Optimization of fused deposition modeling (FDM) process parameters for flexural strength

2021 ◽  
Author(s):  
Saty Dev ◽  
Rajeev Srivastava
Keyword(s):  
Flexural Strength ◽  
Process Parameters ◽  
Fused Deposition Modeling ◽  
Fused Deposition ◽  
Deposition Modeling

Effect of process parameters on flexural strength and surface roughness in fused deposition modeling of PC-ABS material

2021 ◽  
pp. 251659842110311
Author(s):  
Shrikrishna Pawar ◽  
Dhananjay Dolas1
Keyword(s):  
Surface Roughness ◽  
Flexural Strength ◽  
Response Surface ◽  
Layer Thickness ◽  
Process Parameters ◽  
Surface Finish ◽  
Fused Deposition Modeling ◽  
Build Orientation ◽  
Fused Deposition ◽  
Deposition Modeling

Fused deposition modeling (FDM) is one of the most commonly used additive manufacturing (AM) technologies, which has found application in industries to meet the challenges of design modifications without significant cost increase and time delays. Process parameters largely affect the quality characteristics of AM parts, such as mechanical strength and surface finish. This article aims to optimize the parameters for enhancing flexural strength and surface finish of FDM parts. A total of 18 test specimens of polycarbonate (PC)-ABS (acrylonitrile–butadiene–styrene) material are printed to analyze the effect of process parameters, viz. layer thickness, build orientation, and infill density on flexural strength and surface finish. Empirical models relating process parameters with responses have been developed by using response surface regression and further analyzed by analysis of variance. Main effect plots and interaction plots are drawn to study the individual and combined effect of process parameters on output variables. Response surface methodology was employed to predict the results of flexural strength 48.2910 MPa and surface roughness 3.5826 µm with an optimal setting of parameters of 0.14-mm layer thickness and 100% infill density along with horizontal build orientation. Experimental results confirm infill density and build orientation as highly significant parameters for impacting flexural strength and surface roughness, respectively.

Effect of layer thickness on irreversible thermal expansion and interlayer strength in fused deposition modeling

2017 ◽  
Vol 23 (5) ◽  
pp. 943-953 ◽  
Author(s):  
Anthony A. D’Amico ◽  
Analise Debaie ◽  
Amy M. Peterson
Keyword(s):  
Mechanical Properties ◽  
Thermal Expansion ◽  
Flexural Strength ◽  
Layer Thickness ◽  
Fused Deposition Modeling ◽  
Thermal Strain ◽  
Content Type ◽  
Fused Deposition ◽  
Deposition Modeling ◽  
The Impact

Purpose The aim of this paper is to examine the impact of layer thickness on irreversible thermal expansion, residual stress and mechanical properties of additively manufactured parts. Design/methodology/approach Samples were printed at several layer thicknesses, and their irreversible thermal expansion, tensile strength and flexural strength were determined. Findings Irreversible thermal strain increases with decreasing layer thickness, up to 22 per cent strain. Tensile and flexural strengths exhibited a peak at a layer thickness of 200 μm although the maximum was not statistically significant at a 95 per cent confidence interval. Tensile strength was 54 to 97 per cent of reported values for injection molded acrylonitrile butadiene styrene (ABS) and 29 to 73 per cent of those reported for bulk ABS. Flexural strength was 18 to 41 per cent of reported flexural strength for bulk ABS. Practical implications The large irreversible thermal strain exhibited that corresponding residual stresses could lead to failure of additively manufactured parts over time. Additionally, the observed irreversible thermal strains could enable thermally responsive shape in additively manufactured parts. Variation in mechanical properties with layer thickness will also affect manufactured parts. Originality/value Tailorable irreversible thermal strain of this magnitude has not been previously reported for additively manufactured parts. This strain occurs in parts made with both high-end and consumer grade fused deposition modeling machines. Additionally, the impact of layer thickness on tensile and flexural properties of additively manufactured parts has received limited attention in the literature.

scholarly journals Influence of Fill Gap on Flexural Strength of Parts Fabricated by Curved Layer Fused Deposition Modeling

2015 ◽  
Vol 20 ◽  
pp. 243-248 ◽  
Author(s):  
Hua Wei Guan ◽  
Monica Mahesh Savalani ◽  
Ian Gibson ◽  
Olaf Diegel
Keyword(s):  
Flexural Strength ◽  
Fused Deposition Modeling ◽  
Fused Deposition ◽  
Deposition Modeling

scholarly journals Experimental investigation on flexural properties of FDM-processed PET-G specimen using response surface methodology

2021 ◽  
Vol 349 ◽  
pp. 01008
Author(s):  
Nikolaos A. Fountas ◽  
Ioannis Papantoniou ◽  
John D. Kechagias ◽  
Dimitrios E. Manolakos ◽  
Nikolaos M. Vaxevanidis
Keyword(s):  
Flexural Strength ◽  
Response Surface ◽  
Fused Deposition Modeling ◽  
Performance Metrics ◽  
Optimal Parameter ◽  
Strong Dependence ◽  
Control Parameters ◽  
Fused Deposition ◽  
Deposition Modeling ◽  
3D Printed

The properties of fused deposition modeling (FDM) products exhibit strong dependence on process parameters which may be improved by setting suitable levels for parameters related to FDM. Anisotropic and brittle nature of 3D-printed components makes it essential to investigate the effect of FDM control parameters to different performance metrics related to resistance for improving strength of functional parts. In this work the flexural strength of polyethylene terephthalate glycol (PET-G) is examined under by altering the levels of different 3D-printing parameters such as layer height, infill density, deposition angle, printing speed and printing temperature. A response surface experiment was established having 27 experimental runs to obtain the results for flexural strength (MPa) and to further investigate the effect of each control parameter on the response by studying the results using statistical analysis. The experiments were conducted as per the ASTM D790 standard. The regression model generated for flexural strength adequately explains the variation of FDM control parameters on flexural strength and thus, it can be implemented to find optimal parameter settings with the use of either an intelligent algorithm, or neural network.

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