Analysis of the influence of FDM parameters in the tensile strength response using machine learning

Abstract

This research presents a comprehensive experimental study on the effect of temperature, material and process parameters related to tensile strength response in 3D printing manufacturing process with ABS material. A Hyper Latin Square design was chosen for the experimental points distribution. Thirteen parameters with multiple levels are considered LAYERHEIGHT, WALLTHICKNESS, TOPBOTTOMTHICKNESS, TOPBOTTOM-LINEDIRECTION1, TOPBOTTOMLINEDIRECTION2, INFILLDENSITY, INFILLLINEDI-RECTION1, INFILLLINEDIRECTION2, PRINTSPEED, EXTRUSIONTEMP, BEDTEMP, WORKSPACETEMP and POSITION. Type IV tensile specimens are fabricated and tested with an universal testing machine. Maximum stress is measured, evaluated and analyzed in three different building positions.Machine learning algorithm with Orange Data mining software are used to study underlying relations between factors and response. Experimental results indicate that INFILLDENSITY, TOPBOTTOMTHICKNESS and INFILLLINEDIRECTION1 has a strong positive correlation with tensile strength. Meanwhile, TOPBOTTOMLINEDI-RECTION1, WORKSPACETEMPERATURE and PRINTSPEED has a negative correlation with tensile strength. Position 1 with line depositions parallel to Y axis produce the higher tensile strength response. Findings imply that machine algorithms can be used to study multiple parameters at time.