Effect of multi-infill patterns on tensile behavior of FDM printed parts

Dave, Harshit K.; Patel, Brijesh H.; Rajpurohit, Shilpesh R.; Prajapati, Ashish R.; Nedelcu, Dumitru

doi:10.1007/s40430-020-02742-3

Cited by 26 publications

(14 citation statements)

References 37 publications

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“…Fused deposition modeling (FDM) is one of the most popular and used technique in additive manufacturing (AM) due to its simple operation, low cost, and variety of materials [ 1 , 2 , 3 , 4 ]. The FDM method is popular due to its highly flexibility and process speed, but the durability of the printed material is the major concern, especially for products applicated in an outdoor environment [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

An Investigation on the Hardness of Polylactic Acid Parts Fabricated via Fused Deposition Modeling

et al. 2022

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This paper investigated the hardness property of the fused deposition modeling (FDM)-printed PLA samples via different process parameters of printing and raster angles. The hardness data were sampled from the flat and edge surfaces of the samples. In addition, the effect of hardness characters after the ultraviolet (UV) curing process was analyzed. Furthermore, this research found that the printing and raster angles significantly affected the hardness value of the PLA part, which slightly increased after the UV irradiation. Moreover, the results of this study will provide a reference for the field of FDM application.

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Section: Introductionmentioning

confidence: 99%

An Investigation on the Hardness of Polylactic Acid Parts Fabricated via Fused Deposition Modeling

et al. 2022

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“…Results of the study revealed that there is strong dependency of printing direction on material properties. Dave et al [ 11 ] investigated the tensile behavior of FDM produced 3D parts considering multi‐infill patterns with various infill density and raster orientations.…”

Section: Literature Summarymentioning

confidence: 99%

Influence of process parameters on mechanical strength, build time, and material consumption of 3D printed polylactic acid parts

et al. 2022

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The purpose of the study is to examine and analyze the effect of different process parameters of fused deposition modeling process, namely, nozzle diameter, build orientation, infill density, raster pattern, layer height, and print speed on the response variables, namely, tensile strength, build time, and material consumption. Taguchi L 27 orthogonal array experimental design has been adopted to print the PLA samples for experimental investigation. The optimum values of process parameters are obtained from the signal-to-noise ratio values of responses. The performed confirmation tests revealed that optimum process parameters combination determined through Taguchi method improves the tensile strength, reduce the build time, and material consumption significantly. Confirmation from analysis of variance (ANOVA) results revealed that the build orientation and nozzle diameter are the most influential process parameters for all the considered responses. The infill density is found to be an influential process parameter for material consumption. Further, the developed regression models for prediction of response characteristics and normal probability plots show significance of coefficients for predicted models. Results from the confirmation test revealed that the PLA part printed with optimum settings ((Nd) 3 -(Bo) 3 -(Id) 3 -(Rp) 1 -(Lh) 2 -(Ps) 1 )) for tensile strength shows maximum tensile strength of 61.24 MPa with an improvement of 6.26%. Similarly, the part printed with the optimum settings ((Nd) 3 -(Bo) 1 -(Id) 2 -(Rp) 1 -(Lh) 3 -(Ps) 3 ) for build time takes 0.32 h of time with a reduction of 8.57%. The part printed with the optimum settings ((Nd) 1 -(Bo) 1 -(Id) 1 -(Rp) 2 -(Lh) 1 -(Ps) 2 ) for material consumption consumes material of 7.6 g with a reduction of 2.56%. Fracture mechanics results depict that variation of process parameters during the fabrication of samples has significant influence on the tensile strength.

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“…Results of the study revealed that there is strong dependency of printing direction on material properties. Dave et al [ 31 ] investigated the tensile behavior of FDM printed parts considering multi‐infill patterns with different infill densities and the raster orientations. The study concluded that at raster orientation at 0°, lower tensile strength had been observed as compared to the raster orientation at 45°.…”

Section: Literature Summarymentioning

confidence: 99%

Development of artificial intelligence‐based neural network prediction model for responses of additive manufactured polylactic acid parts

et al. 2022

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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.

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Effect of multi-infill patterns on tensile behavior of FDM printed parts

Cited by 26 publications

References 37 publications

An Investigation on the Hardness of Polylactic Acid Parts Fabricated via Fused Deposition Modeling

An Investigation on the Hardness of Polylactic Acid Parts Fabricated via Fused Deposition Modeling

Influence of process parameters on mechanical strength, build time, and material consumption of 3D printed polylactic acid parts

Development of artificial intelligence‐based neural network prediction model for responses of additive manufactured polylactic acid parts

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