Finite element analysis (FEA) of fused deposition modeling (FDM) has recently been recognized in additive manufacturing (AM) for predictions in temperature gradient of three-dimensions (3D) printed components. These predictions can be invaluable for making corrections to the printing process to improve quality of printed components. However, FEA has its limitations. For example, models with fine mesh (small element size) yield more accurate results than ones with coarse mesh (large element size). Comparing with the coarse mesh model, a fine mesh model can take considerably longer computational times and discourages most manufacturers from using FEA. In this work, an innovative deep-learning (DL) based super-resolution approach is used to improve the result accuracy of a coarse mesh model to the higher accuracy level of a fine mesh model and reduce the computational time. The element in the FEA was treated as the physical pixel in an image, so the fine temperature grid and coarse temperature grid in the FEA were analogous to high resolution (HR) images and low resolution (LR) images, respectively. The result shows that the difference value HS between HR image and super resolution (SR) image is much smaller than the one HL between HR image and LR image, which demonstrated that our proposed DL-based super-resolution approach was effective to enhance the result accuracy of the coarse mesh model. Besides, both the increased Peak Signal-to-Nosie Ratio (PSNR) value and Structural Similarity Index (SSIM) value indicated that the quality of the images was also improved through the super-resolution approach.
{"title":"Deep Learning-Based Super-Resolution for the Finite Element Analysis of Additive Manufacturing Process","authors":"Yi Zhang, E. Freeman","doi":"10.1115/msec2022-79992","DOIUrl":"https://doi.org/10.1115/msec2022-79992","url":null,"abstract":"\u0000 Finite element analysis (FEA) of fused deposition modeling (FDM) has recently been recognized in additive manufacturing (AM) for predictions in temperature gradient of three-dimensions (3D) printed components. These predictions can be invaluable for making corrections to the printing process to improve quality of printed components. However, FEA has its limitations. For example, models with fine mesh (small element size) yield more accurate results than ones with coarse mesh (large element size). Comparing with the coarse mesh model, a fine mesh model can take considerably longer computational times and discourages most manufacturers from using FEA. In this work, an innovative deep-learning (DL) based super-resolution approach is used to improve the result accuracy of a coarse mesh model to the higher accuracy level of a fine mesh model and reduce the computational time. The element in the FEA was treated as the physical pixel in an image, so the fine temperature grid and coarse temperature grid in the FEA were analogous to high resolution (HR) images and low resolution (LR) images, respectively. The result shows that the difference value HS between HR image and super resolution (SR) image is much smaller than the one HL between HR image and LR image, which demonstrated that our proposed DL-based super-resolution approach was effective to enhance the result accuracy of the coarse mesh model. Besides, both the increased Peak Signal-to-Nosie Ratio (PSNR) value and Structural Similarity Index (SSIM) value indicated that the quality of the images was also improved through the super-resolution approach.","PeriodicalId":45459,"journal":{"name":"Journal of Micro and Nano-Manufacturing","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89534887","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}
As a facile and versatile additive manufacturing technology, direct ink writing (DIW) has attracted considerable interest in academia and industry to fabricate three-dimensional structures with unique properties and functionalities. However, so far, the physical phenomena during the DIW process are not revealed in detail, leaving a research gap between the physical experiments and the underlying theories. Here, we presented a comprehensive simulation study of non-Newtonian ink flow during the DIW process. We used the computational fluid dynamics (CFD) method and revealed the shear-thinning behavior during the extrusion process. Different nozzle geometry models were adopted in the simulation. The advantages and drawbacks of each syringe-nozzle geometry were analyzed. In addition, the ink shear stress and velocity fields were investigated and compared in the case studies. Based on these investigations and analysis, we proposed an improved syringe-nozzle geometry towards high-resolution DIW. Consequently, the high-resolution and high shape fidelity DIW could enhance the DIW product performance. The results developed in this work offer valuable guidelines and could accelerate further advancement of DIW.
{"title":"Analytical Study of Shear-Thinning Fluid Flow in Direct Ink Writing Process","authors":"Zipeng Guo, F. Fei, Xuan Song, Chi Zhou","doi":"10.1115/msec2022-85747","DOIUrl":"https://doi.org/10.1115/msec2022-85747","url":null,"abstract":"\u0000 As a facile and versatile additive manufacturing technology, direct ink writing (DIW) has attracted considerable interest in academia and industry to fabricate three-dimensional structures with unique properties and functionalities. However, so far, the physical phenomena during the DIW process are not revealed in detail, leaving a research gap between the physical experiments and the underlying theories. Here, we presented a comprehensive simulation study of non-Newtonian ink flow during the DIW process. We used the computational fluid dynamics (CFD) method and revealed the shear-thinning behavior during the extrusion process. Different nozzle geometry models were adopted in the simulation. The advantages and drawbacks of each syringe-nozzle geometry were analyzed. In addition, the ink shear stress and velocity fields were investigated and compared in the case studies. Based on these investigations and analysis, we proposed an improved syringe-nozzle geometry towards high-resolution DIW. Consequently, the high-resolution and high shape fidelity DIW could enhance the DIW product performance. The results developed in this work offer valuable guidelines and could accelerate further advancement of DIW.","PeriodicalId":45459,"journal":{"name":"Journal of Micro and Nano-Manufacturing","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87494558","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}
Chih-yu Chen, Leonard Freißmuth, Suat Mert Altug, D. Colin, Matthias Feuchtgruber, K. Drechsler
Fused filament fabrication (FFF), a type of extrusion-based additive manufacturing method, has proven its suitability for the production of highly complex components without costly tooling. However, traditional FFF systems are restricted to planar layer deposition, which results in poor surface smoothness and a reduction in strength and stiffness along the layer-stacking direction. Recent advancements in the FFF process have made it possible to reinforce and strengthen the printed parts with continuous fibers, which significantly increases the material’s anisotropy. Therefore, non-planar printing is necessary to optimize the anisotropic material behavior. This paper proposes a non-planar slicing method for optimizing the performance of continuous fiber-reinforced FFF parts printed using a 6-DOF industrial robot. The computational framework allows for the deposition of material on non-planar surfaces along the direction of the largest principal stress obtained from a finite element analysis following topology optimization. Three parts were successfully sliced and printed in a non-planar manner to generate stress-oriented toolpaths for continuous fiber-reinforced FFF using a 6-DOF robotic arm.
{"title":"Non-Planar Slicing Method for Maximizing the Anisotropic Behavior of Continuous Fiber-Reinforced Fused Filament Fabricated Parts","authors":"Chih-yu Chen, Leonard Freißmuth, Suat Mert Altug, D. Colin, Matthias Feuchtgruber, K. Drechsler","doi":"10.1115/msec2022-78670","DOIUrl":"https://doi.org/10.1115/msec2022-78670","url":null,"abstract":"\u0000 Fused filament fabrication (FFF), a type of extrusion-based additive manufacturing method, has proven its suitability for the production of highly complex components without costly tooling. However, traditional FFF systems are restricted to planar layer deposition, which results in poor surface smoothness and a reduction in strength and stiffness along the layer-stacking direction. Recent advancements in the FFF process have made it possible to reinforce and strengthen the printed parts with continuous fibers, which significantly increases the material’s anisotropy. Therefore, non-planar printing is necessary to optimize the anisotropic material behavior. This paper proposes a non-planar slicing method for optimizing the performance of continuous fiber-reinforced FFF parts printed using a 6-DOF industrial robot. The computational framework allows for the deposition of material on non-planar surfaces along the direction of the largest principal stress obtained from a finite element analysis following topology optimization. Three parts were successfully sliced and printed in a non-planar manner to generate stress-oriented toolpaths for continuous fiber-reinforced FFF using a 6-DOF robotic arm.","PeriodicalId":45459,"journal":{"name":"Journal of Micro and Nano-Manufacturing","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83695487","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}
Andrew S. Nimon, A. Sherehiy, Moath H. A. Alqatamin, Danming Wei, D. Popa
The placement of SMD components is usually performed with Cartesian type robots, a task known as pick-and-place (P&P). Small Selective Compliance Articulated Robot Arm (SCARA) robots are also growing in popularity for this use because of their quick and accurate performance. This paper describes the use of the Lean Robotic Micromanufacturing (LRM) framework applied on a large, 10kg payload, industrial SCARA robot for PCB assembly. The LRM framework guided the precision evaluation of the PCB assembly process and provided a prediction of the placement precision and yield. We experimentally evaluated the repeatability of the system, as well as the resulting collective errors during the assembly. Results confirm that the P&P task can achieve the required assembly tolerance of 200 microns without employing closed-loop visual servoing, therefore considerably decreasing the system complexity and assembly time.
{"title":"Precision Evaluation of Large Payload SCARA Robot for PCB Assembly","authors":"Andrew S. Nimon, A. Sherehiy, Moath H. A. Alqatamin, Danming Wei, D. Popa","doi":"10.1115/msec2022-85534","DOIUrl":"https://doi.org/10.1115/msec2022-85534","url":null,"abstract":"\u0000 The placement of SMD components is usually performed with Cartesian type robots, a task known as pick-and-place (P&P). Small Selective Compliance Articulated Robot Arm (SCARA) robots are also growing in popularity for this use because of their quick and accurate performance. This paper describes the use of the Lean Robotic Micromanufacturing (LRM) framework applied on a large, 10kg payload, industrial SCARA robot for PCB assembly. The LRM framework guided the precision evaluation of the PCB assembly process and provided a prediction of the placement precision and yield. We experimentally evaluated the repeatability of the system, as well as the resulting collective errors during the assembly. Results confirm that the P&P task can achieve the required assembly tolerance of 200 microns without employing closed-loop visual servoing, therefore considerably decreasing the system complexity and assembly time.","PeriodicalId":45459,"journal":{"name":"Journal of Micro and Nano-Manufacturing","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86907655","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}
Metal additive manufacturing paves the way for industries to create new applications through unique design capabilities. The powder bed fusion process is one among many metal additive manufacturing technologies that are commercially successful. Despite its numerous advantages and application in various fields, defects may occur during processing, which causes premature failure of components. Distortion is one of the major defects, and it depends on process settings, geometry, and orientation related. These distortions and dimensional deviations should be predicted faster for part qualification for many industrial applications. This work attempts to predict distortions based on shape descriptors to address this issue. Shape descriptors are definitions used to identify the details of the shape of a model to be printed. It can be either two dimensional or three dimensional. In this work, 2D shape descriptors are selected for analysis. These 2D shape descriptors can help identify how the design features significantly affect the part distortion in the PBF process. In this work, a few 2D shape descriptors are defined and modelled as a design feature to achieve the objective. Then the respective models are subjected to distortion analysis. The relationship between shape descriptors and distortion are studied through inherent strain method based simulation of distortion. It is observed from the results that most shape descriptors defined in this work can be used to predict the distortion. This work serves as a base and can help create knowledge for proposing design guidelines for the metal powder bed fusion process and helps in redesigning to prevent distortions.
{"title":"Assessment of Shape Descriptors for Distortion Prediction in Powder Bed Fusion Process","authors":"Hemnath Anandan Kumar, S. Kumaraguru","doi":"10.1115/msec2022-86089","DOIUrl":"https://doi.org/10.1115/msec2022-86089","url":null,"abstract":"\u0000 Metal additive manufacturing paves the way for industries to create new applications through unique design capabilities. The powder bed fusion process is one among many metal additive manufacturing technologies that are commercially successful. Despite its numerous advantages and application in various fields, defects may occur during processing, which causes premature failure of components. Distortion is one of the major defects, and it depends on process settings, geometry, and orientation related. These distortions and dimensional deviations should be predicted faster for part qualification for many industrial applications. This work attempts to predict distortions based on shape descriptors to address this issue. Shape descriptors are definitions used to identify the details of the shape of a model to be printed. It can be either two dimensional or three dimensional. In this work, 2D shape descriptors are selected for analysis. These 2D shape descriptors can help identify how the design features significantly affect the part distortion in the PBF process. In this work, a few 2D shape descriptors are defined and modelled as a design feature to achieve the objective. Then the respective models are subjected to distortion analysis. The relationship between shape descriptors and distortion are studied through inherent strain method based simulation of distortion. It is observed from the results that most shape descriptors defined in this work can be used to predict the distortion. This work serves as a base and can help create knowledge for proposing design guidelines for the metal powder bed fusion process and helps in redesigning to prevent distortions.","PeriodicalId":45459,"journal":{"name":"Journal of Micro and Nano-Manufacturing","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85594678","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}
Disassembly is an integral part of maintenance, upgrade, and remanufacturing operations to recover end-of-use products. Optimization of disassembly sequences and the capability of robotic technology are crucial for managing the resource-intensive nature of dismantling operations. This study proposes an optimization framework for disassembly sequence planning under uncertainty considering human-robot collaboration. The proposed model combines three attributes: disassembly cost, disassembleability, and safety, to find the optimal path for dismantling a product and assigning each disassembly operation among humans and robots. The multi-attribute utility function has been employed to address uncertainty and make a tradeoff among multiple attributes. The disassembly time reflects the cost of disassembly and is assumed to be an uncertain parameter with a Beta probability density function; the disassembleability evaluates the feasibility of conducting operations by robot; finally, the safety index ensures the safety of human workers in the work environment. The optimization model identifies the best disassembly sequence and makes tradeoffs among multi-attributes. An example of a computer desktop illustrates how the proposed model works. The model identifies the optimal disassembly sequence with less disassembly cost, high disassembleability, and increased safety index while allocating disassembly operations between human and robot. A sensitivity analysis is conducted to show the model’s performance when changing the disassembly cost for the robot.
{"title":"Optimization-Based Disassembly Sequence Planning Under Uncertainty for Human-Robot Collaboration","authors":"Hao-yu Liao, Yuhao Chen, Boyi Hu, S. Behdad","doi":"10.1115/msec2022-85383","DOIUrl":"https://doi.org/10.1115/msec2022-85383","url":null,"abstract":"\u0000 Disassembly is an integral part of maintenance, upgrade, and remanufacturing operations to recover end-of-use products. Optimization of disassembly sequences and the capability of robotic technology are crucial for managing the resource-intensive nature of dismantling operations. This study proposes an optimization framework for disassembly sequence planning under uncertainty considering human-robot collaboration. The proposed model combines three attributes: disassembly cost, disassembleability, and safety, to find the optimal path for dismantling a product and assigning each disassembly operation among humans and robots. The multi-attribute utility function has been employed to address uncertainty and make a tradeoff among multiple attributes. The disassembly time reflects the cost of disassembly and is assumed to be an uncertain parameter with a Beta probability density function; the disassembleability evaluates the feasibility of conducting operations by robot; finally, the safety index ensures the safety of human workers in the work environment. The optimization model identifies the best disassembly sequence and makes tradeoffs among multi-attributes. An example of a computer desktop illustrates how the proposed model works. The model identifies the optimal disassembly sequence with less disassembly cost, high disassembleability, and increased safety index while allocating disassembly operations between human and robot. A sensitivity analysis is conducted to show the model’s performance when changing the disassembly cost for the robot.","PeriodicalId":45459,"journal":{"name":"Journal of Micro and Nano-Manufacturing","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80822451","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 the past decade, nanoporous metals have been a point of interest in the scientific community because they exhibit chemical, optical, and mechanical properties that are unique from their bulk counterparts. One of the most prominent method for its synthesis is chemical dealloying. While, under electrolytic conditions, dealloying can use process-inputs such as current density and electrical potential to control the ligament size during its synthesis with excellent reproducibility, electroless methods are plagued by the lack of local control of dealloying rates which introduces batch-to-batch variations in ligament size. Given that powder is a format incompatible with electrolysis, this study shows an approach to safely scale fabrication of spherical porous copper powders containing oxides from gas atomized Cu-Al powders. Additionally, the agglomeration that is commonly associated with porous powder fabrication was addressed by its functionalization with an anionic surfactant and powder washing in both deionized water (polar) and anhydrous ethanol (nonpolar). Additionally, hazards associated with its production scaling such as excessive hydrogen evolution, heat generation due to its high-reactivity and exothermic reaction and pyrophoricity are discussed and addressed. As a result of this study, a robust and scalable approach was developed to produce 100 of grams of porous metal powders.
{"title":"Robust and Scalable Synthesis of High Surface Area Porous Copper Spheriodized Powders by Electroless Chemical Dealloying","authors":"S. Niauzorau, N. Kublik, A. Hasib, B. Azeredo","doi":"10.1115/msec2022-85894","DOIUrl":"https://doi.org/10.1115/msec2022-85894","url":null,"abstract":"\u0000 In the past decade, nanoporous metals have been a point of interest in the scientific community because they exhibit chemical, optical, and mechanical properties that are unique from their bulk counterparts. One of the most prominent method for its synthesis is chemical dealloying. While, under electrolytic conditions, dealloying can use process-inputs such as current density and electrical potential to control the ligament size during its synthesis with excellent reproducibility, electroless methods are plagued by the lack of local control of dealloying rates which introduces batch-to-batch variations in ligament size. Given that powder is a format incompatible with electrolysis, this study shows an approach to safely scale fabrication of spherical porous copper powders containing oxides from gas atomized Cu-Al powders. Additionally, the agglomeration that is commonly associated with porous powder fabrication was addressed by its functionalization with an anionic surfactant and powder washing in both deionized water (polar) and anhydrous ethanol (nonpolar). Additionally, hazards associated with its production scaling such as excessive hydrogen evolution, heat generation due to its high-reactivity and exothermic reaction and pyrophoricity are discussed and addressed. As a result of this study, a robust and scalable approach was developed to produce 100 of grams of porous metal powders.","PeriodicalId":45459,"journal":{"name":"Journal of Micro and Nano-Manufacturing","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82821180","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}
While reactor wall preconditioning was previously shown to influence the growth of carbon nanotubes (CNTs) by chemical vapor deposition (CVD), it was previously only limited to studying the accumulating carbon deposits over the history of a large number of growth runs. However, the effect of leaving the reactor walls for an extended period of time between growth runs was not previously systematically studied. Here, we combine experimental measurements with a mathematical model to investigate the effect of thermochemical history of reactor walls on growth yield of vertically aligned CNT forests. Importantly, we demonstrate unexpectedly high CNT yield, exceeding one-order-of-magnitude taller forests, by increasing the interim period between runs (IPBR). We explain the results based on previously unexplored process sensitivity to trace amounts of oxygen-containing species in the reactor. In particular, uncontrolled amounts of water vapor desorbing from reactor walls during growth are modelled in this work. Our modeling results show the effect of IPBR on the outgassing dynamics revealing the underlying mechanism of generating growth promoting molecules during growth. By installing a new humidity sensor in our multizone rapid thermal CVD reactor, we are able to uniquely correlate the amount of moisture within the reactor to real-time measurements of growth kinetics, as well as ex situ characterization of CNT alignment and atomic defects. Our findings enable a scientifically grounded approach toward both boosting growth yield and improving its consistency by reducing run-to-run variations. Accordingly, engineered growth recipes can be envisioned to leverage this effect for improving manufacturing process scalability and robustness.
{"title":"Unexpectedly High Yields in Chemical Vapor Deposition of Carbon Nanotubes Based on Reactor Wall Thermochemical History","authors":"G. Tomaraei, Moataz Abdulhafez, M. Bedewy","doi":"10.1115/msec2022-85633","DOIUrl":"https://doi.org/10.1115/msec2022-85633","url":null,"abstract":"\u0000 While reactor wall preconditioning was previously shown to influence the growth of carbon nanotubes (CNTs) by chemical vapor deposition (CVD), it was previously only limited to studying the accumulating carbon deposits over the history of a large number of growth runs. However, the effect of leaving the reactor walls for an extended period of time between growth runs was not previously systematically studied. Here, we combine experimental measurements with a mathematical model to investigate the effect of thermochemical history of reactor walls on growth yield of vertically aligned CNT forests. Importantly, we demonstrate unexpectedly high CNT yield, exceeding one-order-of-magnitude taller forests, by increasing the interim period between runs (IPBR). We explain the results based on previously unexplored process sensitivity to trace amounts of oxygen-containing species in the reactor. In particular, uncontrolled amounts of water vapor desorbing from reactor walls during growth are modelled in this work. Our modeling results show the effect of IPBR on the outgassing dynamics revealing the underlying mechanism of generating growth promoting molecules during growth. By installing a new humidity sensor in our multizone rapid thermal CVD reactor, we are able to uniquely correlate the amount of moisture within the reactor to real-time measurements of growth kinetics, as well as ex situ characterization of CNT alignment and atomic defects. Our findings enable a scientifically grounded approach toward both boosting growth yield and improving its consistency by reducing run-to-run variations. Accordingly, engineered growth recipes can be envisioned to leverage this effect for improving manufacturing process scalability and robustness.","PeriodicalId":45459,"journal":{"name":"Journal of Micro and Nano-Manufacturing","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91020899","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}
Qingqing He, Brandon Bethers, Brian Tran, Yang Yang
Certain types of Salvinia water ferns present a highly water-repellent upper surface along their floating leaves. This is accomplished through the use of structured trichomes, which create hydrophobic and superhydrophobic surfaces. Particularly, there are four different types of trichomes found in Salvinia plants that present these characteristics. They are known as Cucullata type, Oblongifolia type, Natans type and Molesta type. However, these structures are characterized by very small sizes, along with complex shapes. With the advantages of high-efficiency, low-cost, fast-fabrication, and ability of producing microstructures, additive manufacturing (AM), known as 3D printing method, has brought lots of attentions to various academic fields. Herein, we apply a 3D printing method to create biomimetic structures designed after the trichomes on Salvinia. In this work, the hydrophobic properties of the four biomimetic structures were tested through the use of optical contact angle measurements after initial modeling through the CAD program Solidworks. Finally, an Optical Contact Angle measurement device was used to determine the hydrophobic properties of each structure. This study concludes that each of the four biomimetic structures based on the different types of trichomes of Salvinia have hydrophobic performance. In particular, the Natans type and Molesta type show superhydrophobic properties, with the Molesta inspired structure displaying the highest contact angle among the four types. These results suggest that future research into the trichome structures of Salvinia water ferns could produce biomimetic structures with enhanced hydrophobic properties and applications.
{"title":"3D Printing of Salvinia Water Fern-Inspired Superhydrophobic Structures","authors":"Qingqing He, Brandon Bethers, Brian Tran, Yang Yang","doi":"10.1115/msec2022-85646","DOIUrl":"https://doi.org/10.1115/msec2022-85646","url":null,"abstract":"\u0000 Certain types of Salvinia water ferns present a highly water-repellent upper surface along their floating leaves. This is accomplished through the use of structured trichomes, which create hydrophobic and superhydrophobic surfaces. Particularly, there are four different types of trichomes found in Salvinia plants that present these characteristics. They are known as Cucullata type, Oblongifolia type, Natans type and Molesta type. However, these structures are characterized by very small sizes, along with complex shapes. With the advantages of high-efficiency, low-cost, fast-fabrication, and ability of producing microstructures, additive manufacturing (AM), known as 3D printing method, has brought lots of attentions to various academic fields. Herein, we apply a 3D printing method to create biomimetic structures designed after the trichomes on Salvinia. In this work, the hydrophobic properties of the four biomimetic structures were tested through the use of optical contact angle measurements after initial modeling through the CAD program Solidworks. Finally, an Optical Contact Angle measurement device was used to determine the hydrophobic properties of each structure. This study concludes that each of the four biomimetic structures based on the different types of trichomes of Salvinia have hydrophobic performance. In particular, the Natans type and Molesta type show superhydrophobic properties, with the Molesta inspired structure displaying the highest contact angle among the four types. These results suggest that future research into the trichome structures of Salvinia water ferns could produce biomimetic structures with enhanced hydrophobic properties and applications.","PeriodicalId":45459,"journal":{"name":"Journal of Micro and Nano-Manufacturing","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78228110","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}
A. Alafaghani, Majed Ali, Abdalmageed Almotari, Jian-Qiao Sun, A. Qattawi
Due to the layering nature of additive manufacturing, additively manufactured parts exhibit a unique microstructure and are more susceptible to defects. Post-processing heat treatments of additively manufactured parts have shown great promise in improving their quality and reliability. However, the previous studies presented here demonstrated that additively manufactured parts respond to heat treatments differently compared to their traditional counterparts. This demonstrates a need for models that can predict the influence of different heat treatments on the mechanical behavior of additively manufactured parts. A hybrid approach between data-driven and physically informed models was adopted to model the influence of post-processing heat treatments on the strengthening mechanisms of additively manufactured Inconel 718. This work focuses on Inconel 718 for its common use in additive manufacturing and because it is one of the most studied additively manufactured alloys which resulted in producing more data that can be used to model its behavior.
{"title":"Hybrid Modeling the Influence of Post Processing Heat Treatments on the Strengthening Mechanisms of Additively Manufactured Inconel 718","authors":"A. Alafaghani, Majed Ali, Abdalmageed Almotari, Jian-Qiao Sun, A. Qattawi","doi":"10.1115/msec2022-86354","DOIUrl":"https://doi.org/10.1115/msec2022-86354","url":null,"abstract":"\u0000 Due to the layering nature of additive manufacturing, additively manufactured parts exhibit a unique microstructure and are more susceptible to defects. Post-processing heat treatments of additively manufactured parts have shown great promise in improving their quality and reliability. However, the previous studies presented here demonstrated that additively manufactured parts respond to heat treatments differently compared to their traditional counterparts. This demonstrates a need for models that can predict the influence of different heat treatments on the mechanical behavior of additively manufactured parts. A hybrid approach between data-driven and physically informed models was adopted to model the influence of post-processing heat treatments on the strengthening mechanisms of additively manufactured Inconel 718. This work focuses on Inconel 718 for its common use in additive manufacturing and because it is one of the most studied additively manufactured alloys which resulted in producing more data that can be used to model its behavior.","PeriodicalId":45459,"journal":{"name":"Journal of Micro and Nano-Manufacturing","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78606718","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：