Fused deposition modelling (FDM) process parameter prediction and optimization using group method for data handling (GMDH) and differential evolution (DE)

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Optimization of fused deposition modeling process parameters: a review of current research and future prospects (2015, background): “Rayegani and Onwubolu [31] 2 full factorial design, GMDH & DE ABS Part orientation, raster angle, raster width, air gap Tensile strength All input parameters”
Comparative Study of the Sensitivity of PLA, ABS, PEEK, and PETG’s Mechanical Properties to FDM Printing Process Parameters (2021, methodology): “Rayegani and Onwubolu used a dif‐ ferential evolution approach to process parameters to optimize part’s tensile strength only [31].”
A Systematic Survey of FDM Process Parameter Optimization and Their Influence on Part Characteristics (2019, methodology): “The following is a list of 115 commonly used FDM machines: Fortus FDM 400 mc machine (Stratasys, Eden Prairie, MN, USA) 116 [10,21,22], Fortus 360 mc machine (Stratasys, Eden Prairie, USA) [23], FDM 200 mc machine 117 (Stratasys, Eden Prairie, MN, USA) [24], FDM 1650 machine (Stratasys, Eden Prairie”
Experimental validation of a numerical model for the strand shape in material extrusion additive manufacturing (2018, background): “It was found that the mechanical properties are strongly correlated to the layer thickness [17, 22], the build orientation [17, 7, 23, 24], and the infill density of the parts [17, 25, 26].”