Access the full text.
Sign up today, get DeepDyve free for 14 days.
The purpose of this study is to evaluate the capability and performance of analytical models to predict the structural mechanical behaviour of parts fabricated by fused deposition modelling (FDM).Design/methodology/approachA total of eight existing and newly proposed analytical models, tailored to satisfy the structural behaviour of FDM parts, are evaluated in terms of their capability to predict the ultimate tensile stress (UTS) and the elastic modulus (E) of parts made of polylactic acid (PLA) by the FDM process. This evaluation is made by comparing the structural properties predicted by these models with the experimental results obtained from tensile tests on FDM specimens fabricated with variable infill percentage, perimeter layers and build orientation.FindingsSome analytical models are able to predict with high accuracy (prediction errors smaller than 5%) the structural behaviour of FDM and categories of similar additive manufactured parts. The most accurate model is Gibson’s and Ashby, followed by the efficiency model and the two new proposed exponential and variant Duckworth models.Research limitations/implicationsThe study has been limited to uniaxial loading conditions along three different build orientations.Practical implicationsThe structural properties of FDM parts can be predicted by analytical models based on the process parameters and material properties. Product engineers can use these models during the design for the additive manufacturing process.Originality/valueExisting methods to estimate the structural properties of FDM parts are based on experimental tests; however, such methods are time-consuming and costly. In this work, the use of analytical models to predict the structural properties of FDM parts is proposed and evaluated.
Rapid Prototyping Journal – Emerald Publishing
Published: Jun 4, 2021
Keywords: Analytical models; Material extrusion; Fused deposition modelling (FDM); Structural properties; Prediction performance
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.