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Article
Publication date: 12 March 2018

Joseph Bartolai, Timothy W. Simpson and Renxuan Xie

The weakest point in additively manufactured polymer parts produced by material extrusion additive manufacturing (MEAM) is the interface between adjacent layers and deposition…

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Abstract

Purpose

The weakest point in additively manufactured polymer parts produced by material extrusion additive manufacturing (MEAM) is the interface between adjacent layers and deposition toolpaths or “roads”. This study aims to predict the mechanical strength of parts by utilizing a novel analytical approach. Strength predictions are made using the temperature history of these interfaces, polymer rheological data, and polymer weld theory.

Design/methodology/approach

The approach is validated using experimental data for two common 3D-printed polymers: polycarbonate (PC) and acrylonitrile butadiene styrene (ABS). Interface temperature history data are collected in situ using infrared imaging. Rheological data of the polycarbonate and acrylonitrile butadiene styrene used to fabricate the fused filament fabrication parts in this study have been determined experimentally.

Findings

The strength of the interfaces has been predicted, to within 10% of experimental strength, using polymer weld theory from the literature adapted to the specific properties of the polycarbonate and acrylonitrile butadiene styrene feedstock used in this study.

Originality/value

This paper introduces a novel approach for predicting the strength of parts produced by MEAM based on the strength of interfaces using polymer weld theory, polymer rheology, temperature history of the interface and the forces applied to the interface. Unlike methods that require experimental strength data as a prediction input, the proposed approach is material and build orientation agnostic once fundamental parameters related to material composition have been determined.

Details

Rapid Prototyping Journal, vol. 24 no. 2
Type: Research Article
ISSN: 1355-2546

Keywords

Available. Open Access. Open Access

Abstract

Details

Journal of Intelligent Manufacturing and Special Equipment, vol. 4 no. 1
Type: Research Article
ISSN: 2633-6596

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