Takeru Miyagawa, Y. Sakai, A. Yonezu, K. Mori, Nobuhiko Kato, K. Ishibashi
Combinatorial approach is a prominent method to synthesize samples with atomic composition gradients, which enables the high-throughput discovery of new materials. Titanium-copper (Ti-Cu) alloy is widely used in electronic devices because of its excellent mechanical properties such as stress relaxation resistance, bond formality, and workability. By synthesizing Ti-Cu thin film with combinatorial approach, the mechanical property may be improved, leading to a new application. Molecular Dynamics (MD) simulation is a powerful tool to predict mechanical property, but it requires interatomic potentials to depict the movements of atoms. Because of the complex structures of Ti-Cu thin film synthesized by combinatorial approach, the creation of interatomic potentials is a difficult and time-consuming process. Therefore, in this study, a neural network (NN) based method to create interatomic potentials is developed, which are referred to as neural network potentials (NNPs). It is found that NNP can accurately reproduce the energies and forces calculated by Ab initio molecular dynamics (AIMD) simulations. Finally, using MD simulations with developed NNP, the mechanism of mechanical properties is investigated from the perspective of atomic scales.
{"title":"Investigation of Mechanical Properties of Combinatorial Ti-Cu Film Using MD Simulation With Neural Network Potential","authors":"Takeru Miyagawa, Y. Sakai, A. Yonezu, K. Mori, Nobuhiko Kato, K. Ishibashi","doi":"10.1115/imece2022-94934","DOIUrl":"https://doi.org/10.1115/imece2022-94934","url":null,"abstract":"\u0000 Combinatorial approach is a prominent method to synthesize samples with atomic composition gradients, which enables the high-throughput discovery of new materials. Titanium-copper (Ti-Cu) alloy is widely used in electronic devices because of its excellent mechanical properties such as stress relaxation resistance, bond formality, and workability. By synthesizing Ti-Cu thin film with combinatorial approach, the mechanical property may be improved, leading to a new application. Molecular Dynamics (MD) simulation is a powerful tool to predict mechanical property, but it requires interatomic potentials to depict the movements of atoms. Because of the complex structures of Ti-Cu thin film synthesized by combinatorial approach, the creation of interatomic potentials is a difficult and time-consuming process. Therefore, in this study, a neural network (NN) based method to create interatomic potentials is developed, which are referred to as neural network potentials (NNPs). It is found that NNP can accurately reproduce the energies and forces calculated by Ab initio molecular dynamics (AIMD) simulations. Finally, using MD simulations with developed NNP, the mechanism of mechanical properties is investigated from the perspective of atomic scales.","PeriodicalId":146276,"journal":{"name":"Volume 3: Advanced Materials: Design, Processing, Characterization and Applications; Advances in Aerospace Technology","volume":"12 6 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":"116437056","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 this study, a computational fluid dynamics (CFD) model is developed to investigate the thermal-mechanical process in additive friction stir deposition (AFSD), a novel additive manufacturing (AM) process allowing site-specific deposition. Material conversation law with a steady-state heat is applied where the heat generation is measured considering stacking/slipping boundary conditions and the spatial heat flux is incorporated using ANSYS user-defined functions (UDFs). For measuring the temperature evolution throughout the process, the conservation of energy equation is solved where the heat is generated from the dynamic contact between the tool and feed rod interfaces. For material flow, the laminar viscous model is adopted where the feed rod is considered as a non-Newtonian visco-plastic material, and the viscosity and strain rate are temperature-dependent. The simulation results show the temperature evaluation of the deposited material as a highly viscous flow where the temperature is optimized around 20% below the melting point temperature of the feed rod. Since the heat generation depends on the rotational and translational motion of the feed rod, the maximum temperature changes with varying process parameters. Finally, the results of the simulation such as temperature evolution, heat flux, material velocity, etc are exhibited with varying process parameters.
{"title":"Thermo-Mechanical Process Modeling of Additive Friction Stir Deposition of Ti-6Al-4V Alloy","authors":"G. A. Raihan, U. Chakravarty","doi":"10.1115/imece2022-94717","DOIUrl":"https://doi.org/10.1115/imece2022-94717","url":null,"abstract":"\u0000 In this study, a computational fluid dynamics (CFD) model is developed to investigate the thermal-mechanical process in additive friction stir deposition (AFSD), a novel additive manufacturing (AM) process allowing site-specific deposition. Material conversation law with a steady-state heat is applied where the heat generation is measured considering stacking/slipping boundary conditions and the spatial heat flux is incorporated using ANSYS user-defined functions (UDFs). For measuring the temperature evolution throughout the process, the conservation of energy equation is solved where the heat is generated from the dynamic contact between the tool and feed rod interfaces. For material flow, the laminar viscous model is adopted where the feed rod is considered as a non-Newtonian visco-plastic material, and the viscosity and strain rate are temperature-dependent. The simulation results show the temperature evaluation of the deposited material as a highly viscous flow where the temperature is optimized around 20% below the melting point temperature of the feed rod. Since the heat generation depends on the rotational and translational motion of the feed rod, the maximum temperature changes with varying process parameters. Finally, the results of the simulation such as temperature evolution, heat flux, material velocity, etc are exhibited with varying process parameters.","PeriodicalId":146276,"journal":{"name":"Volume 3: Advanced Materials: Design, Processing, Characterization and Applications; Advances in Aerospace Technology","volume":"51 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":"114487360","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}
Pressure sensors have been used in devices that require accurate and stable pressure measurements for reliable operations. Metastructure-based pressure sensors (MBPS) have the potential to achieve higher sensitivity and broader sensing range with greater design flexibility and lower weight. Currently, additive manufacturing (AM) has enabled rapid prototyping of high-resolution metastructures at small scales. Deposition of a conductive coating layer on the metastructure can effectively introduce electrical conductivity in MBPS. However, the coupling between the electrical response and the mechanical properties of the metastructure remains unknown. It is not clear how the metastructure design can affect the performance of pressure sensors. In this work, a set of octet-truss cubic metastructures with different unit cell numbers are modeled and fabricated. The sensitivity and sensing range of each metastructure design are predicted from the coupled mechanical-electrical finite element model, the analytical model and the in-situ compression-resistance test, respectively. It is found that increasing unit cell number leads to decreased nominal resistance and enhanced sensing range. But the improvement of sensitivity is limited when the unit cell number exceeds a threshold value. The computational and experimental approaches developed here can be applied to other MBPS with different metastructure configurations and material selections.
{"title":"Effect of Metastructure Design on the Performance of Pressure Sensors","authors":"Huan Zhao, J. Huddy, W. Scheideler, Yan Li","doi":"10.1115/imece2022-95099","DOIUrl":"https://doi.org/10.1115/imece2022-95099","url":null,"abstract":"\u0000 Pressure sensors have been used in devices that require accurate and stable pressure measurements for reliable operations. Metastructure-based pressure sensors (MBPS) have the potential to achieve higher sensitivity and broader sensing range with greater design flexibility and lower weight. Currently, additive manufacturing (AM) has enabled rapid prototyping of high-resolution metastructures at small scales. Deposition of a conductive coating layer on the metastructure can effectively introduce electrical conductivity in MBPS. However, the coupling between the electrical response and the mechanical properties of the metastructure remains unknown. It is not clear how the metastructure design can affect the performance of pressure sensors. In this work, a set of octet-truss cubic metastructures with different unit cell numbers are modeled and fabricated. The sensitivity and sensing range of each metastructure design are predicted from the coupled mechanical-electrical finite element model, the analytical model and the in-situ compression-resistance test, respectively. It is found that increasing unit cell number leads to decreased nominal resistance and enhanced sensing range. But the improvement of sensitivity is limited when the unit cell number exceeds a threshold value. The computational and experimental approaches developed here can be applied to other MBPS with different metastructure configurations and material selections.","PeriodicalId":146276,"journal":{"name":"Volume 3: Advanced Materials: Design, Processing, Characterization and Applications; Advances in Aerospace Technology","volume":"13 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":"128528041","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 this paper, a new methodology for the choice of the best structural theories through Machine Learning (ML) techniques is described, with a particular focus on composite shells. The identification of the most adequate theory can be operated very efficiently using Convolutional Neural Networks (CNN) as surrogate models to replicate the performances of a Finite Element (FE) formulation, although requiring only a small fraction of the usual amount of analyses. Enhanced by the introduction of the Carrera Unified Formulation (CUF), the FE Method (FEM) provides the results necessary for the training of the networks, while the Node Dependent Kinematics (NDK) approach opens to the practical implementation of local refinement capabilities. The evaluation of different structural theories is carried out with the Axiomatic/Asymptotic Method (AAM) and this can be done for both static and dynamic analyses, with The Best Theory Diagrams (BTD) being the outcome of this rating procedure. As shown in the results, CNNs can properly identify and reproduce the underlying connections between different sets of problem features and the accuracy of a given structural theory with just a very small amount of available reference data.
{"title":"On the Accuracy and Efficiency of Convolutional Neural Networks for Element-Wise Refinement of FEM Models","authors":"M. Petrolo, P. Iannotti, A. Pagani, E. Carrera","doi":"10.1115/imece2022-93995","DOIUrl":"https://doi.org/10.1115/imece2022-93995","url":null,"abstract":"\u0000 In this paper, a new methodology for the choice of the best structural theories through Machine Learning (ML) techniques is described, with a particular focus on composite shells. The identification of the most adequate theory can be operated very efficiently using Convolutional Neural Networks (CNN) as surrogate models to replicate the performances of a Finite Element (FE) formulation, although requiring only a small fraction of the usual amount of analyses. Enhanced by the introduction of the Carrera Unified Formulation (CUF), the FE Method (FEM) provides the results necessary for the training of the networks, while the Node Dependent Kinematics (NDK) approach opens to the practical implementation of local refinement capabilities. The evaluation of different structural theories is carried out with the Axiomatic/Asymptotic Method (AAM) and this can be done for both static and dynamic analyses, with The Best Theory Diagrams (BTD) being the outcome of this rating procedure. As shown in the results, CNNs can properly identify and reproduce the underlying connections between different sets of problem features and the accuracy of a given structural theory with just a very small amount of available reference data.","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":"128979307","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}
Siddharth Bhaganagar, P. Biswas, Mangilal Agarwal, H. Dalir
Cellulose Nanofibers (CNF) are produced from plant cellulose microfibers through a facile synthesis process. These fibers are discontinuous, very graphitic, and extremely compatible with the majority of polymer processing techniques; they can be dispersed isotropically or anisotropically. Since they are available in a free-flowing powder form, the dry carbon fiber can be physically modified with the addition of CNF. The effect of the CNF compositions, their morphology on carbon fiber, and subsequent mechanical properties are explored in this paper. The CNF composite nanofiber networks are introduced as interleave layers to improve the interlaminar shear strength (ILSS) of an epoxy/carbon fiber laminate composite. Dry carbon fiber is coated by different volume fractions of CNF (0.6 wt.%, 0.8 wt.%, 1 wt.%) through the strong bath sonication process. Laminates are fabricated by modifying dry carbon fiber surface with CNF resulting in a considerable improvement in the mechanical characteristics as compared to a neat sample. The application of CNF composite nanofiber networks as an interleaved layer in an epoxy/carbon laminate increases the delamination resistance of the ILSS in both 0.8wt% and 1 wt.% CNF enhanced laminates by 27.2%, and 12.4% respectively, but no significant difference is found for ILSS in 0.6 wt.% CNF enhanced laminate. Moreover, a significant improvement is observed in flexural modulus for 0.8 wt.% CNF coated carbon fiber laminate. This suggests that CNF can enhance the delamination resistance and flexural strength of an epoxy/carbon fiber laminate undergoing delamination and deformation. This result is attributed to crack path modification, and load energy absorption by higher modulus CNFs reinforced nanofibers interleave in the laminate resulting in a higher shear modulus to the networks.
{"title":"Cellulose Nanofibers (CNF)/Carbon Fiber Composites With Enhanced Flexural Strength for Structural Applications","authors":"Siddharth Bhaganagar, P. Biswas, Mangilal Agarwal, H. Dalir","doi":"10.1115/imece2022-95772","DOIUrl":"https://doi.org/10.1115/imece2022-95772","url":null,"abstract":"\u0000 Cellulose Nanofibers (CNF) are produced from plant cellulose microfibers through a facile synthesis process. These fibers are discontinuous, very graphitic, and extremely compatible with the majority of polymer processing techniques; they can be dispersed isotropically or anisotropically. Since they are available in a free-flowing powder form, the dry carbon fiber can be physically modified with the addition of CNF. The effect of the CNF compositions, their morphology on carbon fiber, and subsequent mechanical properties are explored in this paper. The CNF composite nanofiber networks are introduced as interleave layers to improve the interlaminar shear strength (ILSS) of an epoxy/carbon fiber laminate composite. Dry carbon fiber is coated by different volume fractions of CNF (0.6 wt.%, 0.8 wt.%, 1 wt.%) through the strong bath sonication process. Laminates are fabricated by modifying dry carbon fiber surface with CNF resulting in a considerable improvement in the mechanical characteristics as compared to a neat sample. The application of CNF composite nanofiber networks as an interleaved layer in an epoxy/carbon laminate increases the delamination resistance of the ILSS in both 0.8wt% and 1 wt.% CNF enhanced laminates by 27.2%, and 12.4% respectively, but no significant difference is found for ILSS in 0.6 wt.% CNF enhanced laminate. Moreover, a significant improvement is observed in flexural modulus for 0.8 wt.% CNF coated carbon fiber laminate. This suggests that CNF can enhance the delamination resistance and flexural strength of an epoxy/carbon fiber laminate undergoing delamination and deformation. This result is attributed to crack path modification, and load energy absorption by higher modulus CNFs reinforced nanofibers interleave in the laminate resulting in a higher shear modulus to the networks.","PeriodicalId":146276,"journal":{"name":"Volume 3: Advanced Materials: Design, Processing, Characterization and Applications; Advances in Aerospace Technology","volume":"17 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":"125315494","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}
Senthil Kumar Velukkudi Santhanam, Joshua Richard Jeyarajan, S. Manivannan, Joseph Beski Jayamanickam, Raman Kuppusamy, Nitin Nambi
Friction Stir Processing has become the ideal way to refine the grains which increases the mechanical properties like formability, microhardness, yield strength and Tensile Strength, also increases the corrosion resistance, which emerged as the effective way for selective surface modification and also retaining the bulk properties. In this present work, Titanium grade 2 (Commercially Pure Titanium) is selected as the material of choice due to its superior corrosion resistance compared to other grades of titanium, high tensile strength and high hardness. Due to soft, excellent corrosion resistance and ductile properties Cp - Ti is used in automotive parts and airframe structure application. Friction stir processing is being used to improve mechanical properties such as tensile and microhardness, as well as corrosion properties. Friction stir processing (FSP) is used to fabricate the Titanium plate, by varying the process parameters such as Tool Rotation Speed (rpm), Traverse Speed (mm/min), and Number of Passes. The process parameters used in this experiment are Tool Rotational speed of 1000 rpm, 1200 rpm and 1400 rpm, Traverse speed of 30 mm/min, 45 mm/min, and 60 mm/min and single pass, double pass and triple pass. Taguchi’s L9 Orthogonal array is used to conduct the experiment, which considers three parameters at three separate levels. A tapered cylindrical pin of HSS (High Speed Steel) with Rockwell hardness of 65 HRC is designed and fabricated to provide material flow while simultaneously minimizing the tool wear. The tensile test was carried out using Universal Testing Machine (UTM) as per ASTM E8 standard to determine the ultimate tensile strength and yield strength of FSPed CP – Ti (grade 2), microhardness test was carried out using Vickers Hardness with a diamond indenter and corrosion values are evaluated using Immersion corrosion testing method by weighing the before and after weights of the sample as per ASTM G31 – 72. Since Titanium Grade 2 offers very high corrosion resistance, the rate of corrosion is negligible when done in 24 hours. Thus, immersion corrosion test is done over 120 hours, so that corrosion rate can be measured efficiently. And also evaluate the torque induced in this process. Grey Relational Analysis (GRA) is performed on the multiple test results such that tensile strength, microhardness and corrosion resistances to find the optimum process parameters, by applying the test results as inputs. Analysis of variance (ANOVA) is the most efficient parametric method for analyzing friction stir processing data from experiments results.
{"title":"Analysis on Mechanical Properties and Corrosion Behavior of Friction Stir Processing of Commercially Pure-Titanium","authors":"Senthil Kumar Velukkudi Santhanam, Joshua Richard Jeyarajan, S. Manivannan, Joseph Beski Jayamanickam, Raman Kuppusamy, Nitin Nambi","doi":"10.1115/imece2022-93876","DOIUrl":"https://doi.org/10.1115/imece2022-93876","url":null,"abstract":"\u0000 Friction Stir Processing has become the ideal way to refine the grains which increases the mechanical properties like formability, microhardness, yield strength and Tensile Strength, also increases the corrosion resistance, which emerged as the effective way for selective surface modification and also retaining the bulk properties. In this present work, Titanium grade 2 (Commercially Pure Titanium) is selected as the material of choice due to its superior corrosion resistance compared to other grades of titanium, high tensile strength and high hardness. Due to soft, excellent corrosion resistance and ductile properties Cp - Ti is used in automotive parts and airframe structure application. Friction stir processing is being used to improve mechanical properties such as tensile and microhardness, as well as corrosion properties. Friction stir processing (FSP) is used to fabricate the Titanium plate, by varying the process parameters such as Tool Rotation Speed (rpm), Traverse Speed (mm/min), and Number of Passes. The process parameters used in this experiment are Tool Rotational speed of 1000 rpm, 1200 rpm and 1400 rpm, Traverse speed of 30 mm/min, 45 mm/min, and 60 mm/min and single pass, double pass and triple pass. Taguchi’s L9 Orthogonal array is used to conduct the experiment, which considers three parameters at three separate levels. A tapered cylindrical pin of HSS (High Speed Steel) with Rockwell hardness of 65 HRC is designed and fabricated to provide material flow while simultaneously minimizing the tool wear.\u0000 The tensile test was carried out using Universal Testing Machine (UTM) as per ASTM E8 standard to determine the ultimate tensile strength and yield strength of FSPed CP – Ti (grade 2), microhardness test was carried out using Vickers Hardness with a diamond indenter and corrosion values are evaluated using Immersion corrosion testing method by weighing the before and after weights of the sample as per ASTM G31 – 72. Since Titanium Grade 2 offers very high corrosion resistance, the rate of corrosion is negligible when done in 24 hours. Thus, immersion corrosion test is done over 120 hours, so that corrosion rate can be measured efficiently. And also evaluate the torque induced in this process. Grey Relational Analysis (GRA) is performed on the multiple test results such that tensile strength, microhardness and corrosion resistances to find the optimum process parameters, by applying the test results as inputs. Analysis of variance (ANOVA) is the most efficient parametric method for analyzing friction stir processing data from experiments results.","PeriodicalId":146276,"journal":{"name":"Volume 3: Advanced Materials: Design, Processing, Characterization and Applications; Advances in Aerospace Technology","volume":"84 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":"117175345","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. Fais, Isaiah Yasko, A. Lutfullaeva, Muhammad Ali, R. Walker
This research presents a newly developed hydrodynamic test rig for experimental testing of hydrodynamic thrust bearings. In this study, the test rig applies thrust loads up to 500 lbf at rotational speeds up to 6,000 rpm. Three fixed geometry hydrodynamic thrust bearings with eight identical helically tapered thrust pads made of cast aluminum alloy have each been machined such that the depth of their tapered surface at the leading edge is 0.0005″, 0.0015″, and 0.0025″ with all other geometrical features held constant. The test rig includes an oil conditioning system which supplies a constant flow of ISO 32 motor oil to the test bearing at 40°C. An integrated sensor system includes an eddy current sensor to measure the minimum oil film thickness, a friction torque moment arm with load cell to measure power loss, K-type thermocouples to measure bearing temperature, pressure transducers to measure oil film pressure distribution, and load cells to measure the applied thrust force. The test rig also introduces a novel bearing alignment system used to ensure precise alignment of the bearing and runner during operation based on pressure feedback from individual thrust pads. Results obtained from this experiment are used to compare the effect of taper geometry on active performance of the test bearings considered. Trends in performance observed are related to the trends predicted analytically by the Reynolds equation.
{"title":"Experimental Investigation of Fixed-Geometry Thrust Bearing Taper Geometry on Critical Operating Parameters","authors":"C. Fais, Isaiah Yasko, A. Lutfullaeva, Muhammad Ali, R. Walker","doi":"10.1115/imece2022-95071","DOIUrl":"https://doi.org/10.1115/imece2022-95071","url":null,"abstract":"\u0000 This research presents a newly developed hydrodynamic test rig for experimental testing of hydrodynamic thrust bearings. In this study, the test rig applies thrust loads up to 500 lbf at rotational speeds up to 6,000 rpm. Three fixed geometry hydrodynamic thrust bearings with eight identical helically tapered thrust pads made of cast aluminum alloy have each been machined such that the depth of their tapered surface at the leading edge is 0.0005″, 0.0015″, and 0.0025″ with all other geometrical features held constant. The test rig includes an oil conditioning system which supplies a constant flow of ISO 32 motor oil to the test bearing at 40°C. An integrated sensor system includes an eddy current sensor to measure the minimum oil film thickness, a friction torque moment arm with load cell to measure power loss, K-type thermocouples to measure bearing temperature, pressure transducers to measure oil film pressure distribution, and load cells to measure the applied thrust force. The test rig also introduces a novel bearing alignment system used to ensure precise alignment of the bearing and runner during operation based on pressure feedback from individual thrust pads. Results obtained from this experiment are used to compare the effect of taper geometry on active performance of the test bearings considered. Trends in performance observed are related to the trends predicted analytically by the Reynolds equation.","PeriodicalId":146276,"journal":{"name":"Volume 3: Advanced Materials: Design, Processing, Characterization and Applications; Advances in Aerospace Technology","volume":"16 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":"122948592","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}
Bolted joints are widely used in the field of aerospace, civil and mechanical engineering. During their service life, extreme loading or environmental factors can cause the loosening of bolts. In this paper, a bolt loosening detection method based on computer vision and image processing is developed to identify bolt rotation angle in a steel multi-story frame structure. The experimental results show that the bolt target detection accuracy can reach 100% by using the Yolo-V5s deep learning model trained with a self-developed bolt object dataset. The dataset consists of 337 bolt images captured in nature scenes. For the angle calculation, the final result shows that the identification error is less than 5.8°, and at a slight camera angle (0∼20°), the maximum error even does not exceed 2.8°. Thus, the effectiveness of this method for detecting rotary loosening of bolts is well validated.
{"title":"Bolt Loosening Detection for a Steel Frame Multi-Story Structure Based on Deep Learning and Digital Image Processing","authors":"Yadian Zhao, Zhenglin Yang, Chao Xu","doi":"10.1115/imece2022-94786","DOIUrl":"https://doi.org/10.1115/imece2022-94786","url":null,"abstract":"\u0000 Bolted joints are widely used in the field of aerospace, civil and mechanical engineering. During their service life, extreme loading or environmental factors can cause the loosening of bolts. In this paper, a bolt loosening detection method based on computer vision and image processing is developed to identify bolt rotation angle in a steel multi-story frame structure. The experimental results show that the bolt target detection accuracy can reach 100% by using the Yolo-V5s deep learning model trained with a self-developed bolt object dataset. The dataset consists of 337 bolt images captured in nature scenes. For the angle calculation, the final result shows that the identification error is less than 5.8°, and at a slight camera angle (0∼20°), the maximum error even does not exceed 2.8°. Thus, the effectiveness of this method for detecting rotary loosening of bolts is well validated.","PeriodicalId":146276,"journal":{"name":"Volume 3: Advanced Materials: Design, Processing, Characterization and Applications; Advances in Aerospace Technology","volume":"109 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":"115119304","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 extraction of Nano-sized fillers from bio sources has been a key focus of the material industry to secure green composites for a wide range of applications. Consequently, chemical fragmentation and downsizing of waste lignocellulosic fibers into small size particles is a viable economic and environmental option. The objective of this work is to explore the potential use of Nano natural fillers as a reinforcement element in thermoplastic polymers. In specific, the Nano-sized lignocellulosic filler is extracted from date palm microfibers using the mechanical ball milling technique. The ball milling is performed at a high speed of 12 cycles per minute for four different time durations. The achieved nanoparticle size ranged from 80 to 122 nm, reduced to a range of 70 to 51 nm and then reached 27 to 39 nm after 3, 4 and 5 hours of powdering, respectively, with no significant change in size after 6 hours of milling. After that, the morphological properties of the produced fillers are characterized using various techniques such as transmission electron microscopy (TEM) and scanning electron microscopy (SEM). Finally, the mechanical performance of the reinforced recycled polypropylene (rPP) using 10% (wt.) date palm nanofillers is investigated using tensile and flexural tests, as well as the physical properties including water absorption and density tests. Successful implementation of nanofillers in bio-composites offers an economical and sustainable route to attain high-performance material in the future.
{"title":"Production of Date Palm Nanoparticle Reinforced Composites and Characterization of Their Mechanical Properties","authors":"Mahmoud Al-Safy, Nasr Al Hinai, K. Alzebdeh","doi":"10.1115/imece2022-95413","DOIUrl":"https://doi.org/10.1115/imece2022-95413","url":null,"abstract":"\u0000 The extraction of Nano-sized fillers from bio sources has been a key focus of the material industry to secure green composites for a wide range of applications. Consequently, chemical fragmentation and downsizing of waste lignocellulosic fibers into small size particles is a viable economic and environmental option. The objective of this work is to explore the potential use of Nano natural fillers as a reinforcement element in thermoplastic polymers. In specific, the Nano-sized lignocellulosic filler is extracted from date palm microfibers using the mechanical ball milling technique. The ball milling is performed at a high speed of 12 cycles per minute for four different time durations. The achieved nanoparticle size ranged from 80 to 122 nm, reduced to a range of 70 to 51 nm and then reached 27 to 39 nm after 3, 4 and 5 hours of powdering, respectively, with no significant change in size after 6 hours of milling. After that, the morphological properties of the produced fillers are characterized using various techniques such as transmission electron microscopy (TEM) and scanning electron microscopy (SEM). Finally, the mechanical performance of the reinforced recycled polypropylene (rPP) using 10% (wt.) date palm nanofillers is investigated using tensile and flexural tests, as well as the physical properties including water absorption and density tests. Successful implementation of nanofillers in bio-composites offers an economical and sustainable route to attain high-performance material in the future.","PeriodicalId":146276,"journal":{"name":"Volume 3: Advanced Materials: Design, Processing, Characterization and Applications; Advances in Aerospace Technology","volume":"41 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":"129141614","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 applicability of artificial neural networks (ANNs) on the prediction of the structural optimization results of a truss structure is investigated. Two different ANN architectures are employed and the effect of using various optimizers and activation functions on their prediction performance is evaluated. Unlike the traditional machine learning network strategies where usually a physical response of the truss optimization (such as compliance, stress, etc.) is predicted, in this study, a new way of prediction is utilized for the truss-like structures; particularly predicting the optimized thickness values of the strut members by the ANNs. Thus, the input data used in these networks are the thickness values of the strut members at a certain initial iteration while the optimized thickness values are predicted as the outputs. A cantilever beam example is presented for the truss optimization to show the efficacy of the presented ANNs. The results indicate that using the thickness values at a certain initial iteration as inputs and final iteration thicknesses as outputs in ANNs are promising for the structural optimization prediction of the presented truss problem with the appropriate selection of the architecture, optimizer, activation function, and the input-output data formation.
{"title":"Evaluation of Deep Learning Networks for Predicting Truss Topology Optimization Results","authors":"R. Gorguluarslan, Gorkem Can Ates","doi":"10.1115/imece2022-95870","DOIUrl":"https://doi.org/10.1115/imece2022-95870","url":null,"abstract":"\u0000 The applicability of artificial neural networks (ANNs) on the prediction of the structural optimization results of a truss structure is investigated. Two different ANN architectures are employed and the effect of using various optimizers and activation functions on their prediction performance is evaluated. Unlike the traditional machine learning network strategies where usually a physical response of the truss optimization (such as compliance, stress, etc.) is predicted, in this study, a new way of prediction is utilized for the truss-like structures; particularly predicting the optimized thickness values of the strut members by the ANNs. Thus, the input data used in these networks are the thickness values of the strut members at a certain initial iteration while the optimized thickness values are predicted as the outputs. A cantilever beam example is presented for the truss optimization to show the efficacy of the presented ANNs. The results indicate that using the thickness values at a certain initial iteration as inputs and final iteration thicknesses as outputs in ANNs are promising for the structural optimization prediction of the presented truss problem with the appropriate selection of the architecture, optimizer, activation function, and the input-output data formation.","PeriodicalId":146276,"journal":{"name":"Volume 3: Advanced Materials: Design, Processing, Characterization and Applications; Advances in Aerospace Technology","volume":"17 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":"132309294","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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