Processing Windows for Al-357 by Lpbf Process: A Novel Framework Integrating Fem Simulation and Machine Learning with Empirical Testing

Abstract

Purpose: This study aims to develop a holistic method that integrates finite element modeling, machine learning, and experimental validation to propose processing windows for optimizing the laser powder bed fusion (LPBF) process specific to the Al-357 alloy. Design/methodology/approach: Validation of a 3D heat transfer simulation model was conducted to forecast melt pool dimensions, involving variations in laser power, laser scanning speed, powder bed thickness (PBT) and powder bed pre-heating (PHB). Using the validated model, a data set was compiled to establish a back-propagation-based machine learning capable of predicting melt pool dimensional ratios indicative of printing defects. Findings: The study revealed that, apart from process parameters, PBT and PHB significantly influenced defect formation. Elevated PHBs were identified as contributors to increased lack of fusion and keyhole defects. Optimal combinations were pinpointed, such as 30.0 µm PBT with 90.0 and 120.0 °C PHBs and 50.0 µm PBT with 120.0 °C PHB. Originality/value: The integrated process mapping approach showcased the potential to expedite the qualification of LPBF parameters for Al-357 alloy by minimizing the need for iterative physical testing.

Department(s)

Mechanical and Aerospace Engineering

Comments

Intelligent Systems Center, Grant CMMI-1625736

Keywords and Phrases

Additive manufacturing; Al-357; FEM framework; Laser powder bed fusion process; Machine learning; Process optimization; Processing windows

International Standard Serial Number (ISSN)

1355-2546

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2024 Emerald, All rights reserved.

Publication Date

01 Jan 2024

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