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• W4309269745 abstract "In the material extrusion (MEX) Additive Manufacturing (AM) technology, the layer-by-layer nature of the fabricated parts, induces specific features which affect their quality and may restrict their operating performance. Critical quality indicators with distinct technological and industrial impact are surface roughness, dimensional accuracy, and porosity, among others. Their achieving scores can be optimized by adjusting the 3D printing process parameters. The effect of six (6) 3D printing control parameters, i.e., raster deposition angle, infill density, nozzle temperature, bed temperature, printing speed, and layer thickness, on the aforementioned quality indicators is investigated herein. Optical Microscopy, Optical Profilometry, and Micro Χ-Ray Computed Tomography were employed to investigate and document these quality characteristics. Experimental data were processed with Robust Design Theory. An L25 Taguchi orthogonal array (twenty-five runs) was compiled, for the six control parameters with five levels for each one of them. The predictive quadratic regression models were then validated with two additional confirmation runs, with five replicas each. For the first time, the surface quality features, as well as the geometrical and structural characteristics were investigated in such depth (>500 GB of raw experimental data were produced and processed). A deep insight into the quality of the MEX 3D printed parts is provided allowing the control parameters’ ranking and optimization. Prediction equations for the quality features as functions of the control parameters are introduced herein, with merit in the market-driven practice." @default.
• W4309269745 created "2022-11-25" @default.
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• W4309269745 date "2022-11-01" @default.
• W4309269745 modified "2023-09-25" @default.
• W4309269745 title "The effect of six key process control parameters on the surface roughness, dimensional accuracy, and porosity in material extrusion 3D printing of polylactic acid: Prediction models and optimization supported by robust design analysis" @default.
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• W4309269745 doi "https://doi.org/10.1016/j.aime.2022.100104" @default.
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