Predicting strength of additively manufactured thermoplastic polymer parts produced using material extrusion. Issue 2 (12th March 2018)

• Record Type: Journal Article
• Authors: Bartolai, Joseph; Simpson, Timothy W.; Xie, Renxuan
• Is Part Of: Rapid prototyping journal. Volume 24:Issue 2(2018)
• Page Start: 321
• Page End: 332
• Publication Date: 2018-03-12
• Subjects: Polymers -- Mechanical properties -- Additive manufacturing -- Material extrusion; Engineering design -- Periodicals; 620.004205
• DOI: 10.1108/RPJ-02-2017-0026
• Languages: English
• ISSNs: 1355-2546

Abstract

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.