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      Parametric Optimization of FDM Process for Improving Mechanical Strengths Using Taguchi Method and Response Surface Method: A Comparative Investigation

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      Machines
      MDPI AG

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          Abstract

          In the present study, a comparison of two widely used optimization approaches for fused deposition modeling (FDM), that is, Taguchi method in contrast with response surface method (RSM), was investigated. Four operating parameters, namely extrusion temperature, layer thickness, raster width, print speed, and their interaction terms, were identified as control variables with three levels, while tensile strength and compressive strength were selected responses. L27 orthogonal array and face-centered central composite design (FCCCD) were used for the experimental approach for Taguchi and RSM, respectively. The signal-to-noise (S/N) ratio and analysis of variance (ANOVA) were employed to find the optimal FDM parameter combination as well as the main factor that affect the performance of the PLA samples. Based on experimental results, it was observed that conclusions about significant ranking of parameters on FDM process from these two methods were different. However, both the Taguchi method and RSM succeed in predicting better results compared with the original groups. In addition, the optimum combinations for tensile strength and compressive strength obtained from the RSM were 2.11% and 8.15% higher than Taguchi method, respectively.

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          Additive manufacturing (3D printing): A review of materials, methods, applications and challenges

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            Anisotropic material properties of fused deposition modeling ABS

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              Parametric appraisal of mechanical property of fused deposition modelling processed parts

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                Author and article information

                Contributors
                Journal
                MACHCV
                Machines
                Machines
                MDPI AG
                2075-1702
                September 2022
                August 30 2022
                : 10
                : 9
                : 750
                Article
                10.3390/machines10090750
                dbcc1a40-21ee-4654-ab35-131c274e0e1d
                © 2022

                https://creativecommons.org/licenses/by/4.0/

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