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November 30, 2017Rapid Prototyping Journal228 citations

The effect of process parameters in fused deposition modelling on bonding degree and mechanical properties

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HLHongbin LiHuazhong University of Science and TechnologyTWTao WangQujing Normal UniversityJSJian SunTianjin University

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Abstract

Purpose The purpose of this paper is to study the effects of these major parameters, including layer thickness, deposition velocity and infill rate, on product’s mechanical properties and explore the quantitative relationship between these key parameters and tensile strength of the part. Design/methodology/approach A VHX-1000 super-high magnification lens zoom three-dimensional (3D) microscope is utilized to observe the bonding degree between filaments. A temperature sensor is embedded into the platform to collect the temperature of the specimen under different parameters and the bilinear elastic-softening cohesive zone model is used to analyze the maximum stress that the part can withstand under different interface bonding states. Findings The tensile strength is closely related to interface bonding state, which is determined by heat transition. The experimental results indicate that layer thickness plays the predominant role in affecting bonding strength, followed by deposition velocity and the effect of infill rate is the weakest. The numerical analysis results of the tensile strength predict models show a good coincidence with experimental data under the elastic and elastic-softened interface states, which demonstrates that the tensile strength model can predict the tensile strength exactly and also reveals the work mechanism of these parameters on tensile strength quantitatively. Originality/value The paper establishes the quantitative relationship between main parameters including layer thickness, infill rate and deposition velocity and tensile strength for the first time. The numerically analyzed results of the tensile strength predict model show a good agreement with the experimental result, which demonstrates the effectiveness of this predict model. It also reveals the work mechanism of the parameters on tensile strength quantitatively for the first time.

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Cite This Study

Li et al. (2017) studied this question.

synapsesocial.com/papers/69dc452545b398e6439f56dahttps://doi.org/10.1108/rpj-06-2016-0090
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