Abstract
Fused Deposition Modeling (FDM) is universally used 3D printing technology, to manufacture prototypes as well functional parts due to its capability to create components having any geometric complexity in shorter duration, without any specific tooling requirement or human intervention. FDM fabricated parts have found many promising application in various industries such as aerospace, automobile, medical, customizable products etc. However, the application of FDM parts has been restricted by poor mechanical performance. The mechanical properties of the FDM fabricated part are largely affected by selection of various build parameters. Optimal selection of various build parameters can help to achieve better mechanical strength. The Adaptive network-based a fuzzy Interference System (ANFIS) is uses both neural networks and fuzzy logic to generate a mapping between inputs and response. In ANFIS, the parameters for fuzzy system has been identifying using a neural network. Hybrid learning rule can be used for creating a fuzzy set of IF-THEN rules with the appropriate membership functions and generating previously defined Input/Outputs pairs. Initially, a detailed experimental investigation was conducted to understand the impact of different build parameters on the tensile strength of printed PLA. Using experimental data, an optimized model of ANFIS was developed to anticipate the tensile strength of printed parts.
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Rajpurohit, S.R., Dave, H.K. (2020). Prediction and Optimization of Tensile Strength in FDM Based 3D Printing Using ANFIS. In: Gupta, K., Gupta, M. (eds) Optimization of Manufacturing Processes. Springer Series in Advanced Manufacturing. Springer, Cham. https://doi.org/10.1007/978-3-030-19638-7_5
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DOI: https://doi.org/10.1007/978-3-030-19638-7_5
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