Towards Geometry Prediction in Additive Manufacturing by Considering Variation in the Inherent Strain
Paper i 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

Författare

Vaishak Ramesh Sagar

Chalmers, Industri- och materialvetenskap, Produktutveckling

Samuel C Lorin

Stiftelsen Fraunhofer-Chalmers Centrum för Industrimatematik

Kristina Wärmefjord

Chalmers, Industri- och materialvetenskap, Produktutveckling

Rikard Söderberg

Chalmers, Industri- och materialvetenskap, Produktutveckling

Procedia CIRP

22128271 (eISSN)

Vol. 114 117-122

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

Ämneskategorier

Teknisk mekanik

Annan materialteknik

Metallurgi och metalliska material

DOI

10.1016/j.procir.2022.10.017

Mer information

Senast uppdaterat

2023-01-10