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- W4284892545 abstract "Abstract Fused deposition modeling (FDM) is one of the most economical and popular technology amongst numerous additive manufacturing techniques. The quality of FDM fabricated parts is highly sensitive to the production parameters. Thus, in the present work, an investigation on the FDM printed polylactic acid parts has been performed considering six printing process parameters, that is, nozzle diameter, build orientation, raster pattern, layer height and print speed to develop the feedforward backpropagation (FFBP) artificial neural network prediction model for the prediction of responses, namely, tensile strength, material consumption, build time and surface quality. Tensile specimens as per L 27 orthogonal array are printed considering the various combination of parameters. The printed samples have been subjected to tensile strength testing, surface roughness measurement, build time recording, and material consumption evaluation. The highest tensile strength of 57.633 MPa, lowest surface roughness of 1.71 μm, lowest build time of 0.35 h and lowest material consumption of 7.8 g are observed. The experimental results have been used to develop the artificial intelligence‐based prediction model through FFBP algorithm and sigmoid transfer function to predict the responses. The best performance of the developed neural network with R 2 for testing (0.99343), training (0.99366), and validation (0.99372) of data is recorded for prediction of responses with minimum percentage error. The study concluded that developed model is capable of predicting the responses of FDM process according to the input process parameters." @default.
- W4284892545 created "2022-07-09" @default.
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- W4284892545 date "2022-07-07" @default.
- W4284892545 modified "2023-10-16" @default.
- W4284892545 title "Development of artificial intelligence‐based neural network prediction model for responses of additive manufactured polylactic acid parts" @default.
- W4284892545 cites W1964000165 @default.
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- W4284892545 cites W2038659831 @default.
- W4284892545 cites W2055322620 @default.
- W4284892545 cites W2059405114 @default.
- W4284892545 cites W2071083599 @default.
- W4284892545 cites W2077458446 @default.
- W4284892545 cites W2093500299 @default.
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- W4284892545 cites W2161667240 @default.
- W4284892545 cites W2194311219 @default.
- W4284892545 cites W2495127294 @default.
- W4284892545 cites W2561027720 @default.
- W4284892545 cites W2580709916 @default.
- W4284892545 cites W2592516380 @default.
- W4284892545 cites W2595589086 @default.
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- W4284892545 doi "https://doi.org/10.1002/pc.26876" @default.
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