Department of Mechanical engineering
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Item Analysis of Density of Laser Powder Bed Fusion Fabricated Part Using Decision Tree Algorithm(Springer, 2023-05) Mishra, Radha RamanAdditive manufacturing (AM) enabled manufacturing industries to fabricate metallic components with complex shapes. However, the properties of additively manufactured parts need further improvements to compete with the performance of traditionally manufactured parts. Machine learning (ML) models provide an alternative to study the correlation between the process parameters–properties of the fabricated parts. In the present work, the ML approach has been applied to understand the effect of AM process parameters on the density of additively built parts. The decision tree model was developed for the laser powder bed fusion-processed parts based on the input parameters such as laser power, scan speed, hatching space, energy density, and build rate. The model was trained and tested with experimental data obtained from the relevant literature. The process parameters were optimized to achieve the desired density of the part. A good agreement was indicated between the predicted and experimental data. The study revealed the applicability and potential of the model to determine and predict the density of the additively manufactured parts.Item Parametric Optimization of FDM Process for Fabricating High-Strength PLA Parts(Springer, 2021-02) Mishra, Radha RamanPolylactic acid (PLA) is a biodegradable polymer that can be 3D printed to develop various complex shape parts for industrial use. However, achieving higher tensile strength in 3D-printed PLA parts is challenging. In the present work, the tensile properties of 3D-printed PLA have been analyzed using the Taguchi method. The tensile samples were 3D printed using the fused deposition modeling (FDM) technology using different variable parameters—layer heights (0.2, 0.4, and 0.6 mm), nozzle speeds (5, 10, and 15 mm s−1), and infill patterns (line, zig-zag and concentric). The tensile testing was accomplished following the test standard ASTM D-638. The study revealed that the tensile strength of 3D-printed samples largely depends on the density of the samples. The tensile strength of sample 7 (layer height–0.6 mm, nozzle speed–5 mm s−1, and infill pattern—concentric) was found 54.437 MPa, which is the highest among the developed samples, whereas the extension in sample 6 (layer height–0.4 mm, nozzle speed–15 mm s−1, and infill pattern—line) was 13.19% which is the highest among all the 3D-printed samples.