PSI - Issue 71

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Procedia Structural Integrity 71 (2025) 1–3

5 th International Structural Integrity Conference & Exhibition (SICE 2024) Preface

Atul Ramesh Ballal a , Jatin Bhatt a , Dilip Peshwe a* , Rajesh Khatirkar a , Raghu Prakash b a Department of Metallurgical & Materials Engineering, Visvesvaraya National Institute of Technology, Nagpur 440 010, India. b Department of Mechanical Engineering, Indian Institute of Technology Madras, Chennai, 600 036, India.

* Corresponding author. Tel.: +91-9372802996 E-mail address: dilippeshwe@mme.vnit.ac.in

Structural Integrity assessment and assurance for safe operation of safety critical structures, components is a multi disciplinary field spread over several domains of engineering mechanics, materials science, material processing, design, manufacturing, assembly, testing, certification apart from understanding the use of such systems in the hands of the operator, given the diverse environmental conditions and its interactions. Structural integrity is crucial for strategic stakeholders in sectors like transportation, defense, energy, civil and marine infrastructure, and many more. The Indian Structural Integrity Society (InSIS) has been organizing biennial Structural Integrity Conference and Exhibition (SICE) conferences aimed at deliberating the state-of-the-art research findings as well as professional networking since 2016. Previous SICE conferences were held at Bengaluru (2016), Hyderabad (2018 and 2022), and Mumbai (2020 – online mode). A conference publication which is a compilation of peer reviewed manuscripts presented at the SICE conference is brought out as special issues or edited volumes. This special issue brought out in association with of the publishers of Procedia Structural Integrity presents 64 selected peer reviewed papers from the many presentations made during the 5 th International Structural Integrity Conference and Exhibition (SICE) that was held at Visvesvaraya National Institute of Technology (VNIT) Nagpur, India during October 22-24, 2024. SICE 2024 received a very good response with over 250 delegates from several leading industries, academic and R&D

2452-3216 © 2025 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of SICE 2024 organizers 10.1016/j.prostr.2025.08.001

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institutions of global repute from Japan, Russia, Belgium, Canada, United Kingdom, United States, and India attending the event. The delegates had varied engineering specializations such as Mechanical, Metallurgical & Materials, Mining, Civil, and Chemical Engineering; nevertheless, were focused on structural integrity. The major research topics covered in the conference included Damage mechanisms involving Fatigue and Creep, Fracture mechanics, Novel test methods involving miniature specimen, Computational methods for analysis, Composites, Structural materials in Civil, Marine, and Aerospace applications, Digital twins, Modeling & Simulation, and Structural health monitoring. One of the highlights of SICE-2024 was the introduction of Dr. S. R. Valluri Memorial Lecture , in memory of Dr. S. R. Valluri whose contributions to the domain of fatigue and structural integrity are well known globally. Dr. Ramasubbu Sunder, given his close interaction with Dr. Valluri, was the unanimous choice for delivering the lecture; Sunder presented on the topic “In pursuit of excellence in the advancement of the science & technology of structural integrity” with Prof. B. Dattaguru as the Chair. SICE 2024 had five plenary lectures covering aspects of Aircraft Structural Integrity program (ASIP) and Engine Structural Integrity Program (EnSIP) (Andrew Rosenberger), Environment Assisted Cracking (S. Chandrasekar), Laser Shock Peening (Oleg Plekhov), Creep and Fracture properties estimation from cantilever bending (Vikram Jayaram) and Hydrogen embrittlement in stainless steels (Shiro Torizuka). The conference had twenty-three keynote lectures, thirty-eight invited lectures, and forty-eight contributory presentations delivered by distinguished scientists, engineers, and academicians across the world. Additionally, ninety-three video-poster presentations were made by student authors and young researchers. The organizers of SICE-2024 appreciate the research work, knowledge, and insight shared by all the esteemed speakers. Several industrial organizations showcased their products in the Exhibition during SICE 2024. Two pre-conference workshops were organized for graduate students, researchers, and engineers from the industry: 1) Microstructural Characterization for Failure Analysis of Structural Components, and 2) Mechanical Testing of Alloys and Composites . Both the workshops included lectures as well as demonstrations of testing methods and analysis. The guidance of InSIS Executive Committee and the hard efforts put in by the organizing team of VNIT Nagpur with support from Jawaharlal Nehru Aluminium Research Development & Design Center (JNARDDC) culminated into making SICE 2024 a big success. Sincere thanks are due to the sponsors and to all the delegates for an engaging as well as inspiring meeting of the Structural Integrity community during SICE 2024. This special issue presents a judicious mix of papers highlighting the state-of-the-art research and analysis in the vast domain of Structural integrity. This volume is intended for a wide audience of materials scientists, practicing engineers, researchers, and scholars. We hope that this collection of research works will inspire readers to gain more knowledge through further research, to enhance interactions, and to collaborate actively in this crucial field, ultimately leading to the development of safer, reliable, and sustainable structures. We thank the authors for their time and effort to present their work and submit their manuscript in a timely manner. We express our deep appreciation for all the reviewers for their painstaking efforts in critically reviewing the

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manuscripts. Special thanks to Elsevier for agreeing to bring out this special issue of Procedia Structural Integrity, highlighting the developments and advancements in the important field i.e. Structural Integrity.

Atul Ramesh Ballal Jatin Bhatt Dilip Peshwe Rajesh Khatirkar Raghu Prakash

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Procedia Structural Integrity 71 (2025) 461–468

5 th International Structural Integrity Conference & Exhibition (SICE 2024) A CPFEM based 3D Model for Polycrystalline Plasticity with Diffused Grain Boundaries

Ayub Khan * , A Shivnag Sharma, Pritam Chakraborty

Department of Aerospace Engineering, IIT Kanpur, Kanpur, UP -208016, India

Keywords: Crystal plasticity; Diffused interface; Microstructure; Polycrystals; Geometrically necessary dislocations 1. Introduction Polycrystalline materials constitute a cornerstone of engineering, finding widespread applications in critical industries such as aerospace, automotive, and structural engineering. Their versatility arises from a combination of desirable mechanical properties, including strength, ductility, and toughness, making them indispensable for various structural and functional components. However, unlocking the full potential of these materials necessitates a deep understanding of their intricate microstructural characteristics and their influence on macroscopic behavior (Asaro and Rice (1977). Abstract Understanding the relationship between the microstructure of polycrystalline materials and their macroscopic properties is critical for developing and improving them for advanced applications. In polycrystalline aggregates, the combination of computational homogenization and crystal plasticity has shown promise in simulating the effective properties and capturing such correlations. Our study uses a similar framework to model polycrystals with FCC and BCC crystal structures. The goal is to investigate the plastic deformation behavior of these materials, specifically focusing on the role of Geometrically Necessary Dislocations (GNDs). We employ a phenomenological Crystal Plasticity (CP) model on a cubic Representative Volume Element (RVE). The simulations capture the effect of crystallographic orientations, grain boundaries, and dislocation mechanisms on the deformation of polycrystalline RVE. To account for the complex behavior at grain boundaries, a diffused interface model is proposed. This model homogenizes the deformation behavior within the grain boundary region to capture more effectively the hardening due to dislocation pile-up, providing a more realistic representation of the interaction between grains. Our findings provide insights into the influence of grain boundaries on the overall strain hardening and deformation response of polycrystalline materials. © 2025 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of SICE 2024 organizers

* Corresponding author. Tel.: +91-8791082898.

E-mail address: ayubk21@iitk.ac.in

2452-3216 © 2025 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of SICE 2024 organizers 10.1016/j.prostr.2025.08.062

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A key challenge in designing and developing advanced engineering materials lies in accurately predicting and manipulating their mechanical behavior. Computational modeling, particularly at the mesoscale, has emerged as a powerful tool for uncovering the mechanisms that govern the behavior of polycrystalline materials. By bridging the gap between microscale deformation processes and macroscale behavior, these models provide valuable insights into phenomena such as texture evolution, strain localization, and damage initiation. Theoretical investigations into polycrystals have suggested that the inhomogeneous plastic deformation between neighboring grains may be responsible for the grain-size dependence (i.e., Hall – Petch relationship) of flow stress (Acharya and Beaudoin (2000); Dai and Parks (1997)). One can partition crystallographic dislocations into two types: geometrically necessary dislocations (GNDs), which are associated with the curvature of the crystal lattice and plastic strain field incompatibility, and statistically stored dislocations (SSDs), which arise from random trapping during homogeneous deformation. Consequently, part of the dislocation population in a crystal is linked to the plastic strain gradient (Cermelli and Gurtin (2001)). Our study employs a phenomenological Crystal Plasticity (CP) model (Kalidindi et al. (1992)) to capture the plastic deformation behavior of polycrystals with face-centered cubic (FCC) and body-centered cubic (BCC) crystal structures. Within this framework, the quantification of Geometrically Necessary Dislocations (GNDs) is achieved through the utilization of Nye's dislocation tensor (Nye (1953); Anahid et al. (2011)), which quantifies the gradients of plastic shear, to capture the additional hardening, specifically near the grain boundaries. To enhance the fidelity of our simulations, the constitutive equations are augmented with a diffused interface model. This model homogenizes the deformation behavior in the grain boundary (GB) region to capture more effectively the hardening due to dislocation pile-up. In order to simulate realistic polycrystals, Electron Backscatter Diffraction (EBSD) data obtained from pure iron is used. These experimental datasets serve as the basis for constructing cuboidal simulation domains representative of the polycrystalline microstructure. A biased mesh generation technique (Thondiraj et al. (2024)) is employed that uses coarser elements in the bulk of the grain, assuming it has lower gradients in response, and finer elements near grain boundaries to capture the large gradients. Experimental studies have explored the deformation behavior near grain boundaries, particularly by correlating grain misorientation with variations in displacement fields (Schroeter and McDowell (2003); Kamaya (2004)). However, there is a notable scarcity of experimental investigations that provide insights into the local stress-strain behavior near grain boundaries at such fine length scales. This limitation hinders the direct validation of local response obtained from computational models using available experimental data. Furthermore, the present study aims to predict regions of elevated normal and shear stresses near grain boundaries, which are hypothesized to play a critical role in phenomena such as crack nucleation, grain boundary diffusion, and migration, which are pivotal in understanding material behavior and failure mechanisms. By running CP Finite Element Method (CPFEM) simulations on the polycrystalline RVE, macroscopic response is captured and compared with the stress-strain data of pure iron (Pohl (2019)). 2. Theory and Methodology In this section, a CP model integrated with a diffused interface is proposed that homogenizes the plastic velocity gradient in the GB region. The quantification of extra hardening in the region of high gradients of strain is achieved with the help of an incompatibility parameter calculated from Nye's dislocation tensor. 1.1. Diffused interface CP model and FEM framework In the CP model, the 2 nd Piola-Kirchhoff stress tensor and the elastic Green-Lagrange strain tensor are related by = ∶ (1) where is the fourth order elasticity tensor and = 1 2 ( − ) (2) is the elastic Green-Lagrange strain tensor and is the elastic deformation gradient. The deformation gradient ( ) is multiplicatively decomposed as (Lee (1969)) = −1 (3) where is the plastic deformation gradient, that is related to the plastic velocity gradient as = ̇ −1 (4)

Ayub Khan et al. / Procedia Structural Integrity 71 (2025) 461–468 463 In the CP model, the evolves with slip on systems, which can be uniquely defined once the crystal structure is known. In this work, we have differentiated the flow rule between the bulk of a grain and at the grain boundary region. A diffused representation is assumed at the grain boundary region where the plastic velocity gradient is homogenised following a mixture rule such that =∑ =1 ∑ =1 ̇ ̃ (5) where is the number of grains that share the GB region in which the material point lies, and is the number of slip systems at the material point. These material point weights are evaluated from element weights (explained later in section 2.4). At a particular point, the sum of weights due to all nearby grains ∑ =1 =1 (Fig.1}). In Eq. 5, ̃ = ⊗ the Schmid tensor and depends on the slip plane normal ( ) and direction ( ). Fig. 1. The variation of weights due to ℎ grain in a bicrystal. While this study focuses on FCC and BCC polycrystals, the framework can be extended to other material systems, such as HCP structures and complex concentrated alloys (CCAs) with appropriate modifications. HCP materials, which exhibit non-basal slip systems, twinning, and pronounced anisotropy, would benefit from the diffused interface approach to capture the gradual transition between parent and twin states, as well as the unique stress and strain distributions near grain boundaries. This approach would also enable a more realistic representation of twinning behavior and dislocation-twin interactions. In CCAs, the diffused interface model could be applied to study grain boundary segregation, phase stability, and the effects of compositional heterogeneity on dislocation behavior, including how local variations in composition influence dislocation motion and the activation of different slip systems. 1.2. Evolution of plastic shear A phenomenological relation for the evolution of plastic shear is used (Asaro and Needleman (1985); Kalidindi et al. (1992)) such that ̇ = 0 ̇ | | 1 ( ) (6) where is the resolved shear stress on a slip system . The evolution of slip system resistance is given by (Anahid et al. (2011)) ̇ =∑ ℎ | ̇ |+ 0 ( ̂ 2 2 ) 2( − 0 ) ∑ | ̇ | (7) where 0 and ̂ are dimensionless material constants, is the magnitude of Burgers vector, is the shear modulus, 0 is the initial slip system resistance. The slip system hardening rate ℎ = ℎ 0 (8) To incorporate the effect of GNDs, the slip plane lattice incompatibility parameter is calculated from = ( : ) 1 2 (9) where is the slip plane normal and Nye’s dislocation tensor is evaluated using the plastic deformation gradient as = ( ) (10)

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1.3. Biased mesh generation from EBSD data A biased meshing technique (Thondiraj et al. (2024)) is used in the simulations to reduce the computational effort. The criteria involves using relatively smaller elements in the region of higher gradients of strain, which are assumed to be near the grain boundaries, and larger elements in the bulk where gradients of strain are assumed to be relatively low. First, a fine regular mesh is constructed, then grain IDs are assigned to all the elements. Based on these element grain Ids, nodal grain Ids are evaluated as, =∑ =1 / , with as the number of elements surrounding the respective node in a finite region controlled by a tolerance ( = ∗ ). is the radius factor (taken as 2) and is the minimum element size after coarsening. Grain weights for each element are calculated as, = / , with as number of elements belonging to a certain grain. Element grain weights quantify the influence of each surrounding grain on the element. Coarsening of an element is done based on the difference in the nodal grain Ids as, | − | of the nodes in a specified region decided by coarsening tolerance . The tolerance decides a minimum region required for the calculation of and and decides the thickness of the GB region. To further make an efficient biased mesh, multi-step coarsening is done that generates very fine elements near GB and very coarse elements in the grain bulk (Fig. 2(a)). We have obtained the EBSD data for polycrystalline pure iron and processed it first using an In-house code developed in MATLAB to generate a uniformly discretized mesh. A cubic RVE is then extracted from it (Fig. 2(b)), which is again 2 step coarsened to obtain a biased mesh to reduce simulation time.

Fig. 2. (a) A three-step coarsening of the uniformly discretised mesh using the biased mesh generation technique. (b) A 13 grain polycrystalline RVE of pure iron extracted from the EBSD data. The biased mesh is generated by two-step coarsening of elements. 1.4. Computational procedure A fully-implicit time-integration scheme (Kalidindi et al. (1992)) has been implemented in our In-house FE solver to simulate the micromechanical response of polycrystals. At the start of an iteration, it is assumed that ( ), ( ), ( ), ( ), ( ) at the previous time step ′ ′ are known. Using Eqs. 4, 5, and 6, ( ) can be obtained as ( ) = { + ∑ =1 ∑ =1 ( ( ), ( )) ̃ } ( ) (11) We have used a two-level iterative procedure for the convergence of stress and slip system resistance . In the First Level , the stress at the end of a time step is computed by substituting Eqs. 2 & 3 in Eq. 1 as ( ) = [(1/2){ − ( ) ( ) ( ) − 1 ( )}] (12) Using Eq. 11, Eq. 12 is further reduced to ( ) = −∑ =1 ∑ =1 ( ( ), ( )) (13) where = [(1/2){ − }] = [(1/2) ] = ̃ + ̃

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= − ( ) ( ) ( ) − 1 ( ) The convergence of stress is achieved using a Newton-type algorithm as +1 ( ) = ( ) − − 1 [ ]

(14)

(15)

where = ( ) − ( )+∑ = ⊗ + ∑ =1 ∑ =1

=1 ∑

=1 ⊗ ( )

(16) Once the stress is converged, it is evaluated explicitly in the Second Level . Since the Nye's dislocation tensor is a non-local quantity, it is evaluated by a two-step procedure, similar to that used by Roters and Raabe (2006). In the first step, the integration point values of plastic deformation gradient ( ) are extrapolated to the nodes using the shape functions of the isoparametric element. Taking into account the contribution of all the elements attached to a particular node, the extrapolated ( ) values are then averaged at that node. In the second step, the averaged nodal ( ) values are again interpolated to the Gauss integration points using the shape functions by ( ( )) = ∑ =1 ( ( )) (17) The Nye's dislocation tensor is then evaluated as ( ) = ∑ =1 ( )( ( )) (18) Using Eqs. 6, 7, 9, and 18, is evaluated as ( ) = ( )+∑ ℎ | ( ( ), ( ))| + 0 ( ̂ 2 2 ) 2( ( ) − 0 ) ∑ ( ) | ( ( ), ( ))| (19) For the convergence of stress in the First Level , a tolerance check is put on all components of (Eq.16(a)). In the Second Level , a tolerance is defined to check the value of =( ( )− 2 ( ) ) . If it fails the tolerance check, the iteration restarts at First Level with a lower time increment. 3. Results and discussion We have considered 4 different cases to demonstrate the performance of the diffused interface size dependent CPFEM model. In the first case, a bicrystal is considered and simulated with a stepped and diffused interface. The effect of GNDs is not considered. In the second case, a bicrystal with the diffused interface and GNDs is considered to quantify the additional effect on strain hardening. In the third case, the cubic RVE shown in Fig. 2(b) is simulated as a BCC polycrystal, with the `SSD only' model, to calibrate the simulation input parameters by comparing its macroscopic response with that of pure Iron. In the fourth case, the same polycrystal mesh is considered with diffused grain boundaries and GNDs. In all the simulations, roller supports are used on the bottom, left, and back faces, and the top face is pulled upwards with a constant displacement rate. 1.5. Case I: Bicrystal with Stepped and Diffused Interface In this case, hardening is considered due to SSDs only. The RVE is simulated as FCC crystals with properties given in Table 1. A comparison between the results obtained from stepped and diffused interface (Fig. 3(b)) shows that the diffused interface model can reduce the stress concentrations near the GB. Also, the response in the bulk almost remains unchanged when a diffused representation is used. In the absence of GNDs, the macroscopic response obtained from both models is the same (Fig. 3(c)).

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Fig. 3. Comparison of results obtained for sharp and diffused interface. (a) & (b) are Line Plots on section z=0.5, and (c) macroscopic response.

Table 1. Properties used in the CP model.

1 2 3

100.17 GPa 47.14 GPa 26.515 GPa 0.001 −1 0.02 180 MPa

Reference slip rate, ̇ 0 Strain rate sensitivity, Initial hardening rate, ℎ 0 Ratio self/latent hardening, Initial slip resistance, 0

1.4

16 MPa

1.6. Case II : Bicrystal with Diffused Interface In this case, simulations are performed using the diffused interface model on a bicrystal RVE, and the additional hardening effect because of GNDs is studied. The results from the ‘SSD only’ model are compared with the ‘SSD and GND model’. The line plot (Fig. 4(b)) and the macroscopic response (Fig. 4(c)) show the hardening with the addition of GNDs in the model. The hardening effect is observed to be stronger in the GB region, where the strain gradients are higher.

Fig. 4. Diffused interface with and without GNDs. (a) & (b) are Line Plots on section z=0.5, and (c) macroscopic response.

1.7. Case III: Polycrystal (EBSD data) mesh (Without GND) In this case, a subdomain is taken for analysis from the EBSD data obtained for polycrystalline pure iron (Fig. 2(b)). A 22226 elements biased mesh is then generated for the subdomain by a 2 step coarsening procedure, and simulations are performed on the RVE with the ‘SSD only’ model. The stress-strain data obtained from uniaxial tensile tests of pure iron (Pohl (2019)) were employed to calibrate the parameters of the CPFE model. This calibration involved matching the macroscopic response of the simulated RVE to the experimental stress-strain data (Fig. 5). The

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comparison was confined to the elasto-plastic regime, encompassing both yielding and strain hardening, but excluding damage accumulation. The calibrated simulation input parameters are given in Table 2.

Fig. 5. Macroscopic response of Polycrystalline RVE compared with stress-strain curve of Pure Iron. Table 2. Simulation input properties calibrated from pure iron. 1 327.6 GPa 2 133.83 GPa 3 48.45 GPa Reference slip rate, 0 ̇ 0.008 −1 Strain rate sensitivity, 0.058 Initial hardening rate, ℎ 0 150 MPa Ratio self/latent hardening, 1.4 Initial slip resistance, 0 80 MPa Dimensionless constants (GND), 0 & ̂ 3 & 5

1.8. Case IV :Polycrystal (EBSD data) mesh, Effect of GNDs In this case, simulations are performed on the polycrystalline RVE shown in Fig. 2(b), with the simulation input parameters generated from Case III. As the material accumulates more plastic strain, the hardening near the GB is observed to be relatively hig her in the ‘SSD and GND’ model. This is clear in the local stress profile (Fig. 6(a)), which clearly shows more hardening due to the addition of GNDs. The difference in magnitudes of the stresses obtained from both the models (with and without GND) is observed to be more near the GB, which means that hardening near the GB, which is the region of higher strain gradients, is accurately captured.

Fig. 6. Effect of GNDs on polycrystal RVE. (a) Line plot on section z=0.5, and (b) Macroscopic response.

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4. Summary This study presents an advanced 3D modeling approach to explore the plastic deformation behavior of polycrystalline materials using a CPFE framework. The framework is enhanced by incorporating a diffused interface model and a biased meshing technique, which together improve the accuracy and efficiency of the simulations. The study successfully captures the grain size effect and reveals significant strain hardening near grain boundaries (GBs), which is driven by large gradients in plastic strain, modeled effectively with the addition of GNDs. The diffused interface model plays a crucial role in ensuring a smooth transition of mechanical properties near GBs, thereby suppressing artificial stress concentrations that could otherwise skew the results. Furthermore, the study introduces a non-local criterion to quantify plastic incompatibility near GB, which enhances the model’s ability to analyze complex microstructural behavior with greater precision. The local stress profiles generated through simulations offer valuable insights into the intricate phenomena occurring within the GB regions, contributing to a better understanding of the material's behavior under deformation. The macroscopic response obtained from simulations performed on a polycrystalline RVE generated from EBSD data, under specified boundary conditions, closely mimics the uniaxial deformation of pure iron. The integration of EBSD data into the CPFE model, coupled with the use of biased meshing and a diffused interface, results in a computationally efficient framework that provides a more realistic and robust platform for analyzing polycrystals. This framework not only advances the understanding of plastic deformation in polycrystalline materials but also holds potential for application in material design and optimization. Acknowledgements This work is sponsored by Hydro-Quebec, Canada. The authors acknowledge the sponsors for funding this effort under the strategic project Modelisation Micromecanique des Aciers (MoMA) J-8587. References Acharya, A., Beaudoin, A., 2000. Grain-size effect in viscoplastic polycrystals at moderate strains. Journal of the Mechanics and Physics of Solids 48(10), 2213 – 2230. Anahid, M., Samal, M. K., Ghosh, S., 2011. Dwell fatigue crack nucleation model based on crystal plasticity finite element simulations of polycrystalline titanium alloys. Journal of the Mechanics and Physics of Solids 59(10), 2157 – 2176. Asaro, R. J., Rice, J., 1977. Strain localization in ductile single crystals. Journal of the Mechanics and Physics of Solids 25(5), 309 – 338. Asaro, R. J., Needleman, A., 1985. Texture development and strain hardening in rate dependent polycrystals. Acta Metallurgica 33, 923 – 953. Cermelli, P., Gurtin, M. E., 2001. On the characterization of geometrically necessary dislocations in finite plasticity. Journal of the Mechanics and Physics of Solids 49(7), 1539 – 1568. Dai, H., Parks, D. M., 1997. Geometrically-necessary dislocation density and scale- dependent crystal plasticity. In: Proceedings of Plasticity ’97, Neat Press, pp. 17 – 18. Kalidindi, S. R., Bronkhorst, C. A., Anand, L., 1992. Crystallographic texture evolution in bulk deformation processing of FCC metals. Journal of the Mechanics and Physics of Solids 40(3), 537 – 569. Kamaya, M., 2004. Influence of grain boundaries on short crack growth behaviour of IGSCC. Fatigue & Fracture of Engineering Materials & Structures 27, 513 – 521. Lee, E., 1969. Elastic-plastic deformation at finite strains. Journal of Applied Mechanics 36, 1 – 6. Ma, A., Roters, F., Raabe, D., 2006. A dislocation density based constitutive model for crystal plasticity FEM including geometrically necessary dislocations. Acta Materialia 54(8), 2169 – 2179. Nye, J. F., 1953. Some geometrical relations in dislocated crystals. Acta Metallurgica 1(2), 153 – 162. Pohl, F., 2019. Pop-in behavior and elastic-to-plastic transition of polycrystalline pure iron during sharp nanoindentation. Scientific Reports 9(1), 15350. Schroeter, B. M., McDowell, D. L., 2003. Measurement of deformation fields in polycrystalline OFHC copper. International Journal of Plasticity 19, 1355 – 1376. Thondiraj, J. M., Paranikumar, A., Tiwari, D., Paquet, D., Chakraborty, P., 2024. Diffused interface crystal plasticity finite element method: biased mesh generation and accuracy. Finite Elements in Analysis and Design 228, 104051.

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Procedia Structural Integrity 71 (2025) 388–394

5 th International Structural Integrity Conference & Exhibition (SICE 2024)

Keywords: Drones; Additive Manufacturing; Disaster Response; Rapid Production; Payload Delivery. 1. Introduction In recent years, Unmanned Aerial Vehicles (UAV’s) have emerged as transformative technology to change the traditional disaster response. This paper revolves around design and deployment of drones manufactured using additive manufacturing techniques (AM). This method allows for the rapid production, lightweight and complex geometries making it an ideal choice for disaster response. This paper contributes to the research field of UAV technology by integrating additive manufacturing to it. This eventually increases the efficiency, reliability, and quick response to the In the current scenario, timely delivery of medical supplies and food during a disaster is critical for saving people’s lives. This paper explores use of drones in such calamities using additive manufacturing and deployments of those drones during such situations. This study emphasizes advantages of drones using additive manufacturing as they are lightweight and allow rapid production along with customization based on mission requirements. This study also focuses on drone’s design, various analysis, material selection and payload deployment mechanism. Various tests have been performed ensuring the drone’s capability to navigate through various regions, also focusing on timely and precise delivery of items. This paper highlights the research on the application of drones and integrating additive manufacturing in rapid response conditions. This study shows that usage of such drones reduces the time required to deliver medical supplies, thus saving lives during hazardous situations. The drone is also equipped with an engineered payload mechanism ensuring safety and unhindered delivery of supplies. It is manufactured using light materials ensuring swift flight of the drone. This mechanism ensures a quick release system. © 2025 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of SICE 2024 organizers Additively Manufactured UAV for Disaster Relief and First Aid Prathamesh Patil * , Athul Krishna T.B., Manas Mali, Tanmay Sarnobat, Arsalan Darvesh Afzal Ansari Department of Mechanical Engineering, “Agnel Charities” Fr. C. Rodrigues Institute of Technology, Vashi 400703, Navi Mumbai, Maharashtra, India Abstract

∗ Corresponding author. Tel.: +91-8104505770; E-mail address: prathameshpatil2410@gmail.com

Tel.: +91-9987617928; E-mail address: afzal.ansari@fcrit.ac.in

2452-3216 © 2025 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of SICE 2024 organizers 10.1016/j.prostr.2025.08.052

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disasters. Structural analysis was performed so that the drone could withstand any type of mechanical stresses encountered during operation. As it was expected for the drone to carry supplies over a large distance, it is necessary that the design must be light weight as well as robust. Modal and harmonic analysis was also performed in order to support the design. This was performed to study the natural frequencies and their corresponding deformations. Harmonic analysis was performed to protect the electronic components from damaging, as the structural vibrations get transferred to them during the flight. Computational Fluid Dynamics (CFD) helps to study the airflow around the drone during the flight, eventually helping in the design. It helped in designing a drone which can withstand various conditions and minimizing the time required for testing and making it cost-effective. The drone is equipped with a payload box which is engineered in a way such that it ensures the safe and efficient delivery of supplies. This mechanism is made up of light weight material which does not affect the dynamics of the drone’s flight. The box keeps the item safe and secure. Development of such drones represent the advancement in UAV technology.

2. Airframe and Material Selection

The UAV was designed with constraints. The individual length, width and height of the frame does not exceed 1.2 m. Also, the total weight under which the UAV was designed was 2 kg. Despite various configurations available, the team decided to go for X-Configuration drone due to more stability, centralized centre of gravity, improved efficiency, and enhanced safety. This configuration ensures the mounting of critical components in the frame making it easy. Also, it contributes in achieving a consistent thrust to weight ratio. While designing and fabricating a multicopter frame, material selection plays a crucial role, which ensures the high strength, lightweight, durability, overall performance, and a highly efficient frame. In order to maintain the strength, accessibility of parts and cost effectiveness of the frame, materials mentioned below were selected. Material selection was executed by comparing their mechanical properties such as density, strength, and modulus of elasticity.

Table 1. Material selection

Material

Tensile Strength

Density

Ease of Manufacturing

Salient Features

PLA 60-70 MPa

1240 kg / m 3

Easy (3D printing)

Biodegradable, easy to print, lower impact resistance compared to ABS, Good vibration absorption quality Extremely lightweight, high strength to weight ratio, high stiffness, low thermal expansion

Carbon Fiber

500-600 MPa 1600 kg / m 3

Moderate (Molding)

Aero ply 30-50 MPa Lightweight polywood, good strength to weight ratio. Table 1 shows different materials available for fabrication of the drone. Suitable material was selected for the fabrication. Material such as PLA was useful for the parts that could be made by additive manufacturing, using FDM 3D printing technology. Similarly, carbon fibre and aeroply material were used for their high strength and lightweight features. Some additional parts were used such as Velcro straps and landing leg grips for better ground friction. 2.1 Additive Manufacturing Process 600 kg / m 3 Moderate (Cutting, Laser cutting)

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The main technique used to produce the PLA parts of the UAV was Fused Deposition Modelling (FDM). This method allows for the creation of intricate designs, material efficiency, and quick production. To strike a balance between strength and weight, every PLA component was made using a 0.4 mm nozzle, a layer height of 0.2 mm and 30% infill density. To reduce delamination and improve structural integrity under operational loads, the print orientation was

adjusted for each part. 3. Drone Analysis

The analysis of drones plays a crucial role in validating the drone’s capability. This analysis encompasses various analyses including static structural, aerodynamic efficiency and vibrations. Each part of the frame has been analysed through simulations to find out the scope of improvement and to optimize it. The table below shows the boundary conditions applied in order to perform the analysis.

Table 2. Constraints for analysis

Thrust (N) Torque (N mm)

Battery Weight (N)

Pixhawk Weight (N)

GPS Weight (N)

Gravitational

Acceleration

m / s 2

12.16 9.81 As mentioned in Table 2, the constraints were applied to the drone frame and following results were obtained. 3.1 Transient Structural Analysis In ANSYS, a transient analysis was carried out with a time interval of 0.01 second and a total simulation period of 2 seconds. Simulated take-off and landing sequences were among the loading conditions that included a time-varying thrust profile. In order to understand the dynamic response to time dependent forces, transient analysis was performed. This analysis revolves around the drone’s behaviour during take -off, landing and performing flight manoeuvres. By simulating this we can analyse the points where the stress and deformation might occur, eventually leading to optimization of the design accordingly. 181.23 3.188 0.56 0.314

Fig.1. (a) Stress; (b) Strain

The above images show the stress and the total deformation of the frame. The maximum indices stress was found to be 1.1014 MPa and maximum deformation was 0.1682 mm.

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Fig.2. (a) Deformation Vs Time; (b) Stress Vs Time

Thus, transient structural analysis was performed successfully. The result of transient analysis confirms the robustness of the design and drone’s capability to handle such stresses with variations. The drone stays stable and responsive.

3.2 Modal Analysis

To assess the fundamental vibration behaviour of the bare frame, the modal analysis was performed using free free boundary conditions. With no restrictions that might skew the data, this method makes it possible to identify natural frequencies and related mode shapes. This analysis is only a preliminary baseline. The UAVs mass distribution and dynamic features will alter .

Fig.3. Frequency Vs Deformation

Modal analysis was performed successfully on the frame. Fig. 3 depicts the frequencies and their respective deformations. The frequencies found were under safer limits with their respective deformations. Hence it was concluded that the frame is safe.

3.3 Computational Fluid Dynamics

Computational Fluid Dynamics (CFDs) was performed on the design frame to validate the airflow and check the efficiency of the UAV design. Airflow and streamline were evaluated based on the results obtained. The CFD was performed using Ansys Fluent. SST K-omega model was chosen for the simulation. The analysis was performed for propeller speed of 2000 rpm.

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Fig.4. (a) Velocity Streamline; (b) Velocity Contour

4. Final Design The final design comprises a fully shielded drone frame including propeller ducts along with a payload release box using rocker slider mechanism suitable for payload capacity. The main body is designed in such a way that the components are integrated inside the frame and are smartly placed for maintaining proper wire management and ease of assembly. Carbon fiber rods are used as drone arms as well as a covering for motor ESC placed inside the drone arms to enhance the ergonomics and aesthetic features of the drone. Similarly, the top plate is designed in such a way that it integrates the Here3 GPS module inside the top plate further providing stability and protection to it.

Fig. 5. Fabricated UAV

Fig. 6. UAV during flight

5. Payload Mechanism The payload box was crafted with lightweight material of aero ply keeping in mind the flight of the drone. This mechanism provides both ease of operation and durability. To enable automatic payload release, an aero ply box was chosen for its lightweight, sturdy, and structurally stable properties. Sized at 70 mm x 70 mm with 2 mm thickness on all sides, it incorporates a slider-crank mechanism actuated by a single 30 g servo. The servo is controlled by the flight controller which operates the opening and closing of the box. A press fit action will secure the payload box to the lower plate of the drone frame. A change in the UAV’s center of gravity might cause brief instability when the payload is released due to abrupt drop in weight. In order to counter this, the onboard flight controller uses a closed loop PID controller that adjusts the motor speed using IMU feedback. With this compensation mechanism, the drone can stabilize itself after it is released. Test flights demonstrated that the UAV’s attitude is promptly corrected, maintaining its flight integrity and control precision.

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Fig. 7. (a) Design of Payload Box; (b) Working of Payload Mechanism

6. Innovation

The innovation in duct design shows the advancement in generating more thrust and thus increasing the aerodynamic performance of the drone. This geometry includes a shield around the propeller which works on Bernoulli’s principle and thus increases the thrust of the propeller. It also acts as a shroud around the blade providing safety while in flight or in case of accident. In addition to that, the duct is also manufactured additively with a minimal thickness so that it does not increase weight of the UAV.

6.1 Duct Analysis

Computational Fluid Dynamics (CFD) helps in optimizing the aerodynamic efficiency of the frame. CFD simulations were performed and various parameters such as velocity distribution, pressure gradients and turbulence were studied. Initially a single propeller was analyzed to find out the thrust generated. Based on the results of the first simulation, the second CFD was performed with a ducted propeller to enhance the thrust. Table 3 shows the boundary conditions applied to the propeller while performing the analysis. Table 3. Boundary conditions in ANSYS Sr. No. Name of the Boundary Condition Value 1. Velocity (inlet) 17 m/sec 2. Density of air 1.225 kg/m 3 3. Rotational speed 20000 rpm 4. Pressure (outlet) 1 atm

Fig.8. (a) Ductless Propeller; (b) Propeller with duct

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Table 4. Thrust generation of CFD

Ductless Propeller

Ducted Propeller

Percentage Increase

13.8451 N 29.4% As we can conclude from the results shown in Table 4, the ducted propeller showed higher thrust as compared to the single ductless propeller. The increase in thrust was found to be 29.4%. 7. Conclusion This paper represents design, development, and analysis of the drone which is optimized for the delivery of first aid kit and medical supplies during natural calamities. The structural analysis confirms that the drone can withstand the stress developed under the given payload and manoeuvres, maintaining lightweight and reliable design. Modal and harmonic analysis ensures that no resonance occurs during the flight. The integration of additive manufacturing in drone manufacturing paves a way for research in UAV technology Thus, the drone was successfully manufactured using additive manufacturing techniques resulting in rapid customization and production. 19.6073 N

Acknowledgements

The authors express gratitude to Prof. Afzal Ansari, Assistant Professor, FCRIT, Vashi, for his guidance and encouragement and constructive criticism on the project. Authors also thank faculty of Fr. C. Rodrigues Institute of Technology, Vashi, and in particular Dr. S. M. Khot and Dr. Aqleem Siddiqui for providing support.

References

Chatterjee, A. and B. L. Deopura, 2002. Carbon nanotubes and nanofiber: an overview. Fibers and Polymers 3; 134-139. Farah, Shady, Daniel G. Anderson, and Robert Langer, 2016. Physical and mechanical properties of PLA, and their functions in widespread applications — A comprehensive review. Advanced drug delivery reviews 107; 367-392. J. Tang et al., 2021. Optimizing UAV design for disaster response through additive manufacturing and multi material use. Journal of Manufacturing Processes 68; 308 – 317. Kumar, S., M, R. Janaki, S. Eswar, P. Kiran, and V. Raja., 2021. Structural optimization of frame of the multi rotor unmanned aerial vehicle through computational structural analysis. Journal of Physics Conference Series 1849; 1 – 14

Available online at www.sciencedirect.com

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Procedia Structural Integrity 71 (2025) 430–437

1. Introduction One of the most crucial industrial processes is casting process, which is also a cost-effective method to create products from simple to complex. High energy consumption, long melting time and high number of defects are some of the process limitations. The industry is trying to develop new systems to eliminate such deficiencies. Microwave aided casting is a new technology that can be used in the current industry. In case of microwave heating, the material absorbs electromagnetic energy and later converted to heat through one of the following mechanisms: 1) dipole rotation and collision, 2) ionic current and resistance. In microwave heating, the object generate heat on its own. Hence, no heat conduction and rapid heating is possible. This rapid heating saves manufacturing time in foundry industry Kim et al. (2012) and Thostenson and Chou (1999).With comparatively low power, very high temperatures can be achieved. For this it is essential to control the form of microwave heating and so proper thermal insulations are used. Experimental results have shown that microwave casting is prone to less defects compared to conventional casting Mishra and Sharma (2016). Microwaves are electromagnetic radiations consisting of magnetic and electric fields. The frequency range is 0.3 GHz -300 GHz. For processing of materials, Quality of castings is important for the foundry industries, which depends upon the defects present in the castings. More the proportion of defects, less is their quality. It is always intended to produce defect free castings and in spite of taking lot of precautions, defects arise in casting. Porosity, shrinkage, mismatch, pinholes, blowholes are its some of the common defects. In the present study, microwave aided casting technique was used to produce ASTM B23 tin alloy cylindrical castings. Defects such as solid shrinkage and porosity fraction were considered for the study. It was found that, in microwave castings, the solid shrinkage was 32.88 % less to those produced through conventional casting. Porosity fraction was calculated by using Four Dimensional X-ray microscope. The results revealed that porosity was 78 % less in microwave specimens. © 2025 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of SICE 2024 organizers *Corresponding author: sameersgajmal@gmail.com Keywords: Casting; Casting defects; Microwave aided casting; Porosity; Solid shrinkage; 5 th International Structural Integrity Conference & Exhibition (SICE 2024) Analysis of Porosity and Solid Shrinkage of ASTM B23 Babbitt alloy in Microwave aided Castings Sameer S. Gajmal a, *, Dadarao N. Raut b , T.V.K. Gupta c Sudhir G. Bhatwadekar d a Mechanical Engineering, Gharda Institute of Technology (GIT), Khed, Maharashtra 415708, India b Department of Production Engineering, Veermata Jijabai Technological Institute (VJTI), Mumbai, Maharashtra 400019, India c Department of Mechanical Engineering, Visvesvaraya National Institute of Technology (VNIT), Nagpur, Maharashtra 440010, India d Formerly, Mechanical Engineering, Sanjay Ghodawat Group of Institutions, Kolhapur, Maharashtra 416118, India Abstract

2452-3216 © 2025 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of SICE 2024 organizers 10.1016/j.prostr.2025.08.058