Graphene has been one of the most researched material in the world for the past two decades due to its unique combination of mechanical, thermal and electrical properties. Graphene exists in a stable two dimensional (2D) structure with hexagonal carbon rings. This special 2D structure of graphene enables it to exhibit a wide range of peculiar material properties like high Young’s modulus, high specific strength, and electrical conductivity etc. However, it is extremely challenging and costly to investigate graphene solely based on experimental tests. Atomistic simulations are powerful computational techniques for characterizing materials at small length and time scales with a fraction of cost relative to experimental testing. High fidelity atomistic simulations like Density Functional Theory (DFT) simulations, and ab initio molecular dynamic simulations have higher accuracy in predicting 2D material properties but are computationally expensive. Classic molecular dynamics (MD) simulations adopt empirical interatomic potentials which drastically reduce the computational time but has lower simulation accuracy. To bridge the gap between these two type of simulation techniques, a new artificial neural network potential is developed, for graphene in this study, to enable the characterization of 2D materials using classic MD simulations with a comparable accuracy of first principles simulation. This is expected to accelerate the discovery and design of novel graphene based functional materials. In the present study mechanical and thermal properties of graphene are investigated using the machine learning potentials by conducting MD simulations. To validate the accuracy of machine learning potentials mechanical properties such as Young’s modulus, ultimate tensile strength and thermal properties such as coefficient of thermal expansion and lattice parameter are evaluated for graphene and compared with existing literature.
{"title":"Machine Learning Potentials for Graphene","authors":"Akash Singh, Yumeng Li","doi":"10.1115/imece2022-95341","DOIUrl":"https://doi.org/10.1115/imece2022-95341","url":null,"abstract":"\u0000 Graphene has been one of the most researched material in the world for the past two decades due to its unique combination of mechanical, thermal and electrical properties. Graphene exists in a stable two dimensional (2D) structure with hexagonal carbon rings. This special 2D structure of graphene enables it to exhibit a wide range of peculiar material properties like high Young’s modulus, high specific strength, and electrical conductivity etc. However, it is extremely challenging and costly to investigate graphene solely based on experimental tests. Atomistic simulations are powerful computational techniques for characterizing materials at small length and time scales with a fraction of cost relative to experimental testing. High fidelity atomistic simulations like Density Functional Theory (DFT) simulations, and ab initio molecular dynamic simulations have higher accuracy in predicting 2D material properties but are computationally expensive. Classic molecular dynamics (MD) simulations adopt empirical interatomic potentials which drastically reduce the computational time but has lower simulation accuracy. To bridge the gap between these two type of simulation techniques, a new artificial neural network potential is developed, for graphene in this study, to enable the characterization of 2D materials using classic MD simulations with a comparable accuracy of first principles simulation. This is expected to accelerate the discovery and design of novel graphene based functional materials. In the present study mechanical and thermal properties of graphene are investigated using the machine learning potentials by conducting MD simulations. To validate the accuracy of machine learning potentials mechanical properties such as Young’s modulus, ultimate tensile strength and thermal properties such as coefficient of thermal expansion and lattice parameter are evaluated for graphene and compared with existing literature.","PeriodicalId":146276,"journal":{"name":"Volume 3: Advanced Materials: Design, Processing, Characterization and Applications; Advances in Aerospace Technology","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124762696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The performance of the satellite not only relies on environmental factors but also is impacted by internal disturbances. The influential factors complicate the design of accurate controllers for attitude adjustments. The proposed research addresses this control problem by introducing a Brain Emotional Learning Based Intelligent Controller (BELBIC) tuned by a fuzzy inference system. Here, the learning weights and the gain inputs of the BELBIC are adjusted using a fuzzy inference system. In contrast, the initial parameters of the fuzzy inference system are adapted through the whale optimization algorithm. We validate and evaluate the performance of the proposed intelligent controller utilizing simulation studies. The results demonstrate the applicability and satisfactory performance of the proposed controller compared to the PID-BELBIC.
{"title":"A Novel Fuzzy-BELBIC Structure for the Adaptive Control of Satellite Attitude","authors":"Kosar Safari, Farhad Imani","doi":"10.1115/imece2022-96034","DOIUrl":"https://doi.org/10.1115/imece2022-96034","url":null,"abstract":"\u0000 The performance of the satellite not only relies on environmental factors but also is impacted by internal disturbances. The influential factors complicate the design of accurate controllers for attitude adjustments. The proposed research addresses this control problem by introducing a Brain Emotional Learning Based Intelligent Controller (BELBIC) tuned by a fuzzy inference system. Here, the learning weights and the gain inputs of the BELBIC are adjusted using a fuzzy inference system. In contrast, the initial parameters of the fuzzy inference system are adapted through the whale optimization algorithm. We validate and evaluate the performance of the proposed intelligent controller utilizing simulation studies. The results demonstrate the applicability and satisfactory performance of the proposed controller compared to the PID-BELBIC.","PeriodicalId":146276,"journal":{"name":"Volume 3: Advanced Materials: Design, Processing, Characterization and Applications; Advances in Aerospace Technology","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126045545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
It is well known that cellular materials (including porous materials) are widely observed in engineered and nature systems, because their mechanical performance is excellent, such as compressive deformation and energy absorption against impact loading. The mechanical response is significantly dependent on their inherent cellular structure, i.e., geometric arrangement pattern. A nonuniform arrangement could provide a significant variation of mechanical performance, and then material selection and geometrical designs are challenge. This study established machine-learning (ML) based framework to design geometrical arrangement (architecture) in cellular material to achieve better mechanical performance against uniaxial compression. Especially, we investigated peak force at plateau region and work of energy absorption until structural densification. Cellular material having various pattern of internal geometry was modeled using finite element method (FEM), and we simulated uniaxial deformation behavior, which was used as training data (teaching data) for machine learning method. This study employed neural network (NN) for machine learning method, which connects cellular geometric pattern with mechanical performance (force - displacement curve and peak force - work of energy absorption relationship). Our results showed that the proposed framework is capable of predicting the mechanical response of any given geometric pattern within the domain of our setting. Thus, it is useful to discover cellular structure in order to achieve desired mechanical response.
{"title":"Accelerated Structural Design of Cellular Materials for Compressive Deformation Using a Machine-Learning","authors":"Jin-gui Song, Aoi Takagi, Genki Mitsuhashi, Kohei Saito, Kazuma Ogata, Takeru Miyagawa, A. Yonezu","doi":"10.1115/imece2022-95522","DOIUrl":"https://doi.org/10.1115/imece2022-95522","url":null,"abstract":"\u0000 It is well known that cellular materials (including porous materials) are widely observed in engineered and nature systems, because their mechanical performance is excellent, such as compressive deformation and energy absorption against impact loading. The mechanical response is significantly dependent on their inherent cellular structure, i.e., geometric arrangement pattern. A nonuniform arrangement could provide a significant variation of mechanical performance, and then material selection and geometrical designs are challenge. This study established machine-learning (ML) based framework to design geometrical arrangement (architecture) in cellular material to achieve better mechanical performance against uniaxial compression. Especially, we investigated peak force at plateau region and work of energy absorption until structural densification. Cellular material having various pattern of internal geometry was modeled using finite element method (FEM), and we simulated uniaxial deformation behavior, which was used as training data (teaching data) for machine learning method. This study employed neural network (NN) for machine learning method, which connects cellular geometric pattern with mechanical performance (force - displacement curve and peak force - work of energy absorption relationship). Our results showed that the proposed framework is capable of predicting the mechanical response of any given geometric pattern within the domain of our setting. Thus, it is useful to discover cellular structure in order to achieve desired mechanical response.","PeriodicalId":146276,"journal":{"name":"Volume 3: Advanced Materials: Design, Processing, Characterization and Applications; Advances in Aerospace Technology","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116677752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In recent times, interest in the fabrication of porous NiTi structures have grown significantly. Porous structures have remarkable potential to be used in the areas of tissue engineering, impact absorption, and fluid permeability. However, fabrication of NiTi structures poses challenges such as poor machinability, high work hardening, and inherent springback effects, which render them difficult to tackle through conventional manufacturing routes. Additive manufacturing (AM) can alleviate the aforementioned issues associated with NiTi shape memory alloys (SMAs). In addition, this technology can be employed for producing metallic scaffolds and porous structures of complex architectural details. Recently, a class of minimal surface topologies, known as triply periodic minimal surface (TPMS) structures has emerged as an attractive configuration for building architected constructs. Very little work can be found in the literature addressing the fabrication of NiTi TPMS structures and investigating their behaviors. The complex geometries of these structures may influence the dynamics of the melt pool in beam-based AM processes as well as the solidification rate within different regions of a product, thereby affecting the microstructures of fabricated parts. An inhomogeneity in microstructures of fabricated parts was observed, which motivated a detailed examination of these structures. The novelty of the present work lies in studying the influence of geometries of NiTi TPMS lattices along with laser process parameters.
{"title":"Inhomogeneous Microstructure due to Non-Uniform Solidification Rate in NiTi Triply Periodic Minimal Surface (TPMS) Structures Fabricated via Laser Powder Bed Fusion","authors":"Shahadat Hussain, Alireza Alagha, W. Zaki","doi":"10.1115/imece2022-95320","DOIUrl":"https://doi.org/10.1115/imece2022-95320","url":null,"abstract":"\u0000 In recent times, interest in the fabrication of porous NiTi structures have grown significantly. Porous structures have remarkable potential to be used in the areas of tissue engineering, impact absorption, and fluid permeability. However, fabrication of NiTi structures poses challenges such as poor machinability, high work hardening, and inherent springback effects, which render them difficult to tackle through conventional manufacturing routes. Additive manufacturing (AM) can alleviate the aforementioned issues associated with NiTi shape memory alloys (SMAs). In addition, this technology can be employed for producing metallic scaffolds and porous structures of complex architectural details. Recently, a class of minimal surface topologies, known as triply periodic minimal surface (TPMS) structures has emerged as an attractive configuration for building architected constructs. Very little work can be found in the literature addressing the fabrication of NiTi TPMS structures and investigating their behaviors. The complex geometries of these structures may influence the dynamics of the melt pool in beam-based AM processes as well as the solidification rate within different regions of a product, thereby affecting the microstructures of fabricated parts. An inhomogeneity in microstructures of fabricated parts was observed, which motivated a detailed examination of these structures. The novelty of the present work lies in studying the influence of geometries of NiTi TPMS lattices along with laser process parameters.","PeriodicalId":146276,"journal":{"name":"Volume 3: Advanced Materials: Design, Processing, Characterization and Applications; Advances in Aerospace Technology","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128058122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ashok Kumar Sivarathri, Amit Shukla, Ayush Gupta, Amit Kumar
UAV-AGV heterogeneous multi-agent robotic system has drawn the attention of researchers to explore its capabilities in different perspectives. The UAV-AGV can concatenate their individual capabilities to overcome the drawbacks of each. UAV will benefit in payload and AGV will have the navigation guidance due to the presence of UAV. Collaborative kinematics between both agents is basic requirement of the system. Vision-based method is one of the techniques to implement collaborative motion. A high-level sliding mode controller is developed and validated for the vision-based navigation of UAV for reaching the target/AGV. Gazebo simulations are performed for trajectory tracking in the image frame to reach the target by the UAV. UAV autonomously detects the target and plans the trajectory to reach it. Apparent size-based depth controller is developed for the UAV and simulated in the Gazebo. Altitude trajectory tracking is implemented for the UAV using sliding model controller. Sliding mode based high-level controllers are performing well for the navigation of UAV and trajectory tracking in the image frame opens a different approach for the reaching of AGV by UAV. A non-linear depth controller is developed and simulated in Gazebo which can be useful for the landing task of UAV over AGV.
{"title":"Trajectory Tracking in the Image Frame for Autonomous Navigation of UAV in UAV-AGV Multi-Agent System","authors":"Ashok Kumar Sivarathri, Amit Shukla, Ayush Gupta, Amit Kumar","doi":"10.1115/imece2022-95750","DOIUrl":"https://doi.org/10.1115/imece2022-95750","url":null,"abstract":"\u0000 UAV-AGV heterogeneous multi-agent robotic system has drawn the attention of researchers to explore its capabilities in different perspectives. The UAV-AGV can concatenate their individual capabilities to overcome the drawbacks of each. UAV will benefit in payload and AGV will have the navigation guidance due to the presence of UAV. Collaborative kinematics between both agents is basic requirement of the system. Vision-based method is one of the techniques to implement collaborative motion. A high-level sliding mode controller is developed and validated for the vision-based navigation of UAV for reaching the target/AGV. Gazebo simulations are performed for trajectory tracking in the image frame to reach the target by the UAV. UAV autonomously detects the target and plans the trajectory to reach it. Apparent size-based depth controller is developed for the UAV and simulated in the Gazebo. Altitude trajectory tracking is implemented for the UAV using sliding model controller. Sliding mode based high-level controllers are performing well for the navigation of UAV and trajectory tracking in the image frame opens a different approach for the reaching of AGV by UAV. A non-linear depth controller is developed and simulated in Gazebo which can be useful for the landing task of UAV over AGV.","PeriodicalId":146276,"journal":{"name":"Volume 3: Advanced Materials: Design, Processing, Characterization and Applications; Advances in Aerospace Technology","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127737326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Rahman, Javier Becerril, Dipannita Ghosh, Nazmul Islam, A. Ashraf
Infrared (IR) thermography is a non-contact method of measuring temperature that analyzes the infrared radiation emitted by an object. Properties of polymer composites are heavily influenced by the filler material, filler size, and filler dispersion, and thus thermographic analysis can be a useful tool to determine the curing and filler dispersion. In this study, we investigated the curing mechanisms of polymer composites at the microscale by capturing real-time temperature using an IR Thermal Camera. Silicone polymers with fillers of Graphene, Graphite powder, Graphite flake, and Molybdenum disulfide (MoS2) were subsequently poured into a customized 3D printed mold for thermography. The nanocomposites were microscopically heated with a Nichrome resistance wire, and real-time surface temperatures were measured using different Softwares. This infrared thermal camera divides the target area into 640 × 480 pixels, allowing measurement and analysis of the sample with a resolution of 65 micrometers. Depending on the filler material, the temperature rises to a certain maximum point before curing, and once curing is complete, polymer composites exhibit a rapid temperature change indicating a transition from viscous fluid to solid. MoS2, Polydimethylsiloxane (PDMS) without filler, and PDMS with larger filler are ranked in order of maximum constant temperature. PDMS (without filler) cures in 500s, while PDMS-Graphene and PDMS Graphite Powder cure in about 800s. The curing time for PDMS Graphite flake is slightly longer (950s), while MoS2 is around 520s. Therefore, this technique can indicate the influence of fillers on the curing of composites at the microscale, which is difficult to achieve by conventional methods such as differential scanning calorimetry. This nondestructive, low-cost, fast infrared thermography can be used to analyze the properties of polymer composites with different fillers and dispersion qualities in a variety of applications including precision additive manufacturing and quality control of curable composite inks.
{"title":"Non-Destructive Infrared Thermographic Curing Analysis of Polymer Composites","authors":"M. Rahman, Javier Becerril, Dipannita Ghosh, Nazmul Islam, A. Ashraf","doi":"10.1115/imece2022-96116","DOIUrl":"https://doi.org/10.1115/imece2022-96116","url":null,"abstract":"\u0000 Infrared (IR) thermography is a non-contact method of measuring temperature that analyzes the infrared radiation emitted by an object. Properties of polymer composites are heavily influenced by the filler material, filler size, and filler dispersion, and thus thermographic analysis can be a useful tool to determine the curing and filler dispersion. In this study, we investigated the curing mechanisms of polymer composites at the microscale by capturing real-time temperature using an IR Thermal Camera. Silicone polymers with fillers of Graphene, Graphite powder, Graphite flake, and Molybdenum disulfide (MoS2) were subsequently poured into a customized 3D printed mold for thermography. The nanocomposites were microscopically heated with a Nichrome resistance wire, and real-time surface temperatures were measured using different Softwares. This infrared thermal camera divides the target area into 640 × 480 pixels, allowing measurement and analysis of the sample with a resolution of 65 micrometers. Depending on the filler material, the temperature rises to a certain maximum point before curing, and once curing is complete, polymer composites exhibit a rapid temperature change indicating a transition from viscous fluid to solid. MoS2, Polydimethylsiloxane (PDMS) without filler, and PDMS with larger filler are ranked in order of maximum constant temperature. PDMS (without filler) cures in 500s, while PDMS-Graphene and PDMS Graphite Powder cure in about 800s. The curing time for PDMS Graphite flake is slightly longer (950s), while MoS2 is around 520s. Therefore, this technique can indicate the influence of fillers on the curing of composites at the microscale, which is difficult to achieve by conventional methods such as differential scanning calorimetry. This nondestructive, low-cost, fast infrared thermography can be used to analyze the properties of polymer composites with different fillers and dispersion qualities in a variety of applications including precision additive manufacturing and quality control of curable composite inks.","PeriodicalId":146276,"journal":{"name":"Volume 3: Advanced Materials: Design, Processing, Characterization and Applications; Advances in Aerospace Technology","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133595027","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
C. Duncan, R. Perkins, Daniel Johnson, M. Chandler, Robert Moser, J. Sherburn, Y. Hammi
Concrete is a widely implemented material in simulation codes and understanding its response in different loading scenarios is of interest to researchers. Notably, concrete is an extremely versatile material for many different types of applications due to its ability to withstand high compressive loading conditions at an affordable cost. For this reason, it is of a strong interest to many researchers. Specifically, understanding the response of the concrete materials in ballistic loading conditions is of importance for scenarios such as military and defense applications. Furthermore, computational models have been developed to simulate the response of contentious materials in these loading conditions. In our study, a computational finite element analysis is conducted to evaluate the response of the high strength concrete denoted as BBR9. The mechanical response of this concrete is captured using two constitutive material models denoted as the Concrete Damage and Plasticity Model 2 (CDPM2) and the Holmquist-Johnson-Cook (HJC) concrete model. In this study, the material parameters of these concrete models are calibrated using existing experimental data found in literature. Specifically, confined triaxial compression and uniaxial compressive experiments (for multiple strain rates) are used to determine the parameters which are implemented to define the response of the BBR9 concrete for each material model. These calibrated material models are implemented to conduct finite element simulations to capture the ballistic impact response of the BBR9 concrete. The finite element simulations are conducted using impact velocities ranging from 300m/s to 1300m/s to present a wide ranged assessment of the energy transfer between the projectile and the BBR9 concrete targets due to the impact. Additionally, for our study a BBR9 target thickness of 25.4mm and a simple spherical projectile is considered. A numerical assessment of the material models is presented by comparing the impact velocity against the residual velocity for each simulation point considered in this study. These results present an assessment of the concrete models and also provides a conceptual validation of their responses. The material models are also qualitatively compared through crater and scabbing diameter results of the targets. The CDPM2 model presents scabbing on the front and rear surfaces of the concrete target, while the HJC model shows cratering of the impact site. Additional experimental studies are warranted to assess the response of this concrete under ballistic loads. Further, future experimental studies can be used to validate these finite element constitutive material models in the appropriate referent of the ballistic impacts.
{"title":"Comparison of Ballistic Impact Simulations Using Different Constitutive Material Models of Concrete","authors":"C. Duncan, R. Perkins, Daniel Johnson, M. Chandler, Robert Moser, J. Sherburn, Y. Hammi","doi":"10.1115/imece2022-94248","DOIUrl":"https://doi.org/10.1115/imece2022-94248","url":null,"abstract":"\u0000 Concrete is a widely implemented material in simulation codes and understanding its response in different loading scenarios is of interest to researchers. Notably, concrete is an extremely versatile material for many different types of applications due to its ability to withstand high compressive loading conditions at an affordable cost. For this reason, it is of a strong interest to many researchers. Specifically, understanding the response of the concrete materials in ballistic loading conditions is of importance for scenarios such as military and defense applications. Furthermore, computational models have been developed to simulate the response of contentious materials in these loading conditions. In our study, a computational finite element analysis is conducted to evaluate the response of the high strength concrete denoted as BBR9. The mechanical response of this concrete is captured using two constitutive material models denoted as the Concrete Damage and Plasticity Model 2 (CDPM2) and the Holmquist-Johnson-Cook (HJC) concrete model. In this study, the material parameters of these concrete models are calibrated using existing experimental data found in literature. Specifically, confined triaxial compression and uniaxial compressive experiments (for multiple strain rates) are used to determine the parameters which are implemented to define the response of the BBR9 concrete for each material model. These calibrated material models are implemented to conduct finite element simulations to capture the ballistic impact response of the BBR9 concrete. The finite element simulations are conducted using impact velocities ranging from 300m/s to 1300m/s to present a wide ranged assessment of the energy transfer between the projectile and the BBR9 concrete targets due to the impact. Additionally, for our study a BBR9 target thickness of 25.4mm and a simple spherical projectile is considered. A numerical assessment of the material models is presented by comparing the impact velocity against the residual velocity for each simulation point considered in this study. These results present an assessment of the concrete models and also provides a conceptual validation of their responses. The material models are also qualitatively compared through crater and scabbing diameter results of the targets. The CDPM2 model presents scabbing on the front and rear surfaces of the concrete target, while the HJC model shows cratering of the impact site. Additional experimental studies are warranted to assess the response of this concrete under ballistic loads. Further, future experimental studies can be used to validate these finite element constitutive material models in the appropriate referent of the ballistic impacts.","PeriodicalId":146276,"journal":{"name":"Volume 3: Advanced Materials: Design, Processing, Characterization and Applications; Advances in Aerospace Technology","volume":"307 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131608735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Margaret Nowicki, Sara Sheward, Lane Zuchowski, Seth Addeo, Owen States, Oreofeoluwa Omolade, Steven Andreen, N. Ku, Lionel Vargas-Gonzalez, Jennifer L. Bennett
Additive manufacturing (AM) is a growing field in which products are created through the addition of materials in a layer-by-layer fashion. Ceramics are typically manufactured using powder compaction and sintering. Ceramic AM is typically executed using Selective Lase Sintering (SLS) techniques to fuse powders using a laser. As with many AM techniques this process allows for the inclusion of unique and complex geometries but does not easily allow for gradient or composite material features. Conclusions from previous investigations indicate chaotic mixing, achieved through integrating a disrupted nubbed section on a traditional screw auger, was more effective for achieving composite homogeneity. However, channel depth results conflicted upon integration of nubbed sections: the existing simulation does not accurately match this inconsistency in the test data. Current work strives to close the gap between test data and simulation, and specifically match this inconsistency between the effect of channel depth and nubbed sections independently, and when combined. The goal is to seamlessly transition between mixtures while minimizing or eliminating waste. To achieve this, it will be necessary to not only understand how print head volume and geometries impact transport, but also determine the impact of gcode on improving transition speed while minimizing material waste.
{"title":"Additive Manufacturing With Ceramic Slurries","authors":"Margaret Nowicki, Sara Sheward, Lane Zuchowski, Seth Addeo, Owen States, Oreofeoluwa Omolade, Steven Andreen, N. Ku, Lionel Vargas-Gonzalez, Jennifer L. Bennett","doi":"10.1115/imece2022-96033","DOIUrl":"https://doi.org/10.1115/imece2022-96033","url":null,"abstract":"\u0000 Additive manufacturing (AM) is a growing field in which products are created through the addition of materials in a layer-by-layer fashion. Ceramics are typically manufactured using powder compaction and sintering. Ceramic AM is typically executed using Selective Lase Sintering (SLS) techniques to fuse powders using a laser. As with many AM techniques this process allows for the inclusion of unique and complex geometries but does not easily allow for gradient or composite material features. Conclusions from previous investigations indicate chaotic mixing, achieved through integrating a disrupted nubbed section on a traditional screw auger, was more effective for achieving composite homogeneity. However, channel depth results conflicted upon integration of nubbed sections: the existing simulation does not accurately match this inconsistency in the test data. Current work strives to close the gap between test data and simulation, and specifically match this inconsistency between the effect of channel depth and nubbed sections independently, and when combined. The goal is to seamlessly transition between mixtures while minimizing or eliminating waste. To achieve this, it will be necessary to not only understand how print head volume and geometries impact transport, but also determine the impact of gcode on improving transition speed while minimizing material waste.","PeriodicalId":146276,"journal":{"name":"Volume 3: Advanced Materials: Design, Processing, Characterization and Applications; Advances in Aerospace Technology","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126603048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Previous studies have shown that cicada wings has the ability to kill the bacteria on contact. Study of natural bactericidal surface in cicada wings has opened new dimensions of scientific research in bio-inspired chemical-free bactericidal surfaces. To develop and design such biomimetic bactericidal surface, it is necessary to understand the mechanical bactericidal effects of nanopillars in the presence of bacteria, which is extremely challenging due to the small relevant length and time scales. In this study, we have conducted molecular dynamics (MD) simulations to investigate the biomimetic surface with various nanopillars configurations. MD simulations is an exceptional method to simulate materials with small time and length scales with good accuracy and low computational costs. We have simulated the bacteria’s model using coarse-grained modelling and conducting MD simulations. Effects of nanopillar spacing, diameter and height on the lysis process is studied in this article. It is expected that this study will provide us insights on designing nanopillars in terms of height, spacing and diameter for optimal bactericidal effects that can help in the development of chemical-free antibacterial surface.
{"title":"Bactericidal Effects of Micropillars: A Molecular Dynamics Study","authors":"Akash Singh, Yumeng Li","doi":"10.1115/imece2022-95325","DOIUrl":"https://doi.org/10.1115/imece2022-95325","url":null,"abstract":"\u0000 Previous studies have shown that cicada wings has the ability to kill the bacteria on contact. Study of natural bactericidal surface in cicada wings has opened new dimensions of scientific research in bio-inspired chemical-free bactericidal surfaces. To develop and design such biomimetic bactericidal surface, it is necessary to understand the mechanical bactericidal effects of nanopillars in the presence of bacteria, which is extremely challenging due to the small relevant length and time scales. In this study, we have conducted molecular dynamics (MD) simulations to investigate the biomimetic surface with various nanopillars configurations. MD simulations is an exceptional method to simulate materials with small time and length scales with good accuracy and low computational costs. We have simulated the bacteria’s model using coarse-grained modelling and conducting MD simulations. Effects of nanopillar spacing, diameter and height on the lysis process is studied in this article. It is expected that this study will provide us insights on designing nanopillars in terms of height, spacing and diameter for optimal bactericidal effects that can help in the development of chemical-free antibacterial surface.","PeriodicalId":146276,"journal":{"name":"Volume 3: Advanced Materials: Design, Processing, Characterization and Applications; Advances in Aerospace Technology","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124697574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
R-phase transformation has been known to originate from thermomechanical processing, ageing and microalloying various elements in NiTi. R-phase transformation has been known for high cyclic fatigue resistance, higher stability but in short range of 1%–2% strain thus, possess high potential for sensors, actuators, dampers and elastocaloric cooling applications. Constrained groove pressing (CGP) is well known sheet metal severe plastic deformation technique for grain refinement. In this study, the microstructure evolution and R-phase transformation has been analyzed first time in NiTi sheet processed by CGP and ageing treatment. The CGP leads to severe plastic deformation and grain refinement while post ageing resulted into Ni4Ti3 precipitation. This gives rise to improvement and stabilization of R-phase transformation, larger R-phase transformation and higher martensite stress during cyclic loading as compared to the water quenched NiTi alloy. These improvements will help to extend nitinol sheet-based applications domain which utilize R-phase transformation.
{"title":"Microstructure Evolution and R-Phase Transformation in NiTi Shape Memory Alloy Processed by Constrained Groove Pressing and Ageing Treatment","authors":"A. Bhardwaj, D. Mathur, Kunthal Oswal, A. Gupta","doi":"10.1115/imece2022-94155","DOIUrl":"https://doi.org/10.1115/imece2022-94155","url":null,"abstract":"\u0000 R-phase transformation has been known to originate from thermomechanical processing, ageing and microalloying various elements in NiTi. R-phase transformation has been known for high cyclic fatigue resistance, higher stability but in short range of 1%–2% strain thus, possess high potential for sensors, actuators, dampers and elastocaloric cooling applications. Constrained groove pressing (CGP) is well known sheet metal severe plastic deformation technique for grain refinement. In this study, the microstructure evolution and R-phase transformation has been analyzed first time in NiTi sheet processed by CGP and ageing treatment. The CGP leads to severe plastic deformation and grain refinement while post ageing resulted into Ni4Ti3 precipitation. This gives rise to improvement and stabilization of R-phase transformation, larger R-phase transformation and higher martensite stress during cyclic loading as compared to the water quenched NiTi alloy. These improvements will help to extend nitinol sheet-based applications domain which utilize R-phase transformation.","PeriodicalId":146276,"journal":{"name":"Volume 3: Advanced Materials: Design, Processing, Characterization and Applications; Advances in Aerospace Technology","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125811192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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