Gaussian process-based surrogate modeling framework for process planning in laser powder-bed fusion additive manufacturing of 316L stainless steel

Tapia, Gustavo; Khairallah, Saad A.; Matthews, Manyalibo J.; King, Wayne E.; Elwany, Alaa

doi:10.1007/s00170-017-1045-z

Title: Gaussian process-based surrogate modeling framework for process planning in laser powder-bed fusion additive manufacturing of 316L stainless steel

Journal Article · Fri Sep 22 00:00:00 EDT 2017 · International Journal of Advanced Manufacturing Technology

DOI:https://doi.org/10.1007/s00170-017-1045-z· OSTI ID:1420279

Tapia, Gustavo ^[1]; Khairallah, Saad A. ^[2]; Matthews, Manyalibo J. ^[2]; King, Wayne E. ^[2];

^[1]

Texas A & M Univ., College Station, TX (United States)
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

Here, Laser Powder-Bed Fusion (L-PBF) metal-based additive manufacturing (AM) is complex and not fully understood. Successful processing for one material, might not necessarily apply to a different material. This paper describes a workflow process that aims at creating a material data sheet standard that describes regimes where the process can be expected to be robust. The procedure consists of building a Gaussian process-based surrogate model of the L-PBF process that predicts melt pool depth in single-track experiments given a laser power, scan speed, and laser beam size combination. The predictions are then mapped onto a power versus scan speed diagram delimiting the conduction from the keyhole melting controlled regimes. This statistical framework is shown to be robust even for cases where experimental training data might be suboptimal in quality, if appropriate physics-based filters are applied. Additionally, it is demonstrated that a high-fidelity simulation model of L-PBF can equally be successfully used for building a surrogate model, which is beneficial since simulations are getting more efficient and are more practical to study the response of different materials, than to re-tool an AM machine for new material powder.

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

Sponsoring Organization:: USDOE

Grant/Contract Number:: AC52-07NA27344

OSTI ID:: 1420279

Report Number(s):: LLNL-JRNL-726754

Journal Information:: International Journal of Advanced Manufacturing Technology, Vol. 94, Issue 9-12; ISSN 0268-3768

Publisher:: SpringerCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 107 works

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Machine‐Learning‐Based Monitoring of Laser Powder Bed Fusion Yuan, Bodi; Guss, Gabriel M.; Wilson, Aaron C. Advanced Materials Technologies, Vol. 3, Issue 12 https://doi.org/10.1002/admt.201800136	journal	August 2018
Uncertainty Quantification in Metallic Additive Manufacturing Through Physics-Informed Data-Driven Modeling Wang, Zhuo; Liu, Pengwei; Ji, Yanzhou JOM, Vol. 71, Issue 8 https://doi.org/10.1007/s11837-019-03555-z	journal	June 2019
Process Design of Laser Powder Bed Fusion of Stainless Steel Using a Gaussian Process-Based Machine Learning Model Meng, Lingbin; Zhang, Jing JOM, Vol. 72, Issue 1 https://doi.org/10.1007/s11837-019-03792-2	journal	September 2019
Multiphysics modeling and simulation of laser additive manufacturing process Khanafer, Khalil; Al-Masri, Ali; Aithal, Shashikant International Journal on Interactive Design and Manufacturing (IJIDeM), Vol. 13, Issue 2 https://doi.org/10.1007/s12008-018-0520-6	journal	December 2018
Uncertainty quantification of grain morphology in laser direct metal deposition Nath, Paromita; Hu, Zhen; Mahadevan, Sankaran Modelling and Simulation in Materials Science and Engineering, Vol. 27, Issue 4 https://doi.org/10.1088/1361-651x/ab1676	journal	April 2019
Layerwise Anomaly Detection in Laser Powder-Bed Fusion Metal Additive Manufacturing Mahmoudi, Mohamad; Ezzat, Ahmed Aziz; Elwany, Alaa Journal of Manufacturing Science and Engineering, Vol. 141, Issue 3 https://doi.org/10.1115/1.4042108	journal	January 2019
A Review of Model Inaccuracy and Parameter Uncertainty in Laser Powder Bed Fusion Models and Simulations Moges, Tesfaye; Ameta, Gaurav; Witherell, Paul Journal of Manufacturing Science and Engineering, Vol. 141, Issue 4 https://doi.org/10.1115/1.4042789	journal	February 2019

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42 ENGINEERING
36 MATERIALS SCIENCE
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Additive manufacturing
Laser powder-bed fusion
316L stainless steel
Gaussian processes
Bayesian statistics

Title: Gaussian process-based surrogate modeling framework for process planning in laser powder-bed fusion additive manufacturing of 316L stainless steel

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