Title

An Efficient Predictive Modeling for Simulating Part-Scale Residual Stress in Laser Metal Deposition Process

Abstract

Residual stress and deformation are common issues which prevent dimensional accuracy and lead early fatigue of products by laser metal deposition process. Finite element analysis is an efficient means to estimate the temperature history and thermal stress distribution. However, it is extremely challenging to predict thermal history and stress distribution of a practical large and complex geometry if each laser pulse is taken into account as by conventional laser pulse modeling. Therefore, in this study, an efficient predictive finite element model with assumptions on the laser heat source and loading is established to study the evolution of thermal history, residual stresses, and deformations of a test coupon. The efficient predictive model, which is also called as the 2-layer by 2-layer model, simulates two layers at each laser pulse time. This model is compared with conventional laser pulse model in terms of the evolution of thermal history of selected points and residual stresses. Results show that the 2-layer by 2-layer model considerably reduces the simulation time without much compromising the accuracy of the prediction of deformation and thermal stress. In addition, test coupon is designed and fabricated to capture temperature history and observe microstructure change. It is found that microstructure presents certain correlation with cooling rate. Microstructure and residual stress of the test coupon are evaluated and found to be consistent with the prediction by proposed model. This efficient predictive model can shed light on large-scale part fabrication by laser metal deposition process in industry.

Department(s)

Mechanical and Aerospace Engineering

Keywords and Phrases

Deformation; Laser metal deposition; Quick prediction; Residual stress; Thermomechanical model

International Standard Serial Number (ISSN)

0268-3768; 1433-3015

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2021 The Authors, All rights reserved.

Publication Date

01 May 2021

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