density functional theory was adopted in this study to build a microscopic heterostructure model of Si₃N₄-based composite ceramics containing N-GQDs. Meanwhile, the surface energy of Si₃N₄ and the system energy of the N-GQDs@Si₃N₄ heterostructure were calculated. The calculation results showed that when the distance between N-GQDs and Si₃N₄ in the heterostructure was 2.3 Å, the structural energy was the smallest and the structure was the steadiest. This is consistent with the previous experimental results and further validates the coating mechanism of N-GQDs covering the Si₃N₄ column-shaped crystals. Simultaneously, based on the results of the previous experiments, the stress of the heterostructure composed of Si₃N₄ particles coated with different numbers of layers of nitrogen quantum dots was calculated to predict the optimal lignin doping amount. It was found that when the doping amount was between 1% and 2%, the best microstructure and mechanical properties were obtained. This paper provides a new method for studying the graphene quantum dot coating structure. Full article