Integration of Physically-based and Data-driven Approaches for Thermal Field Prediction in Additive Manufacturing

A quantitative understanding of thermal field evolution is vital for quality control in additive manufacturing (AM). Because of the unknown material parameters, high computational costs, and imperfect understanding of the underlying science, physically-based approaches alone are insufficient for component-scale thermal field prediction. Here, I present a new framework that integrates physically-based and data-driven approaches with quasi in situ thermal imaging to address this problem. The framework consists of (i) thermal modeling using 3D finite element analysis (FEA), (ii) surrogate modeling using functional Gaussian process, and (iii) Bayesian calibration using the thermal imaging data. Based on heat transfer laws, I first investigate the transient thermal behavior during AM using 3D FEA. A functional Gaussian process-based surrogate model is then constructed to reduce the computational costs from the high-fidelity, physically-based model. I finally employ a Bayesian calibration method, which incorporates the surrogate model and thermal measurements, to enable layer-to-layer thermal field prediction across the whole component. A case study on fused deposition modeling is conducted for components with 7 to 16 layers. The cross-validation results show that the proposed framework allows for accurate and fast thermal field prediction for components with different process settings and geometric designs.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Materials Science and Engineering

Department dc:contributor.department

Materials Science and Engineering

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2017

Author dc:creator

• Li, Jingran

Chair dc:contributor.committeechair

• Yu, Hang

Committee members dc:contributor.committeemember

• Kapania, Rakesh K.
• Jin, Ran

• additive manufacturing
• geometry of freeform
• layer-to-layer modeling
• numerical simulation

Statement dc:rights

• Creative Commons Attribution 3.0 United States

Licence dc:rights.uri

• http://creativecommons.org/licenses/by/3.0/us/

Language dc:language.iso

en_US

Handle dc:identifier.uri

http://hdl.handle.net/10919/79620

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/79620