Browsing by Author "Fallah, Ali"

Now showing 1 - 5 of 5

Citation - WoS: 2
Citation - Scopus: 2
4D Printing of Reusable Mechanical Metamaterial Energy Absorber, Experimental and Numerical Investigation
(Iop Publishing Ltd, 2025) Fallah, Ali; Saleem, Qandeel; Scalet, Giulia; Koc, Bahattin; 01. Atılım University
This study investigates the compression behavior, energy absorption, shape memory properties, and reusability of 4D-printed smart mechanical metamaterials. Four structural configurations, i.e. honeycomb, re-entrant, and two modified re-entrant designs were developed to assess microstructure effects. Samples were fabricated using Polylactic Acid (PLA), a widely used shape memory polymer (SMP) in 4D printing, and polyethylene terephthalate glycol (PETG), an emerging SMP with demonstrated shape memory performance in recent studies. Cold-programming-induced shape recovery was evaluated at room temperature, simulating real-world conditions. Finite element simulations of compression and shape memory cycles matched experimental results well. Auxetic samples with negative Poisson's ratios showed superior energy absorption. However, only PETG demonstrated sufficient reusability, while PLA proved unsuitable for reusable designs. The PETG-3 modified re-entrant structure exhibited the best performance, with high energy absorption, delayed densification onset, and shape recovery and reusability factors of 0.95 and 0.96, respectively. Findings highlight the importance of considering both shape recovery and reusability when designing smart structures to address industrial challenges.
4D-Printed Continuous Fiber-Reinforced PLA/TPU Auxetic Composites: Mechanical Performance, Energy Absorption, Shape Recovery, and Reusability Evaluation
(SpringerNature, 2025) Alkan, Atakan; Ranjbar Aghjehkohal, Amin; Fallah, Ali; Koc, Bahattin; 01. Atılım University
This study explores the mechanical performance, energy absorption, shape recovery, and reusability of 4D-printed continuous carbon fiber-reinforced auxetic composite structures based on PLA/TPU blends, designed for load-bearing applications. PLA-TPU mixtures with different TPU content were developed to optimize the balance between flexibility and strength, with carbon fibers incorporated to enhance the mechanical properties of the resulting composites. Thermo-mechanical characterization of the blends was conducted, followed by a detailed evaluation of the structures' mechanical behavior and energy absorption capacity under room temperature conditions, simulating practical industrial scenarios. The shape recovery performance of these composite structures was also investigated. To assess reusability, the programming-recovery cycle was repeated five times, analyzing the retention of mechanical properties and shape recovery over multiple cycles to determine durability. Results revealed that TPU integration provided sufficient flexibility for cold programming, while carbon fiber reinforcement significantly enhanced stiffness and strength. The 4D-printed composites exhibited consistent shape recovery and maintained mechanical integrity after five cycles, confirming their reusability. These findings demonstrate the potential of 4D-printed PLA/TPU-based carbon fiber-reinforced composites as smart, durable materials for load-bearing applications in industries such as biomedical engineering, automotive, and aerospace.
Citation - WoS: 2
Citation - Scopus: 1
Large 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 Mohammadi; Automotive Engineering; 06. School Of Engineering; 01. Atılım University
This 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.
Citation - WoS: 4
Citation - Scopus: 4
Physics-Informed Neural Network for Bending Analysis of Twodimensional Functionally Graded Nano-Beams Based on Nonlocal Strain Gradient Theory
(Univ Tehran, Danishgah-i Tihran, 2025) Esfahani, Saba Sadat Mirsadeghi; Fallah, Ali; Aghdam, Mohammad Mohamadi; Automotive Engineering; 06. School Of Engineering; 01. Atılım University
This paper presents the bending analysis of two-dimensionally functionally graded (2D FG) nano-beams using a physics-informed neural network (PINN) approach. The material properties of the nanobeams vary along their length and thickness directions, governed by a power-law function. Hamilton's principle, combined with the nonlocal strain gradient theory (NSGT) and Euler-Bernoulli beam theory, is employed to derive the governing equation for the bending analysis of 2D FG nanobeams. Due to the incorporation of size dependency and the variation of material properties in two dimensions, the governing equation becomes a high-order variable- coefficient differential equation, which is challenging, if not impossible, to solve analytically. In this study, the applicability of PINN for solving such high-order complex differential equations is investigated, with potential applications in nanomechanical engineering. In the PINN approach, a deep feedforward neural network is utilized to predict the mechanical response of the beam. Spatial coordinates serve as inputs, and a loss function is formulated based on the governing equation and boundary conditions of the problem. This loss function is minimized through the training process of the neural network. The accuracy of the PINN results is validated by comparing them with available reference solutions. Additionally, the effects of material distribution, power-law index (in both length and thickness directions), nonlocal strain gradient parameters, and material length scale parameters are investigated. This study demonstrates the versatility of the PINN approach as a robust tool for solving high-order differential equations in structural mechanics.
Physics-Informed Neural Network for Nonlinear Bending Analysis of Nano-Beams: A Systematic Hyperparameter Optimization
(MDPI, 2025) Esfahani, Saba Sadat Mirsadeghi; Fallah, Ali; Aghdam, Mohammad Mohammadi; 01. Atılım University
This paper investigates the nonlinear bending analysis of nano-beams using the physics-informed neural network (PINN) method. The nonlinear governing equations for the bending of size-dependent nano-beams are derived from Hamilton's principle, incorporating nonlocal strain gradient theory, and based on Euler-Bernoulli beam theory. In the PINN method, the solution is approximated by a deep neural network, with network parameters determined by minimizing a loss function that consists of the governing equation and boundary conditions. Despite numerous reports demonstrating the applicability of the PINN method for solving various engineering problems, tuning the network hyperparameters remains challenging. In this study, a systematic approach is employed to fine-tune the hyperparameters using hyperparameter optimization (HPO) via Gaussian process-based Bayesian optimization. Comparison of the PINN results with available reference solutions shows that the PINN, with the optimized parameters, produces results with high accuracy. Finally, the impacts of boundary conditions, different loads, and the influence of nonlocal strain gradient parameters on the bending behavior of nano-beams are investigated.