Experimental investigation and empirical modelling of FDM process for compressive strength improvement

By Anoop Kumar Sood, Rajkumar Ohdar, Siba Sankar Mahapatra. 2012

548 citations recorded by the source corpus.

Abstract Fused deposition modelling (FDM) is gaining distinct advantage in manufacturing industries because of its ability to manufacture parts with complex shapes without any tooling requirement and human interface. The properties of FDM built parts exhibit high dependence on process parameters and can be improved by setting parameters at suitable levels. Anisotropic and brittle nature of build part makes it important to study the effect of process parameters to the resistance to compressive loading for enhancing service life of functional parts. Hence, the present work focuses on extensive study to understand the effect of five important parameters such as layer thickness, part build orientation, raster angle, raster width and air gap on the compressive stress of test specimen. The study not only provides insight into complex dependency of compressive stress on process parameters but also develops a statistically validated predictive equation. The equation is used to find optimal parameter setting through quantum-behaved particle swarm optimization (QPSO). As FDM process is a highly complex one and process parameters influence the responses in a non linear manner, compressive stress is predicted using artificial neural network (ANN) and is compared with predictive equation.

Subfields: Automotive Engineering, Building and Construction, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/099cc8dfe588efcc46d0b4937fa076ba26e783e9

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Citations in the corpus

Influence of meso-structure and chemical composition on FDM 3D-printed parts (2017, background): “[24,25] investigated the functional relationship between fiber dimensions, overlap and orientation and specimen strength using response surface methodology; results show that such parameters influence fiber bonding capabilities and distortion of the printed part and, consequently, the compressive st”
Applying Neural-Network-Based Machine Learning to Additive Manufacturing: Current Applications, Challenges, and Future Perspectives (2019, background): “FDM Layer thickness, orientation, raster angle, raster width, air gap Compressive strength [39]”
Fatigue performance of fused filament fabrication PLA specimens (2018, background): “As layer height decreases, parts show a higher cohesion among layers, due to an increase in surface contact between filaments, and the consequent increase in heat transport mechanisms, which favor neck growth among them [21].”
A review on quality control in additive manufacturing (2018, methodology): “In result, this optimized statistically predictive equations that gave a maximum compressive stress of 17.4751 MPa at optimum values of layer thickness, orientation, raster angle, raster width and air gap as 0.254 mm, 0.036°, 59.44°, 0.422 mm and 0.00026 mm, respectively (Sood et al., 2012).”