Parameter effects and process modelling of Polyamide 12 3D-printed parts strength and toughness

ABSTRACT

Polyamide 12 (PA12) is a high-performance polymeric material adopted by various industries for its thermal, electrical, and mechanical properties. In this work, a comprehensive examination of the impact of three 3D printing (3DP) parameters, i.e., Nozzle Temperature (NT), Layer Height (LH), and raster Deposition Angle (DA), on the mechanical strength and toughness of Fused Filament Fabrication (FFF) 3DP PA12 polymer is studied. The general full factorial experimental methodology is followed. The 3DP parameters’ effects were analyzed using descriptive and analytic statistical tools, such as Box plots, interaction charts, and ANOVA analysis. The experimental data depicted different spreads and median values for each parameter level regarding the utilized responses, concluding that the modeling process is vital for the process control and the parameters’ optimization. Two predictive models, a Quadratic Regression Model (QRM) and an Artificial Neural Network (ANN) are fitted on the median values of the experimental data responses predictions, i.e. static mechanical strength (σ_b), elastic modulus (E), and toughness (T). The ANN performance was proven to be better than the QRM, providing better Mean Absolute Percentage Error (MAPE) values. NT and LH increase σ_b and T medians and spreads, while zero raster DA optimizes the σ_b and T.

keywords:

• 3d-Printing
• FFF
• Box-plot
• neural
• network
• temperature
• layer
• raster
• polyamide
• strength
• E
• toughness

Disclosure statement

No potential conflict of interest was reported by the author(s).