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Liao, A. S., Bennington, M. J., Dai, K., Irez, A. B., Sun, A., Schaffer, S., ... & Webster-Wood, V. A. (2026). “Impacts of Sterilization Method on Material Properties of 3D Printable Resins With Prolonged Exposure to Cell Culture Environments.” Journal of Biomechanical Engineering, ASME. https://doi.org/10.1115/1.4071099 |
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Hasirci, Kemal, and Alaeddin Burak Irez. “A machine learning-driven surrogate modeling approach for the structural optimization of hybrid composite wind turbine blades.” Structural and Multidisciplinary Optimization. https://doi.org/10.1007/s00158-026-04320-y |
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Hasirci, Kemal, Denizhan Yavas, and Alaeddin Burak Irez. “A Review of Advances in Composite Materials, Structural Optimization, and Machine Learning for Wind Turbine Blades: Challenges and Future Perspectives.” Polymer Composites. https://doi.org/10.1002/pc.71353 |
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| 4 |
Hasirci, Kemal, and Alaeddin Burak Irez. “Autonomous Erosion Assessment with Deep Learning Assisted Drone Imaging and Integrated Structural Analysis.” Springer Nature Singapore. https://doi.org/10.1007/978-981-95-2133-3_22 |
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Aysegul Abusoglu “A comprehensive evaluation of an integrated waste-to-biogas production facility and power plant using the extended exergy accounting (EEA) methodology.” The International Journal of Materials and Engineering Technology (TIJMET). https://doi.org/10.70858/tijmet.1834687 |
The Extended Exergy Accounting (EEA) method assesses the exergy value of labor as equivalent to the primary exergy required to produce and sustain the workforce essential to the system's long-term operation. The neoclassical economic approach, which incorporates capital flows into the thermodynamic framework, falls short in revealing the environmental impacts of various industrial, agricultural, and financial activities. In contrast, the exergy method provides a robust basis for examining the ecological effects of production activities. It allows us to directly calculate the cost of balancing wastes with environmental conditions. Evaluations of waste disposal processes and waste-to-energy (WtE) studies using the EEA method facilitate the determination of service costs in environmental impact management. In this study, a real gas turbine biopower plant, which generates 32.0 GWh of electricity and 33.5 GWh of heat annually and employs approximately 700 people, was analyzed using the EEA method. First, all sub-processes, from raw material input to electricity and heat production, were assessed using the classical exergy method. The system boundaries were then expanded to include emissions from raw material transportation, the labor and capital required for transportation, and waste management following the completion of the production process. These were evaluated through analysis using the EEA method. The exergy efficiency of the biopower plant was 52%, while the bio-methanization unit, responsible for biogas production, had an exergy efficiency of 39%. By applying the EEA method, the labor, capital, and environmental impact factors within the expanded control volume of the biopower plant were quantified in exergy units. Results showed that the exergy equivalent of the labor factor per working hour was 27.64 MJ/h, and that of the unit capital was 18.30 MJ/USD. The environmental impact assessment examined three interconnected systems responsible for disposing of emissions-CO2, N2O, CH4, and compost-generated within the expanded control volume. The labor and capital inputs for each of these systems were calculated separately. Comparative evaluations, based on the results presented in this paper and previous studies, indicate that the EEA method distinguishes itself by integrating human-induced factors into the exergy method. This perspective comprehensively addresses human labor and environmental impacts, which are often neglected in classical exergoeconomic assessments, thereby underscoring the significance of energy production from rural and municipal wastes. |
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Gökay Tavşancı, Aysegul Abusoglu, Arif Burak Çiftçi, Emre Özgül “Development and validation of a predictive SITurb combustion model for a heavy-duty turbocharged direct-injection hydrogen SI engine.” ENERGY CONVERSION AND MANAGEMENT (ECM). |
Decarbonizing heavy-duty road transport requires powertrain solutions that provide extended range and high payload capacity while meeting increasingly stringent emissions regulations. Hydrogen internal combustion engines (H2ICE) have emerged as a promising near-to mid-term alternative, benefiting from established engine manufacturing and service infrastructure. Nevertheless, the rapid flame kinetics and pronounced sensitivity to turbulence of H2ICE pose significant challenges for conventional non-predictive combustion models typically employed in one-dimensional (1D) simulations. This study develops and validates a physics-based predictive combustion model (SITurb) for a heavy-duty, 14.8-liter, turbocharged, direct injection hydrogen spark ignition (SI) engine using GT-SUITE. A measurement-driven workflow is adopted, utilizing apparent heat release rate profiles derived from Three Pressure Analyses (TPA) to calibrate key SITurb parameters across a broad operating range. The predictive model is benchmarked against a previously calibrated non-predictive SIWiebe baseline under identical experimental boundary conditions. Both modeling approaches replicate map-level performance trends with acceptable accuracy; however, SITurb consistently demonstrates enhanced combustion fidelity. Across the validated operating points, the mean absolute errors are 0.63% for brake torque and 0.33% for brake-specific fuel consumption (BSFC). Prediction of peak firing pressure is notably improved, with a mean absolute error of 0.93 bar compared to 2.36 bar for SIWiebe. These results indicate that TPA-informed calibration enhances the physical reliability of 1D predictive combustion modeling, providing a more robust numerical foundation for pressure-limited assessments, durability evaluations, and calibration-focused design studies in heavy-duty hydrogen spark-ignition applications. |
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Hilal KOÇ “Closed-form analytical solutions for the buckling and vibration of Euler-Bernoulli nanobeams within Doublet Mechanics incorporating Higher-Order Boundary Conditions.” Thin-Walled Structures. https://doi.org/10.1016/j.tws.2026.114932 |
This study presents a unified closed-form analytical framework for the buckling and vibration behavior of Euler–Bernoulli nanobeams within the Doublet Mechanics theory while consistently incorporating all variationally admissible higher-order boundary conditions. Using Hamilton’s principle, the sixth-order governing equations and associated boundary conditions are derived, enabling a general analytical formulation that is not available in existing numerical-based studies. The results show that higher-order boundary conditions have negligible influence on simply supported nanobeams, whereas their effects become significant for clamped–clamped and clamped–simply supported configurations. The dimensionless buckling load and natural frequency decrease as the scale parameter increases, indicating a clear size-dependent softening effect. Conversely, extending the beam length causes these parameters to approach the corresponding predictions of the classical Euler–Bernoulli theory. The proposed analytical solutions provide reliable benchmark data for the validation of numerical models and support the modelling, design, and optimisation of thin-walled nanoscale structural components such as carbon nanotube based resonators and sensors. |
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Eyyup Sincar, Zeki Y. Bayraktaroglu, Eray A. Baran, Evren Emre “Robust unified dual-domain control framework for high-performance parallel robots.” Control Engineering Practice. https://doi.org/10.1016/j.conengprac.2026.106803 |
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Mehmet Korkunç, Nurdan Bilgin, Zeki Y. Bayraktaroğlu “A Hierarchical State Machine and Multimodal Sensor-Fusion Approach for Active Fall Prevention in Smart Walkers.” Applied Sciences. https://doi.org/10.3390/app16104986 |
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Sunju Kang, Mustafa Mete, Srinivas Gandla, Dila Türkmen, Rohit Kadungamparambil John, Merve Acer Kalafat, Sunkook Kim, Jamie Paik “An 18-g haptic feedback ring with a three-axis force-sensing skin.” Nature Electronics. 10.1038/s41928-025-01515-x |
Wearable human–machine interfaces could provide immersive, multisensory interactions, turning everyday items into smart haptic devices for virtual and augmented reality. However, the development of tactile wearables with kinaesthetic feedback remains limited by the size and weight of the devices, which restricts portability and comfort. Here we report a haptic ring that weighs 18 g and offers three-degrees-of-freedom force sensing and feedback. The system has an origami-inspired structural base that provides efficient and compact force transmission, and a soft force-sensing skin capable of simultaneously detecting shear and normal forces. The force-sensing skin is made by combining a topology-optimized, laser-patterned layer that has pyramid microstructures with a layer with four resistive pixels, an approach that ensures linear sensitivity and a rapid response time. The ring, which is powered by soft pneumatic actuators and integrated with inkjet-printed bending sensors, can provide kinaesthetic force feedback of up to 6.5 N. |
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Tugce Caliskan, Ahmed Fahmy Soliman, Aleyna Arslan, Ozkan Bebek, Barkan Ugurlu, Merve Acer Kalafat, Ikilem Gocek “Design and experimental characterization of resistive force transducers manufactured via screen-printing.” The Journal of Mechanical Engineering Science. 10.1177/09544062251408822 |
This paper presents a systematic methodology for the development of resistive force transducers (RFTs), tailored for robotics, particularly for applications in assistive technologies such as exoskeletons. RFTs are sensors that alter their electrical resistance in response to applied force, pressure, or mechanical stress. Their adjustability, flexible design, cost-effectiveness, and ability to be easily positioned at contact points without compromising user comfort make them ideal for mapping pressure distributions over defined and limited areas. To this end, the current study aims to leverage screen printing, a scalable and cost-efficient manufacturing technique—to enable high-precision customization of sensor structures and conductive inks with tailored properties. The fabricated RFTs were integrated into Co-Ex, a bipedal exoskeleton equipped with a lower-body framework designed to assist individuals with mobility impairments. The performance of the custom RFTs was rigorously evaluated in realistic testing scenarios and benchmarked against commercially available RFTs of comparable size and specifications. Key performance metrics, including center of pressure (CoP) measurements, demonstrated that the custom RFTs exhibited performance comparable to their commercial counterparts, with CoP measurements showing strong agreement. This work highlights the potential of custom RFTs as cost-effective, flexible solutions for advanced robotics and assistive technologies. |
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L Mengue, A Benelfellah, R Matadi Boumbimba, Ozgen Colak, H Jmal Hamdi, M Pançot, L Chupin “Elium Acrylic Based Graphene Nanocomposite: Processing, Microstructure and Thermomechanical Properties.” Polymer Engineering & Science. https://doi.org/10.1002/pen.70615 |
This study demonstrates that the mechanical and thermal performance of Elium, a recyclable thermoplastic resin, can be significantly improved with a small amount of graphene addition. These results reveal that Elium-based nanocomposites, offering a sustainable alternative to thermoset composites, hold significant potential for lightweight, high-performance, and recyclable structural applications. |
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Ozgen Colak, Okan Bakbak “Cyclic Compression Response and Strain Rate Sensitivity of Graphene Oxide‐Reinforced Epoxy Nanocomposites.” Journal of Applied Polymer Science. https://doi.org/10.1002/app.70615 |
This study shows that graphene oxide reinforcement increases the strain-rate sensitivity and damage resistance of epoxy. The findings provide important contributions to the development of high-performance and more durable polymer nanocomposites operating under dynamic loads. |
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Yuksel Cakir, Ozgen Colak “Hybrid Machine Learning Approach for Predicting Stress Relaxation Behavior of Epoxy Resins.” Polymer Engineering & Science. https://doi.org/10.1002/pen.70381 |
This study demonstrates that the stress relaxation behavior of epoxy resin can be predicted with high accuracy using machine learning methods. In particular, the developed hybrid model enables more reliable prediction of long-term mechanical performance, making an important contribution to the design and service-life prediction of polymer and composite materials. |
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Ozgen Colak, Okan Bakbak, Nadia Bahlolui “A Comparative Experimental Study on High Strain Rate Response of Graphene‐Based Epoxy Nanocomposites.” Polymer Engineering & Science. https://doi.org/10.1002/pen.70259 |
The significance of this study lies in its contribution to understanding the mechanical behavior of epoxy nanocomposites under high deformation rates (dynamic loading conditions). In particular, by obtaining data close to real impact conditions through SHPB testing, energy absorption, strength, and rate sensitivity—critical in material design—were evaluated together. Moreover, it was shown that graphene derivatives (GNF and GO) can significantly alter performance even at very low ratios, but that the effectiveness largely depends on dispersion quality and filler amount. In this respect, the study serves as an important guide for the design of impact-resistant lightweight composites and advanced engineering applications. |
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Volkan Bekir Yangin, Erdinc Altug “An Improved AI-Driven LiDAR–Camera Fusion for Object Detection, Classification, and Distance Estimation in an Autonomous Electric Vehicle.” 2026 IEEE Electric Vehicles International Symposium. |
Minimizing or fully preventing human intervention in vehicles can enhance driving safety and mitigate fatal and injury-related accidents, as well as associated economic losses. Trajectory planning is a key factor in autonomous vehicles for safe driving, as it facilitates interaction between the vehicle and its surrounding environment. Object detection, classification, and distance estimation (DCD) are the main tasks of trajectory planning. Various electronic sensors, such as cameras, radar, and LiDAR, can be used to perform these tasks. These technologies can be used individually; however, their fusion has great potential to improve performance and estimation accuracy. In particular, fusing camera and LiDAR data can maximize performance by leveraging LiDAR’s superior scanning and detection capabilities, alongside the camera’s vision-based classification strengths. Moreover, the disadvantages caused by LiDAR’s inability to perform object classification can be mitigated through a fusion mechanism. This paper proposes an improved AI-driven LiDAR–camera fusion framework (AI-F) for an autonomous vehicle, aimed at DCD tasks. The proposed approach detects and classifies objects around the vehicle and focuses only on a specific person. It then computes the relative depth information of the person using a single camera. The related processes are carried out by different deep-learning models. The relative depth information is converted into pseudo-distance measurements using an artificial neural network (ANN) structure. The measurements are fused with LiDAR data to obtain the absolute distance between the person and the vehicle. The entire process is fully online and coordinated. Additionally, the proposed framework provides a practical solution since camera calibration is not required. Furthermore, there is no necessity for the objects to be fully visible to the camera; their depth information is used instead. The experiments showed that the proposed mechanism can detect and classify objects and estimate the distance between the specific person and the vehicle with superior performance. |
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Navdar, M. B., Çelebi, E., Engin, T., Kemerli, M., Serbes, S. A., İriç, S., Metin, M., & Faizan, A. A. “A novel semi-active piston-TLCD for structural vibration control: Design, modeling, and performance assessment.” Soil Dynamics and Earthquake Engineering. https://doi.org/10.1016/j.soildyn.2025.109848 |
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Serbes, S. A., Engin, T., Kemerli, M., Navdar, M. B., & Yazıcı, İ. “Semi-active piston tuned liquid column damper (SP-TLCD) for earthquake-loaded vibration suppression in structures: Functionality analysis and numerical system modelling.” Communications in Nonlinear Science and Numerical Simulation. https://doi.org/10.1016/j.cnsns.2026.110036 |
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