RSM based investigation of compressive properties of FDM fabricated part

This study investigates the performance of the acrylonitrile-butadiene-styrene (ABS) P400 part, manufactured using fused deposition modeling (FDM), under compressive loading. Effect of varying levels of three FDM process parameters, i.e., raster angle (A), air gap (B), and raster width (C) on three responses, namely compressive stress (C-S), percentage deformation (% D), and breaking stress (B-S) of the fabricated parts is studied. Experimental results are analysed using analysis of variance (ANOVA), response graphs, and 3D surface plots. Investigations established the anisotropic nature of the ABSP400 part, which causes lower strength of the fabricated parts. Chosen process parameters significantly affect the compressive properties. A complex relationship exists between process parameters and the studied responses. It is also observed that B-S is much lower than C-S and % D varies in a contradictory fashion with changes in process parameters. Predictive models for the responses are developed and validated through additivity tests. To discover the best combination of FDM process parameters for optimum responses, multi-objective optimization utilizing the desirability function approach is used. Multi-objective optimization results are validated using the confirmatory test. The novelty of the present work is that it explores the effect of three critical parameters of the FDM process not only on compressive stress (C-S) but also on other critical compressive properties such as percentage deformation (% D) and breaking stress (B-S) of the ABS P400 fabricated parts. (C) 2021 CIRP.

Automated summary

This study investigates the performance of ABS P400 parts manufactured using FDM under compressive loading, with a focus on the effect of varying FDM process parameters on compressive properties. The study establishes the anisotropic nature of the parts, with process parameters significantly affecting compressive properties. Predictive models and multi-objective optimization are utilized to discover the best combination of parameters for optimum responses.