Fused Deposition Modeling (FDM - a technology of additive manufacturing) parts entail a certain amount of ambiguity in terms of its material properties and microstructure due to its manufacturing technique. Therefore, an FDM part differs from its design model in terms of strength and stiffness. With an increasing amount of FDM parts being used as end use products, it is necessary to simulate and analyze them. Due to the differences in microstructure and material properties of FDM parts, it is necessary to determine the accuracy of analysis methods like Finite Element Analysis (FEA) while analyzing the non-continuous, non-linear FDM parts. The goal of this study is to compare FEA simulations of the as-built geometries with the experimental tests of actual FDM parts. A dogbone geometry with different infill patterns is tested under tensile loading. Further, as-built 3D models are simulated using FEA and the stress results are compared with experimental data. This study found that FEA results are not always an accurate or reliable means of predicting FDM part behaviors.
Parts built using Fused Deposition Modeling (FDM -an additive manufacturing technology) differ from their design model in terms of their microstructure and material properties. These differences lead to a certain amount of ambiguity regarding the structure, strength and stiffness of the nal FDM part.Increasing use of FDM parts as end use products, necessitates accurate simulations and analyses during part design. However, analysis methods such as Finite Element Analysis, are used for analysis of continuum models, and may not accurately represent the non-continuous non-linear FDM parts. Therefore, it is necessary to determine the accuracy and precision of FEA for FDM parts. The goal of this study is to compare FEA simulations of the as-built geometries with the experimental tests of actual FDM parts. Dogbone geometries that include different in ll patterns are tested under tensile loading and later simulated using FEA. This study found that FEA results are not always an accurate or reliable means of predicting FDM part behaviors.
Parts built using Fused Deposition Modeling (FDM – an additive manufacturing technology) differ from their design model in terms of their microstructure and material properties. These differences lead to a certain amount of ambiguity regarding the structure, strength and stiffness of the final FDM part. Increasing use of FDM parts as end use products, necessitates accurate simulations and analyses during part design. However, analysis methods such as Finite Element Analysis, are used for analysis of continuum models, and may not accurately represent the non-continuous non-linear FDM parts. Therefore, it is necessary to determine the accuracy and precision of FEA for FDM parts. The goal of this study is to compare FEA simulations of the as-built geometries with the experimental tests of actual FDM parts. Dogbone geometries that include different infill patterns are tested under tensile loading and later simulated using FEA. This study found that FEA results are not always an accurate or reliable means of predicting FDM part behaviors.
Pedal Powered Clothes Washer is a low cost machine made up of easily and readily available scrap parts in daily life. It is a machine which generates power through human pedaling and with the drive mechanism, converts the pedaling motion into required rotary motion of the washing drum. Its innovation lies in its simple design, use of inexpensive parts, very low repairing and maintenance cost, affordability to each member of the society and it does not affect the environment. Our team intends to directly address the problems faced in washing clothes, and thus have developed a new design for easy effort in washing, rinsing and drying clothes. The Pedal Powered Clothes Washer is a completely new concept, which in its one laundry cycle does washing, rinsing and drying of clothes similar to that of an automatic washing machine available in the market.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.