Fused Deposition Modelling is one of the most widely used processes of additive manufacturing or 3D printing. The FDM process of 3D printing deposits material in the form of a continuous flow, layer-by-layer to make o...
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Fused Deposition Modelling is one of the most widely used processes of additive manufacturing or 3D printing. The FDM process of 3D printing deposits material in the form of a continuous flow, layer-by-layer to make objects. As the FDM-based products are used in various fields it becomes important to look after the mechanical aspects, part quality, and the economical aspect of FDM 3D printing and hence optimize the necessary process parameters. In this study, critical process parameters like layer thickness, air gap, raster width, build orientation, raster angle, and the number of contours is optimized for enhancing the properties of FDM printed part such as tensile strength surface roughness, and build time. The material used for 3D printing is polylactic acid (PLA). The task of training the data sets and optimizing them was accomplished by using functionapproximation of Artificial Neural Network. ANN can predict experimental data with a coefficient of correlation R = 0.9981,0.9984,0.99837 subsequently for tensile strength, Build time, and surface roughness and root mean square error as 0.5543, 0.578 and 0.241 for three outputs. Further, it is revealed that build orientation is the most important parameter for optimum results. (C) 2021 Elsevier Ltd. All rights reserved.
Fused Deposition Modelling is one of the most widely used processes of additive manufacturing or 3D printing. The FDM process of 3D printing deposits material in the form of a continuous flow, layer-by-layer to make o...
详细信息
Fused Deposition Modelling is one of the most widely used processes of additive manufacturing or 3D printing. The FDM process of 3D printing deposits material in the form of a continuous flow, layer-by-layer to make objects. As the FDM-based products are used in various fields it becomes important to look after the mechanical aspects, part quality, and the economical aspect of FDM 3D printing and hence optimize the necessary process parameters. In this study, critical process parameters like layer thickness, air gap, raster width, build orientation, raster angle, and the number of contours is optimized for enhancing the properties of FDM printed part such as tensile strength surface roughness, and build time. The material used for 3D printing is polylactic acid (PLA). The task of training the data sets and optimizing them was accomplished by using functionapproximation of Artificial Neural Network. ANN can predict experimental data with a coefficient of correlation R = 0.9981,0.9984,0.99837 subsequently for tensile strength, Build time, and surface roughness and root mean square error as 0.5543, 0.578 and 0.241 for three outputs. Further, it is revealed that build orientation is the most important parameter for optimum results.
The article proposes an optimization method based on the functionapproximation in control strategies design of medium earth orbit (MEO) and inclined geosynchronous orbit (IGSO) satellites. As an extension of the func...
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The article proposes an optimization method based on the functionapproximation in control strategies design of medium earth orbit (MEO) and inclined geosynchronous orbit (IGSO) satellites. As an extension of the functional approximationmethod (FAM), this method is suitable to solve a single-variable or a multivariable optimization question with equality or inequality constraints. This ensures that the optimal control strategies for MEO and IGSO satellites to manoeuvre along the ideal control arc can be easily determined, and finally make satellites enter the designed orbits as soon as possible after satellites being launched under restrictions of the limited propellant and number of revolutions around the earth. In the current article, the basic FAM model is first introduced, and then the method applications and the simulation results are discussed in detail. Compared with the conventionally adopted exhaust search in the process of the optimal strategy design for the MEO and IGSO satellites, this method has the advantages of simplicity, less dependence on the initial parameter range, and requires much less computational effort.
When applying reinforcement learning algorithms such as Q-learning to real world problems, we must consider the high and redundant dimensions and continuity of the state-action space. The continuity of state-action sp...
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ISBN:
(纸本)9781479959556
When applying reinforcement learning algorithms such as Q-learning to real world problems, we must consider the high and redundant dimensions and continuity of the state-action space. The continuity of state-action space is often treated by value functionapproximation. However, conventional function approximators such as radial basis function networks (RBFNs) are unsuitable in these environments, because they incur high computational cost, and the number of required experiences grows exponentially with the dimension of the state-action space. By contrast, selective desensitization neural network (SDNN) is highly robust to redundant inputs and computes at low computational cost. This paper proposes a novel function approximation method for Q-learning in continuous state-action space based on SDNN. The proposed method is evaluated by numerical experiments with redundant input(s). These experimental results validate the robustness of the proposed method to redundant state dimensions, and its lower computational cost than RBFN. These properties are advantageous to real-world applications such as robotic systems.
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