Atılım Academic Archive (Atılım University)
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Machining Performance and Carbon Footprint Analysis of Inconel 718 Using Different Minimum Quantity Lubrication Oils
No full textInconel 718, üstün korozyon ve oksidasyon direnci ile yüksek dayanım özellikleri sayesinde yüksek sıcaklık uygulamaları için son derece uygun bir malzemedir. Ancak, yüksek sertliği, aşınma direnci ve belirgin işleme sertleşme oranı nedeniyle işlenmesi zordur. Geleneksel Kesme Sıvıları (GKS), bu zorlukların üstesinden gelmede yetersiz kalmakta ve yüksek tüketim oranları ile insan sağlığına ve çevreye olan zararlı etkileri nedeniyle sürdürülebilir bir çözüm sunmamaktadır. Bu nedenle, Minimum Miktarda Yağlama (MMY), GKS'ye etkili bir alternatif olarak dikkat çekmektedir. Sıkıştırılmış hava ile karıştırılan az miktarda yağın aerosol formunda kesme bölgesine iletilmesiyle, MMY özellikle Inconel 718 gibi işlenmesi zor malzemeler için çevre dostu ve sürdürülebilir bir üretim yöntemi sunmaktadır. Aerosol uygulama yöntemi, kesici takım ile iş parçası arasındaki bölgeye daha iyi nüfuz ederek işleme verimliliğini artırmaktadır. Ancak, MMY uygulamalarında kullanılan yağların doğrudan işleme performansını etkilemesi, işlenecek malzemeye özgü yağların geliştirilmesini kritik hâle getirmektedir. Bu çalışmada, Inconel 718'in işlenebilirliğini artırmaya yönelik olarak geliştirilen üç farklı MQL yağı önce fiziksel özellikleri ve performansları açısından test edilmiştir. Ardından, Inconel 718 malzemesi üzerinde yapılan kanal frezeleme deneylerinde performansları değerlendirilmiştir. Geleneksel İşleme (Gİ), kuru işleme ve MMY yöntemleri karşılaştırmalı olarak ele alınmış; kesme kuvvetleri, takım aşınması, yüzey kalitesi gibi temel performans metriklerine ek olarak, çevresel etki ve sürdürülebilirlik, karbon ayak izi temel alınarak değerlendirilmiştir. Elde edilen bulgular, MMY yönteminin işleme performansını önemli düzeyde artırırken karbon salımını azalttığını ve Inconel 718'in verimli ve sürdürülebilir şekilde işlenmesi için daha uygun bir çözüm sunduğunu ortaya koymaktadır.Inconel 718 is highly suitable for high-temperature applications due to its exceptional corrosion and oxidation resistance, along with its high strength at elevated temperatures. However, its high hardness, wear resistance, and significant work hardening rate make its machining challenging. Conventional Cutting Fluids (CCFs) are inadequate for addressing these challenges and are not sustainable due to their high consumption and harmful effects on human health and the environment. Minimum Quantity Lubrication (MQL) has emerged as an effective alternative to CCFs. By delivering a small amount of oil mixed with compressed air in aerosol form to the cutting zone, MQL promotes sustainable and environmentally friendly manufacturing, particularly for hard-to-machine materials like Inconel 718. The aerosol delivery method improves penetration between the cutting tool and workpiece, enhancing efficiency. However, the oils used in MQL applications directly affect machining performance, making it crucial to develop oils specific to the material being machined. In this study, three different MQL developed to improve the machining performance of Inconel 718 were first tested for their physical characteristics and performance. Subsequently, their performance during slot milling experiments of Inconel 718 material was evaluated. A comparative investigation of CCFs, dry machining and MQL is provided with the focus on critical performance metrics including cutting forces, tool wear, surface finish alongside an assessment of environmental impact and sustainability in terms of carbon footprint. The findings demonstrate that MQL significantly enhances machining performance while reducing carbon emissions, offering a more viable solution for efficient and sustainable machining of Inconel 718
Ann-assisted Numerical Modelling of Lifetime Estimation for Single Crystals in Turbine Blades
No full textNikel bazlı tek kristalli süperalaşımlar, üstün mekanik dayanımları, sünme direnci ve yüksek sıcaklıklardaki kararlılıkları sayesinde modern türbin kanadı uygulamalarının temel malzemeleri arasında yer almaktadır. Bu malzemeler, yüksek gerilme genlikleri, sıcaklık gradyanları ve bekletme sürelerini içeren karmaşık termomekanik yorulma (TMF) döngülerine sıklıkla maruz kalmaktadır. Özellikle uzay ve savunma sanayisinde kullanılan bileşenler için deneysel veri kısıtlılığı ve tek kristallerin anizotrop davranışı, bu zorlayıcı yükleme koşulları altında yorulma ömrü tahminini olduk- ça güçleştirmektedir. Bu zorluklardan yola çıkan bu tez, yorulma ömrünü tahmin etmek ve sınırlı veri koşullarında bile malzeme parametrelerini yüksek doğrulukla belirlemek amacıyla fizik tabanlı sürekli hasar mekaniğini (CDM) ve veri odaklı makine öğrenimi modellerini birleştiren hibrit bir öngörü çerçevesi sunmaktadır. Bu çalışmada, sentetik yorulma ömrü verilerinin oluşturulmasında temel olarak kullanılan model, FCC kristal yapısına sahip tek kristalli süperalaşımların anizotrop davra- nışlarını ve sıcaklığa bağlı malzeme tepkilerini dikkate alan, özelleştirilmiş bir Chaboche sürekli hasar modeli olmuştur. Ancak, yorulma ömrü tahmininde esas rolü oynayan yapı, farklı veri türleriyle eğitilen yapay sinir ağı (YSA) modelleridir. Tezin üçüncü bölümü- nün başında iki ayrı YSA modeli tanıtılmaktadır. İlk model, sınırlı UTS ve dayanım bilgileriyle birlikte deneysel yorulma ve sünme verilerinden oluşturulan yarı-sentetik veri setini kullanarak hem sünme kopma süresini hem de yüksek çevrimli ve birleşik çevrimli yorulma (HCF ve CCF) koşullarındaki yorulma ömrünü tahmin etmektedir. İkinci model ise tamamen, özelleştirilmiş Chaboche modeli ile oluşturulan sentetik SN eğrileri ile eğitilmiştir. Bu model, sıcaklık ve R oranlarına bağlı olarak normalleştirilmiş ve logaritmik ölçeklenmiş girdiler üzerinden SN eğrileri ve Goodman diyagramları üretmek üzere yapılandırılmıştır. Bu iki model, geniş çalışma koşul- larını kapsayabilen tahmin altyapısını oluşturmaktadır. Deneysel yorulma verilerinin dağınık ve yetersiz olması problemini aşmak amacıyla, sentetik verilerle eğitilen YSA modelleri, transfer öğrenmesi yaklaşımlarıyla hem CMSX-4 hem de AISI 4340 gibi genel metal türleri için elde edilen deneysel veri kümelerine uyarlanmıştır. Bu bağlamda, biri önceden eğitilmiş modelin parametrelerinin doğrudan ayarlanmasına, diğeri ise yeni fiziksel değişkenlerin modele dâhil edilmesini sağlayan bir giriş genişletme yapısına dayalı iki farklı transfer öğrenmesi modeli geliştirilmiştir. Her iki model de veri güvenilirliğini sağlamak amacıyla bırak-bir çapraz doğrulama (LOOCV) yöntemi ile değerlendirilmiştir. Bu yaklaşımlar, sınırlı deneysel veriyle yorulma ömrü tahminlerinin gerçekleştirilebileceğini ve model tutarlılığının sıcaklık ve gerilme oranı ekseninde korunabileceğini göstermektedir. Yorulma ömrü tahminine ek olarak, bu tezde, gerilme-şekil değiştirme verilerinden malzeme model parametrelerini tersine belirlemeyi amaçlayan, veri odaklı bir parametre optimizasyonu süreci de sunulmaktadır. Bu kapsamda, Voce tipi izotropik pekleşme içeren, oransal olmayan J2 plastiklik modeli ile oluşturulan sonlu eleman simülasyonları üzerinden bir veri seti üretilmiş ve tam gerilme-şekil değiştirme eğrisinden model parametrelerine haritalama yapabilen bir YSA regresyon modeli eğitilmiştir. Bu yapı, güçlü doğrusal olmayan plastik davranışlarda dahi temel malzeme sabitlerini yüksek doğrulukla ve verimli bir şekilde geri kazanabilmektedir. Sonuç olarak, bu tezde sunulan yöntemsel yapı, yüksek performanslı malzemelerde yorulma ömrü değerlendirmesi, model kalibrasyonu ve veri genişletmesi gibi alanlarda esnek ve ölçeklenebilir bir çözüm sağlamaktadır. Sentetik veri üretimi, YSA tabanlı regresyon ve transfer öğrenmesini tek bir çerçevede birleştiren bu yaklaşım, sınırlı deneysel destekle anizotropik ve yüksek sıcaklık dayanımı gerektiren malzeme- lerin yorulma modellemesine dair süregelen zorluklara etkin bir çözüm sunmaktadır. Bu kapsamda geliştirilen yöntemler, yalnızca tahmin doğruluğunu artırmakla kalmayıp, aynı zamanda gaz türbin motoru bileşenleri gibi kritik uygulamalarda yorulma modelleme araçlarının yorumlanabilirliğini ve uyarlanabilirliğini de geliştirmektedir.Nickel-based single crystal superalloys, such as CMSX-4, are the backbone of modern turbine blade applications due to their exceptional mechanical strength, creep resistance, and thermal stability under extreme service conditions. These materials are frequently subjected to complex thermo-mechanical fatigue (TMF) cycles, including high stress amplitudes, temperature gradients, and dwell times. Accurate lifetime assessment under such demanding loading conditions remains a critical challenge due to the anisotropic behavior of single crystals and the limited availability of experimental data, particularly for components used in aerospace and defense sectors. Motivated by these challenges, this thesis explores a hybrid predictive framework that combines physics-based continuum damage mechanics (CDM) with data-driven machine learning models to assess fatigue lifetime and identify material parameters with high precision, even under data scarcity. A modified Chaboche continuum damage model serves as the foundation for the generation of synthetic fatigue lifetime data in this study. This model is tailored to capture the anisotropic behavior of FCC single crystal superalloys, incorporating temperature-dependent material responses across a range of stress ratios and loading conditions. However, the broader fatigue lifetime prediction framework is built upon artificial neural networks (ANNs), which are trained on multiple types of input data. Two distinct ANN-based models are introduced. The first model uses semi-synthetic datasets combining experimental fatigue and creep data with limited UTS/endurance information to predict both creep rupture time and fatigue lifetime under high-cycle and combined-cycle fatigue (HCF and CCF) conditions. The second model is trained exclusively on fully synthetic SN curves generated using the modified Chaboche law. It is designed to reproduce lifetime predictions across various temperatures and R-ratios, generating SN curves and Goodman diagrams from normalized, log-scaled inputs. Together, these models establish a predictive backbone capable of extrapolating fatigue behavior across a wide range of operational domains. To overcome the critical limitation of sparse and scattered experimental fatigue data, transfer learning strategies are applied to adapt the synthetic-data-trained ANNs to experimental datasets from both CMSX-4 and generic metals such as AISI 4340. Two transfer learning models are developed: one based on direct parameter tuning of a pretrained model and another incorporating an input expansion scheme to integrate new physical variables. These models are evaluated using leave-one-out cross-validation (LOOCV) to ensure robustness and generalizability. The transfer learning approach demonstrates significant potential in predicting fatigue lifetime from minimal experimental data while preserving model consistency across temperature and R-ratio domains. In addition to fatigue life prediction, the thesis presents a data-driven parameter optimization pipeline designed to infer constitutive model parameters from stress–strain data. This approach leverages a custom-built finite element-based simulation loop to generate datasets using a rate-independent J2 plasticity model with Voce-type hardening. A neural network regressor is trained to map the full stress–strain profile to the underlying material parameters, enabling inverse modeling from experimental or simulated mechanical responses. This framework is capable of recovering key material constants efficiently and accurately, even for highly nonlinear plastic behavior. Overall, the proposed framework offers a versatile and scalable methodology for fatigue life assessment, model calibration, and data augmentation in high-performance materials. By synthesizing synthetic data generation, ANN-based regression, and transfer learning under a unified pipeline, this thesis addresses the longstanding challenge of modeling fatigue in anisotropic, high-temperature materials with limited experimental support. The methods developed here not only contribute to predictive accuracy but also enhance the interpretability and adaptability of fatigue modeling tools in critical applications such as gas turbine engine components
A Note on the Structure of Interpolative Metric Spaces
No full textIn this note we observe that interpolative metric spaces lie between strong b-metric spaces and b-metric spaces. This paper aims to comprehend the connections of these notions and the corresponding structures, namely, interpolative metric spaces, strong b-metric spaces, and b-metric spaces. An example is considered to illustrate our claims
Enhancing Machining Efficiency and Sustainability of Ti-6Al-4V Through Minimum Quantity Lubrication With Ester-Based Oils
No full textNamlu, Ramazan Hakki/0000-0002-7375-8934Ti-6Al-4 V is known as difficult-to-cut due to its low thermal conductivity and high chemical reactivity. While cutting fluids aid lubrication and reduce friction, Conventional Cutting Fluids (CCF) have high consumption, limited efficiency gains and negative environmental and health effects. Therefore, there is an ongoing search for more sustainable alternatives to CCF that do not adversely affect machining performance. Minimum Quantity Lubrication (MQL), which delivers compressed air - oil aerosol, has emerged as a promising solution by drastically reducing fluid use and associated risks. Selecting the right MQL fluid is key to optimising machining performance. This study evaluates MQL fluids based on polyol and polymeric esters for Ti-6Al-4 V machining and compares their performance with CCF. Cutting forces, surface roughness and topography are examined. Results show that MQL reduces cutting forces up to 21.7% and surface roughness up to 57.6% compared to CCF, with more uniform surface topography. Among MQL oils, polymeric esters perform better than polyol esters, with a reduction in cutting force up to 14.6% and surface roughness up to 47.7%. High viscosity indexed polymeric esters showed the best overall performance due to their thermal stability. Moreover, according to the sustainability assessment analysis polymeric esters were identified as the most sustainable option.SMART Eureka [S0411]This work was supported by the SMART Eureka [S0411]Emerging Sources Citation Inde
A Model-Based Evaluation Metric for Question Answering Systems
No full textThe paper addresses the limitations of traditional evaluation metrics for Question Answering (QA) systems that primarily focus on syntax and n-gram similarity. We propose a novel model-based evaluation metric, MQA-metric, and create a human-judgment-based dataset, squad-qametric and marco-qametric, to validate our approach. The research aims to solve several key problems: the objectivity in dataset labeling, the effectiveness of metrics when there is no syntax similarity, the impact of answer length on metric performance, and the influence of real answer quality on metric results. To tackle these challenges, we designed an interface for dataset labeling and conducted extensive experiments with human reviewers. Our analysis shows that the MQA-metric outperforms traditional metrics like BLEU, ROUGE and METEOR. Unlike existing metrics, MQA-metric leverages semantic comprehension through large language models (LLMs), enabling it to capture contextual nuances and synonymous expressions more effectively. This approach sets a standard for evaluating QA systems by prioritizing semantic accuracy over surface-level similarities. The proposed metric correlates better with human judgment, making it a more reliable tool for evaluating QA systems. Our contributions include the development of a robust evaluation workflow, creation of high-quality datasets, and an extensive comparison with existing evaluation methods. The results indicate that our model-based approach provides a significant improvement in assessing the quality of QA systems, which is crucial for their practical application and trustworthiness. © 2025 World Scientific Publishing Company.Science Citation Index Expande
Latent Psychological Pathways in Thermal Comfort Perception: The Mediating Role of Cognitive Uncertainty on Depression and Vigour
No full textAlkan, Nese/0000-0002-5615-3183Thermal comfort is the condition of mind that expresses satisfaction with the thermal environment, and it is assessed through subjective evaluation, according to the American Society of Heating, Refrigerating, and Air-Conditioning Engineers. While research has traditionally emphasised physical factors, growing evidence highlights the role of the state of mind in shaping thermal perception. In a prior Monte Carlo sensitivity analysis, six mood subscales-Anger, Confusion, Vigour, Tension, Depression, and Fatigue-were examined for how they affect the absolute difference between actual and predicted thermal sensation. Depression and vigour were found to be the most influential, while confusion appeared least impactful. However, to accurately assess the role of confusion, it is necessary to consider its potential interactions with other mood subscales. To this end, a mediation analysis was conducted using Hayes' PROCESS tool. The mediation analyses revealed that confusion partially mediated depression's effect in males and vigour's effect in females. These results suggest that, despite a weak direct impact, confusion critically influences thermal perception by altering the effects of key mood states. Accounting for the indirect effects of mood states may lead to more accurate predictions of human sensory experiences and improve the design of occupant-centred environments.Scientific and Technological Research Council of Turkiye [120M890]This research was funded by the Scientific and Technological Research Council of Turkiye (Grant Number: 120M890)
Robust Backstepping Control of a Quadrotor Unmanned Aerial Vehicle Under Colored Noises
No full textKARAHAN, MEHMET/0000-0002-5828-497XAdvances in software and hardware technologies have facilitated the production of quadrotor unmanned aerial vehicles (UAVs). Nowadays, people actively use quadrotor UAVs in essential missions such as search and rescue, counter-terrorism, firefighting, surveillance, and cargo transportation. While performing these tasks, quadrotors must operate in noisy environments. Therefore, a robust controller design that can control the altitude and attitude of the quadrotor in noisy environments is of great importance. Many researchers have focused only on white Gaussian noise in their studies, whereas researchers need to consider the effects of all colored noises during the operation of the quadrotor. This study aims to design a robust controller that is resistant to all colored noises. Firstly, a nonlinear quadrotor model was created with MATLAB. Then, a backstepping controller resistant to colored noises was designed. The designed backstepping controller was tested under Gaussian white, pink, brown, blue, and purple noises. PID and Lyapunov-based controller designs were also carried out, and their time responses (rise time, overshoot, settling time) were compared with those of the backstepping controller. In the simulations, time was in seconds, altitude was in meters, and roll, pitch, and yaw references were in radians. Rise and settling time values were in seconds, and overshoot value was in percent. When the obtained values are examined, simulations prove that the proposed backstepping controller has the least overshoot and the shortest settling time under all noise types.Science Citation Index Expande
Ultrafast Green Synthesis of Superparamagnetic Hybrid Nanoparticle for Advanced Applications
No full textThe integration of superparamagnetic iron oxide nanoparticles (SPIONs) into functional hybrid nanostructures remains a challenge, particularly in preserving their magnetic properties within composite frameworks. Herein, we present a rapid and environmentally friendly synthesis strategy for SPION-decorated polyhedral oligomeric silsesquioxane (POSS) nanoparticles. The process involves UV-induced polymerization to form POSS nanoparticles, followed by in situ SPION precipitation, with comprehensive characterization performed via SEM, FTIR, XRD, and VSM analyses. This approach enables the fabrication of hybrid nanoparticles (similar to 160 nm) within 5 min and subsequent SPION decoration in under an hour, ensuring (10 emu/g) superparamagnetic behavior. The developed method is highly scalable, efficient, and compatible with green chemistry principles, making it a promising platform for engineering advanced hybrid nanostructures. These nanoparticles hold significant potential for applications in biomedicine, catalysis, and next-generation material science.Turkiye Bilimsel ve Teknolojik Arastirma Kurumu [122C228]This work was supported by the Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (122C228)
An Updated Meta-Analysis on the Association Between Celiac Disease and Cardiovascular Diseases
No full textAsadi Anar, Mahsa/0000-0002-5772-2472Objectives: Research on the relationship between celiac disease (CD) and cardiovascular disease (CVD) is still ongoing, and different studies have reported contradictory findings. To carry out a meta-analysis and systematic review to look into the connection between CD and CVD risk. Methods: A thorough search was conducted in PubMed, Scopus, and Google Scholar databases up to February 19, 2024. Relevant articles were extracted, and the titles, abstracts, and full texts of the related articles were screened. The quality of the studies was assessed using the Joanna Briggs Institute critical appraisal tools. Results: Nine cohort and one case-control studies involving 49,621,333 individuals were included in the meta-analysis. The pooled analysis revealed a 7% increased risk of CVD in CD patients compared to controls (OR: 1.07, 95% CI: 1.03-1.10, P < 0.05). Significant heterogeneity was observed among studies (I2 = 76%, P < 0.001). Conclusion: This meta-analysis provides evidence of a modest but significant increase in CVD risk in patients with CD. The results highlight the importance of considering cardiovascular health in CD treatment and the need for further research to elucidate the mechanisms underlying this association and to develop targeted prevention strategies.Emerging Sources Citation Inde
Robust and Adaptive Control of an Unmanned Aerial Vehicle
No full textBu tezin amacı, bir İnsansız Hava Aracı (İHA) için gürbüz ve uyarlanabilir yönelim ve irtifa kontrolcüleri tasarlamak ve performanslarını değerlendirmektir. Dört rotorlu İHA sistemi kontrolü için geleneksel ve modern kontrol yöntemleri uygulanmıştır. Geleneksel yöntem, Oransal-İntegral-Türevsel kontrolünü ifade ederken, modern yaklaşımlar Aktif Bozulma Reddetme Kontrolü (ADRC) ve Geri Besleme Hatası ile Öğrenme kontrolcüsü içerir. ADRC yaklaşımında, dört rotorlu İHA sisteminin doğrusal olmayan dinamiklerini ele almak için 2. derecen 4. dereceye kadar Genişletilmiş Durum Gözlemcileri kullanılmaktadır. Dört rotorlu İHA davranışını etkili bir şekilde analiz edebilmek için sisteme ait dinamik model geliştirilmiştir. Dört rotorlu İHA sistemi dinamiğinin eksik eyleyici yapısından dolayı modelleme karmaşık bir durum oluşturmaktadır, dört kontrol girdisi on iki durum değişkenini etkiler ve bu durum, öteleme dinamiklerinin rotasyonel dinamikleriyle bağlantılı olmasından kaynaklıdır. Sistem modeli tasarlandıktan sonra kontrol mimarileri oluşturulmuş ve hedeflenen yörüngelerin doğru bir şekilde takip edilmesini sağlamak için kontrol parametreleri atanmıştır. Performans değerlendirmeleri hem kare hem de helizon yörüngeler için gerçekleştirilmiştir. Tasarlanan kontrolcülerin dayanıklılığını değerlendirmek amacıyla, tüm dinamiklere farklı harici bozucular uygulanmıştır. Diğer yandan, kontrolcülerin uyarlanabilirliğini incelemek için sistem parametrelerinde değişiklikler yapılmıştır. Tasarlanan kontrolcülerin harici bozucuları ve parametre belirsizliklerini reddetme performansları, kontrol metrikleri ile analiz edilerek ve yorumlanarak değerlendirilmiştir.The primary objective of this thesis is to design robust and adaptive attitude and altitude controllers for an Unmanned Aerial Vehicle and make assessments on performances. The thesis involves traditional and modern control methods for a quadrotor system. Traditional method implies Proportional-Integral-Derivative (PID) controller while modern approaches include Active Disturbance Rejection Control (ADRC) and Feedback Error Learning (FEL) which implies Artificial Neural Network (ANN) with PID as well as ADRC. The ADRC method substitutes Proportional - Derivative controller and integrates it with the Extended State Observer with an order of 2nd, 3rd and 4th to handle the disturbances and uncertainties. In order to effectively analyse the behaviour of the quadrotor, dynamic model is developed. Due to the underactuated nature of the quadrotor, modelling has a significant challenge, as the four control inputs influence six state variables, with the translational dynamics being coupled with the rotational dynamics. After deriving the system model, controller architectures are constructed, and the control parameters are assigned to enable accurate tracking of desired trajectories. Performance evaluations are conducted for both square and helical trajectories. To assess the robustness of the designed controllers, different external disturbances are applied to all dynamics. On the other hand, to study the adaptivity of them, system parameters variation is tested. The performance evaluation of the designed controllers conducted to make comparison of rejection ability for both external disturbances and parametric uncertainties considering evaluation metrics