Towards Geometry Prediction in Additive Manufacturing by Considering Variation in the Inherent Strain
Paper in proceeding, 2022

The inherent strain method is commonly employed to predict part distortion in the additive manufacturing (AM) process simulations. However, mean inherent strain values are considered which could hinder the prediction accuracy. Therefore in this paper, variation in inherent strain values are considered to predict the geometric distortion. In the first step, a five layer mesoscale thermo-mechanical model is employed to estimate the varying inherent strain values in each of the five layers. This serves as an input to the inherent strain method to predict geometric distortion at the part level. A comparison between mean versus varying inherent strain approach is shown to highlight the differences in geometric distortion prediction accuracy.

thermo-mechanical simulation

Inherent strain method

geometric distortion prediction

laser powder bed fusion

metal additive manufacturing

Author

Vaishak Ramesh Sagar

Chalmers, Industrial and Materials Science, Product Development

Samuel C Lorin

Fraunhofer-Chalmers Centre

Kristina Wärmefjord

Chalmers, Industrial and Materials Science, Product Development

Rikard Söderberg

Chalmers, Industrial and Materials Science, Product Development

Procedia CIRP

22128271 (eISSN)

Vol. 114 117-122

17th CIRP Conference on Computer Aided Tolerancing, CAT 2022
Metz, France,

Subject Categories

Applied Mechanics

Other Materials Engineering

Metallurgy and Metallic Materials

DOI

10.1016/j.procir.2022.10.017

More information

Latest update

1/10/2023