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Article Citation - WoS: 7Citation - Scopus: 10Combined Use of Ultrasonic-Assisted Drilling and Minimum Quantity Lubrication for Drilling of Niti Shape Memory Alloy(Taylor & Francis inc, 2023) Namlu, Ramazan Hakki; Lotfi, Bahram; Kilic, S. Engin; Yilmaz, Okan Deniz; Akar, SametThe drilling of shape-memory alloys based on nickel-titanium (Nitinol) is challenging due to their unique properties, such as high strength, high hardness and strong work hardening, which results in excessive tool wear and damage to the material. In this study, an attempt has been made to characterize the drillability of Nitinol by investigating the process/cooling interaction. Four different combinations of process/cooling have been studied as conventional drilling with flood cooling (CD-Wet) and with minimum quantity lubrication (CD-MQL), ultrasonic-assisted drilling with flood cooling (UAD-Wet) and with MQL (UAD-MQL). The drill bit wear, drilling forces, chip morphology and drilled hole quality are used as the performance measures. The results show that UAD conditions result in lower feed forces than CD conditions, with a 31.2% reduction in wet and a 15.3% reduction in MQL on average. The lowest feed forces are observed in UAD-Wet conditions due to better coolant penetration in the cutting zone. The UAD-Wet yielded the lowest tool wear, while CD-MQL exhibited the most severe. UAD demonstrated a & SIM;50% lower tool wear in the wet condition than CD and a 38.7% in the MQL condition. UAD is shown to outperform the CD process in terms of drilled-hole accuracy.Article Citation - Scopus: 1Critical Connections: Network Analysis of Human Errors in Aviation Accidents(Taylor & Francis inc, 2025) Yilmaz, Ayse AsliObjectiveThis study aims to explores the systemic role of human factors in aviation safety by integrating the Human Factors Analysis and Classification System (HFACS) with network analysis.BackgroundHuman factors contribute to over 70% of aviation accidents, emphasizing their importance in safety research. This study uses the National Transportation Safety Board (NTSB), 2024 accident database to uncover systemic vulnerabilities through network analysis.MethodologyA bipartite network of 150,000 nodes and 250,000 edges was constructed using Python's NetworkX and visualized in Gephi. Centrality metrics identified systemic vulnerabilities, analyzing pilot error, crew error, and maintenance issues across general aviation, commercial jets, and rotary-wing aircraft.ResultsPilot error dominated general aviation accidents (70%), linked to single-pilot demands. Crew error was most prevalent in commercial jets (50%), highlighting multi-crew coordination challenges. Maintenance oversights in rotary-wing aircraft (45%) showed the highest betweenness centrality, underscoring their systemic impact.ConclusionTargeted safety measures include advanced training for general aviation pilots, optimized Crew Resource Management (CRM) for jet crews, and stricter maintenance protocols for helicopters. Integrating HFACS with network analysis provides a robust framework for mitigating systemic vulnerabilities and enhancing aviation safety.Article Citation - WoS: 2Citation - Scopus: 2Large Deflection Analysis of Functionally Graded Reinforced Sandwich Beams With Auxetic Core Using Physics-Informed Neural Network(Taylor & Francis inc, 2025) Nopour, Reza; Fallah, Ali; Aghdam, Mohammad MohammadiThis paper aims to investigate the large deflection behavior of a sandwich beam reinforced with functionally graded (FG) graphene platelets (GPL) together with an auxetic core, rested on a nonlinear elastic foundation. The nonlinear governing equations of the problem are derived using Hamilton's principle based on the Euler-Bernoulli beam theory for large deflections. Five different distributions are considered to describe the dispersion of GPL in the top and bottom faces of the sandwich beam. The Physics-Informed Neural Network (PINN) method is employed to model the nonlinear deflection of the beam under various boundary conditions. This study highlights the effectiveness of PINN in handling the complexities of nonlinear structural analyses. The findings underscore the impact of the core auxeticity, GPL amount and distribution, and elastic foundation coefficient on the nonlinear deflection of the sandwich beam under different loading scenarios. For instance, using Type I configuration can reduce the deflection of the beam by nearly half compared to using Type IV. Furthermore, a nonlinear foundation with a unit coefficient results in a 48% reduction in deflection compared to the scenario without an elastic foundation.Article Citation - WoS: 24Citation - Scopus: 25Machining Performance and Sustainability Analysis of Al2o3< Hybrid Nanofluid Mql Application for Milling of Ti-6al(Taylor & Francis inc, 2024) Lotfi, Bahram; Namlu, Ramazan Hakki; Kilic, S. EnginMachining of Ti-6Al-4V presents challenges due to its low thermal conductivity, and conventional cutting fluids (CCF) are inadequate in providing a productive and sustainable solution. This study aims to achieve more sustainable and productive machining of Ti-6Al-4V by utilizing Al2O3 and CuO-added Nanofluid Minimum Quantity Lubrication (NMQL) individually and in hybrid form with different concentrations. A comparison is made with pure-MQL, CCF and dry conditions. The study consists of three stages. In the first stage, the physical properties of the coolants, like contact angle and surface tension, are investigated. The second stage involves slot milling operations, and various outputs including cutting forces, surface roughness, surface topography, surface finish, and subsurface microhardness are analyzed. In the last stage, a sustainability analysis is conducted based on the Pugh Matrix Approach. The results indicate that Al2O3-NMQL exhibits lower contact angles and surface tensions compared to other conditions. Furthermore, HNMQL applications result in lower cutting forces (up to 46.5%), surface roughness (up to 61.2%), and microhardness (up to 6.6%), while yielding better surface finish and topography compared to CCF. The sustainability analysis demonstrates that HNMQL application is the most suitable option for achieving sustainable machining of Ti-6Al-4V.Article Reevaluation of Plate-Fin Heatsink Natural Convection Correlations for Sideways and Three-Dimensional Inclinations(Taylor & Francis inc, 2025) Mehrtash, MehdiThe common orientations of the plate-fin heat sink for natural convection cooling of electronics are vertical and upward-facing horizontal. However, depending on various use scenarios, the heat sink may be inclined, intentionally or otherwise. In our previous papers concerning this subject, the author proposed a set of correlations for plate-fin heat sinks covering all inclination angles backward and forward (pitch rotation) from the vertical position of the heat sink. The set was based on a series of computational simulations with a validated model. At the time, tilting the heat sink sideways (roll rotation) was not considered. In the present study, though, the sideways inclination of the plate-fin heat sinks is simulated using our previous model only by adjusting the direction of the gravitational acceleration vector, thus requiring no additional validation. It is determined that the previously proposed correlation is valid up to 80 degrees sideways inclinations of the heat sink. Interesting flow structures are observed when the heat sink is tilted 90 degrees sideways. Furthermore, it is demonstrated that the correlation surprisingly remains valid if the heat sink is simultaneously rotated in both axes (pitch and roll).Article Citation - WoS: 9Citation - Scopus: 9A Hybrid Deep Learning Methodology for Wind Power Forecasting Based on Attention(Taylor & Francis inc, 2024) Akbal, Yildirim; Unlu, Kamil DemirberkWind energy, as a sustainable energy source, poses challenges in terms of storage. Therefore, careful planning is crucial to utilize it efficiently. Deep learning algorithms are gaining popularity for analyzing complex time series data. However, as the "no free lunch" theorem suggests, the trade-off is: they need a lot of data to achieve the benefits. This even brings up a severe challenge for time series analysis, as the availability of historical data is often limited. This study aims to address this issue by proposing a novel shallow deep learning approach for wind power forecasting. The proposed model utilizes a fusion of transformers, convolutional and recurrent neural networks to efficiently handle several time series simultaneously. The empirical evidence demonstrates that the suggested innovative method exhibits exceptional forecasting performance, as indicated by a coefficient of determination (R2) of 0.99. When the forecasting horizon reaches 48, the model's performance declines significantly. However, when dealing with long ranges, utilizing the mean as a metric rather than individual point estimates would yield superior results. Even when forecasting up to 96 hrs in advance, obtaining an R2 value of 0.50 is considered a noteworthy accomplishment in the context of average forecasting.Article Citation - WoS: 39Citation - Scopus: 45Acceptance of Educational Use of Ai Chatbots in the Context of Self-Directed Learning With Technology and Ict Self-Efficacy of Undergraduate Students(Taylor & Francis inc, 2024) Esiyok, Elif; Gokcearslan, Sahin; Kucukergin, Kemal GurkanFor long now, the use of Artificial Intelligence (AI) chatbots in higher education to support and engage learners in classroom learning activities has been attracting the attention of researchers. The acceptance of this technology for learning purposes is indicative of learners' intentions and actual use in the future. Hence, this study aims to test the educational use of AI chatbots in the context of self-directed learning with technology (SDLT) along with information and communication technology (ICT) self-efficacy, using the extended Technology Acceptance Model (TAM). The study involved 414 undergraduate students, and the research model was tested by utilizing the Partial Least Square Structural Equation Model (PLS-SEM). The results indicate that ICT self-efficacy affects only the perceived ease of use (PEU), whereas PEU and perceived usefulness have a positive effect on the intention to use AI chatbots. Moreover, SDLT is shown to affect both the intention and the actual use of AI chatbots. As such, it is suggested - among other notes - that universities update their curricula and activities to support SDLT, and also organize activities in order to increase ICT self-efficacy among students.