Exploring the impact of 3D printing parameters on mechanical properties of ABS and PLA materials through machine learning analysis

Abstract

Objective: 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.
 Methodology or method: A Hyper Latin Square design was chosen for the experimental points’ distribution. Thirteen parameters with multiple levels are considered LAYERHEIGHT, WALLTHICKNESS, TOPBOT-TOMTHICKNESS, TOPBOTTOMLINEDIRECTION1, TOPBOTTOMLINEDIRECTION2, INFILLDENSITY, INFILLLINEDI- RECTION1, INFILLLINEDIRECTION2, PRINTSPEED, EXTRUSIONTEMP, BEDTEMP, WORKSPACETEMP and POSITION.
 Results: Type IV tensile specimens are fabricated and tested with a 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 mechanic properties for ABS and PLA. Meanwhile, TOPBOT-TOMLINEDIRECTION1, WORKSPACETEMPERATURE and PRINTSPEED has a negative correlation with tensile strength. Position 1 and 3 with line depositions parallel to Y axis produce the higher tensile strength response.
 Conclusions: Findings imply that machine algorithms can be used to study multiple parameters at a time.