Keywords

Aluminum, Laser Powder Bed Fusion, Strength, Ductility, Machine Learning, Neural Networks

Abstract

Laser powder bed fusion (LPBF) additive manufacturing is a promising manufacturing technology enabling enhanced design freedom through layer-by-layer production. However, traditional, wrought aluminum (Al) alloys suffer from solidification cracking during LPBF processing and the initial printer parameter optimization process required for every novel or untested material is time and resource intensive. There exists a need to formulate Al alloys for LPBF and a method to reduce the resources consumed during initial printer parameter optimization studies. Al-9.5Ce-xMo (x = 0.2, 0.6, 1.0 wt. %) alloys have been designed specifically for LPBF utilizing the CALPHAD approach. Additionally, a neural network model was developed and capable of predicting the window of print parameters likely to yield high density printed parts. The Al-9.5Ce-xMo alloys were processed by LPBF and found to be crack free. Each alloy underwent microstructural characterization and tensile testing both in the as-built state and after long thermal exposure at various times and temperatures to determine the thermal stability of the alloys. Comparisons were made to the Al-10Ce base alloy. In the as-built state, additions of Mo as low as 0.2 wt.% increased strength and ductility, with ductility increasing from 10.8 ± 0.1 % in Al-10Ce to 16.8 ± 0.2 %. With 1.0 wt. % Mo addition, yield strength increased by 44 MPa over the Al-10Ce alloy to 266.2 ± 1.6 MPa and ductility was further enhanced to 16.9 ± 0.8 %. Full retention of tensile properties was found for all Mo-containing alloys after exposure to 200 °C for 120 hours. The neural network model, guided by material thermophysical properties and print parameters, successfully predicted the printability windows of Fe-, Ni-, and Al-based alloys, is capable of base alloy differentiation, and making predictions for novel alloys.

Completion Date

2025

Semester

Summer

Committee Chair

Sohn, Yongho

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Department

Materials Science and Engineering

Format

PDF

Identifier

DP0029545

Language

English

Document Type

Thesis

Campus Location

Orlando (Main) Campus

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