Title: Photodiode-based machine learning for optimization of laser powder bed fusion parameters in complex geometries

Abstract

We report the quality of parts produced through laser powder bed fusion additive manufacturing can be irregular, with complex geometries sometimes exhibiting dimensional inaccuracies and defects. For optimal part quality, laser process parameters should be selected carefully prior to printing and adjusted during the print if necessary. This is challenging since approaches to control and optimize the build parameters need to take into account the part geometry, the material, and the complex physics of laser powder bed fusion. This work describes a data-driven approach using experimental diagnostics for the optimization of laser process parameters prior to printing. A training dataset is generated by collecting high speed photodiode signal data while printing simple parts containing key geometry features with various process parameter strategies. Supervised learning approaches are employed to train both a forward model and an inverse model. The forward model takes as inputs track-wise geometry features and laser parameters and outputs the photodiode signal along the scan path. The inverse model takes as inputs the geometry features and photodiode signal and predicts the laser parameters. Given the part geometry and a desired photodiode signal, the inverse model can thus determine the required laser parameters. Two test parts which contain defect-pronemore » features are used to assess the validity of the inverse model. The use of the model leads to improved part quality (higher dimensional accuracy, reduced dross, reduced distortion) for both test geometries.« less

Authors:

Lapointe, S. ^[1]; Guss, G. ^[1]; Reese, Z. ^[1]; Strantza, M. ^[1]; Matthews, M. J. ^[1]; Druzgalski, C. L. ^[1]

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+ Show Author Affiliations

1. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

Publication Date:

Sat Feb 26 00:00:00 EST 2022

Research Org.:

Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

Sponsoring Org.:

USDOE National Nuclear Security Administration (NNSA)

OSTI Identifier:

1886148

Alternate Identifier(s):

OSTI ID: 1846997

Report Number(s):

LLNL-JRNL-821280
Journal ID: ISSN 2214-8604; 1033207

Grant/Contract Number:  

AC52-07NA27344

Resource Type:

Accepted Manuscript

Journal Name:

Additive Manufacturing

Additional Journal Information:

Journal Volume: 53; Journal Issue: N/A; Journal ID: ISSN 2214-8604

Publisher:

Elsevier

Country of Publication:

United States

Language:

English

Subject:

36 MATERIALS SCIENCE; additive manufacturing; laser powder bed fusion; photodiode; machine learning