Machine-Learning-Based Monitoring of Laser Powder Bed Fusion

Yuan, Bodi; Guss, Gabriel M.; Wilson, Aaron C.; Hau-Riege, Stefan P.; DePond, Phillip J.; McMains, Sara; Matthews, Manyalibo J.; Giera, Brian

doi:10.1002/admt.201800136

Title: Machine-Learning-Based Monitoring of Laser Powder Bed Fusion

Abstract

A two-step machine learning approach to monitoring Laser Powder Bed Fusion (LPBF) additive manufacturing is demonstrated that enables on-the-fly assessments of laser track welds. First, in situ video melt pool data acquired during LPBF is labeled according to the (1) average and (2) standard deviation of individual track width and also (3) whether or not the track is continuous, measured post-build through an ex situ height map analysis algorithm. This procedure generates three ground truth labeled datasets for supervised machine learning. Using a portion of the labeled 10-millisecond video clips, a single Convolutional Neural Network architecture is trained to generate three distinct networks. With the remaining in situ LPBF data, the trained neural networks are tested and evaluated and found to predict track width, standard deviation, and continuity without the need for ex situ measurements. This two-step approach should benefit any LPBF system – or any additive manufacturing technology – where height-map-derived properties can serve as useful labels for in situ sensor data.

Authors:

Yuan, Bodi ^[1]; Guss, Gabriel M. ^[1]; Wilson, Aaron C. ^[1]; Hau-Riege, Stefan P. ^[1]; DePond, Phillip J. ^[1]; McMains, Sara ^[1]; Matthews, Manyalibo J. ^[1];

^[1]

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

Publication Date:: Wed Sep 05 00:00:00 EDT 2018

Research Org.:: Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)

Sponsoring Org.:: USDOE National Nuclear Security Administration (NNSA)

OSTI Identifier:: 1481059

Alternate Identifier(s):: OSTI ID: 1468826

Report Number(s):: LLNL-JRNL-748383
Journal ID: ISSN 2365-709X; 933429

Grant/Contract Number:: AC52-07NA27344

Resource Type:: Accepted Manuscript

Journal Name:: Advanced Materials Technologies

Additional Journal Information:: Journal Volume: 3; Journal Issue: 12; Journal ID: ISSN 2365-709X

Publisher:: Wiley

Country of Publication:: United States

Language:: English

Subject:: 42 ENGINEERING

Citation Formats


                    Yuan, Bodi, Guss, Gabriel M., Wilson, Aaron C., Hau-Riege, Stefan P., DePond, Phillip J., McMains, Sara, Matthews, Manyalibo J., and Giera, Brian. Machine-Learning-Based Monitoring of Laser Powder Bed Fusion.  United States: N. p., 2018. 
Web.  doi:10.1002/admt.201800136.

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                    Yuan, Bodi, Guss, Gabriel M., Wilson, Aaron C., Hau-Riege, Stefan P., DePond, Phillip J., McMains, Sara, Matthews, Manyalibo J., & Giera, Brian. Machine-Learning-Based Monitoring of Laser Powder Bed Fusion.  United States.  https://doi.org/10.1002/admt.201800136

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                    Yuan, Bodi, Guss, Gabriel M., Wilson, Aaron C., Hau-Riege, Stefan P., DePond, Phillip J., McMains, Sara, Matthews, Manyalibo J., and Giera, Brian. Wed .  
"Machine-Learning-Based Monitoring of Laser Powder Bed Fusion".  United States.  https://doi.org/10.1002/admt.201800136.  https://www.osti.gov/servlets/purl/1481059.

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@article{osti_1481059,

  title        = {Machine-Learning-Based Monitoring of Laser Powder Bed Fusion},

  author       = {Yuan, Bodi and Guss, Gabriel M. and Wilson, Aaron C. and Hau-Riege, Stefan P. and DePond, Phillip J. and McMains, Sara and Matthews, Manyalibo J. and Giera, Brian},

  abstractNote = {A two-step machine learning approach to monitoring Laser Powder Bed Fusion (LPBF) additive manufacturing is demonstrated that enables on-the-fly assessments of laser track welds. First, in situ video melt pool data acquired during LPBF is labeled according to the (1) average and (2) standard deviation of individual track width and also (3) whether or not the track is continuous, measured post-build through an ex situ height map analysis algorithm. This procedure generates three ground truth labeled datasets for supervised machine learning. Using a portion of the labeled 10-millisecond video clips, a single Convolutional Neural Network architecture is trained to generate three distinct networks. With the remaining in situ LPBF data, the trained neural networks are tested and evaluated and found to predict track width, standard deviation, and continuity without the need for ex situ measurements. This two-step approach should benefit any LPBF system – or any additive manufacturing technology – where height-map-derived properties can serve as useful labels for in situ sensor data.},

  doi          = {10.1002/admt.201800136},

  journal      = {Advanced Materials Technologies},

  number       = 12,

  volume       = 3,

  place        = {United States},

  year         = {Wed Sep 05 00:00:00 EDT 2018},

  month        = {Wed Sep 05 00:00:00 EDT 2018}

}

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Journal Article:

Free Publicly Available Full Text

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https://doi.org/10.1002/admt.201800136

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Cited by: 90 works

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Figures / Tables:

Figure 1: Schematic of video capture system. Adjustable mirrors trace the path of a focal point shared between the co-aligned high-speed camera, illumination laser, and high-power laser. The camera records in situ video taken during each welding event.

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Similar Records

Title: Machine-Learning-Based Monitoring of Laser Powder Bed Fusion

Abstract

Citation Formats

Figures / Tables:

Single track formation in selective laser melting of metal powders journal, September 2010

Multifractal Analysis of Image Profiles for the Characterization and Detection of Defects in Additive Manufacturing journal, January 2018

Why Beam Analysis is Crucial for Additive Manufacturing: Reproducible laser beam parameters are the key to success in selective laser manufacturing processes journal, January 2018

Deep learning journal, May 2015

Parametric analysis of the selective laser melting process journal, July 2007

Optical diagnostics of selective laser melting and monitoring of single-track formation journal, January 2017

Elucidation of melt flows and spatter formation mechanisms during high power laser welding of pure titanium journal, August 2015

Monitoring and Adaptive Control of Laser Processes journal, January 2014

Additive manufacturing of metallic components – Process, structure and properties journal, March 2018

Computer Vision and Machine Learning for Autonomous Characterization of AM Powder Feedstocks journal, December 2016

Anomaly detection and classification in a laser powder bed additive manufacturing process using a trained computer vision algorithm journal, January 2018

Effect of Laser Power and Scan Speed on Melt Pool Characteristics of Commercially Pure Titanium (CP-Ti) journal, June 2017

Metal powder absorptivity: modeling and experiment journal, January 2016

Flaw detection in powder bed fusion using optical imaging journal, May 2017

Experimental analysis of spatter generation and melt-pool behavior during the powder bed laser beam melting process journal, January 2018

Gaussian process-based surrogate modeling framework for process planning in laser powder-bed fusion additive manufacturing of 316L stainless steel journal, September 2017

Mesoscopic simulation model of selective laser melting of stainless steel powder journal, November 2014

Laser powder-bed fusion additive manufacturing: Physics of complex melt flow and formation mechanisms of pores, spatter, and denudation zones journal, April 2016

ImageNet classification with deep convolutional neural networks journal, May 2017

Density of additively-manufactured, 316L SS parts using laser powder-bed fusion at powers up to 400 W journal, May 2014

In situ monitoring of selective laser melting of zinc powder via infrared imaging of the process plume journal, February 2018

Characterization of Metal Powders Used for Additive Manufacturing journal, January 2014

The Unreasonable Effectiveness of Data journal, March 2009

Metal vapor micro-jet controls material redistribution in laser powder bed fusion additive manufacturing journal, June 2017

Defect generation and propagation mechanism during additive manufacturing by selective beam melting journal, November 2014

Evaluation of Powder Layer Density for the Selective Laser Melting (SLM) Process journal, January 2017

Optical In-Process Temperature Monitoring of Selective Laser Melting journal, January 2013

Data mining and statistical inference in selective laser melting journal, January 2016

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Review of selective laser melting: Materials and applications journal, December 2015

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A fuzzy pattern recognition based system for monitoring laser weld quality journal, July 2001

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A review on quality control in additive manufacturing journal, April 2018

Direct measurements of temperature-dependent laser absorptivity of metal powders journal, January 2015

Review of in-situ process monitoring and in-situ metrology for metal additive manufacturing journal, April 2016

Quality assessment in laser welding: a critical review journal, May 2017

Deep Learning text, January 2018

Design and validation of a ten nanosecond resolved resistive thermometer for Gaussian laser beam heating journal, December 2019

Toward the digital twin of additive manufacturing: Integrating thermal simulations, sensing, and analytics to detect process faults journal, January 2020

Single track formation in selective laser melting of metal powders
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Multifractal Analysis of Image Profiles for the Characterization and Detection of Defects in Additive Manufacturing
journal, January 2018

Why Beam Analysis is Crucial for Additive Manufacturing: Reproducible laser beam parameters are the key to success in selective laser manufacturing processes
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Deep learning
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Parametric analysis of the selective laser melting process
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Optical diagnostics of selective laser melting and monitoring of single-track formation
journal, January 2017

Elucidation of melt flows and spatter formation mechanisms during high power laser welding of pure titanium
journal, August 2015

Monitoring and Adaptive Control of Laser Processes
journal, January 2014

Additive manufacturing of metallic components – Process, structure and properties
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Computer Vision and Machine Learning for Autonomous Characterization of AM Powder Feedstocks
journal, December 2016

Anomaly detection and classification in a laser powder bed additive manufacturing process using a trained computer vision algorithm
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Effect of Laser Power and Scan Speed on Melt Pool Characteristics of Commercially Pure Titanium (CP-Ti)
journal, June 2017

Metal powder absorptivity: modeling and experiment
journal, January 2016

Flaw detection in powder bed fusion using optical imaging
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Experimental analysis of spatter generation and melt-pool behavior during the powder bed laser beam melting process
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Gaussian process-based surrogate modeling framework for process planning in laser powder-bed fusion additive manufacturing of 316L stainless steel
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Mesoscopic simulation model of selective laser melting of stainless steel powder
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Laser powder-bed fusion additive manufacturing: Physics of complex melt flow and formation mechanisms of pores, spatter, and denudation zones
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Density of additively-manufactured, 316L SS parts using laser powder-bed fusion at powers up to 400 W
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In situ monitoring of selective laser melting of zinc powder via infrared imaging of the process plume
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Characterization of Metal Powders Used for Additive Manufacturing
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The Unreasonable Effectiveness of Data
journal, March 2009

Metal vapor micro-jet controls material redistribution in laser powder bed fusion additive manufacturing
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Defect generation and propagation mechanism during additive manufacturing by selective beam melting
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Evaluation of Powder Layer Density for the Selective Laser Melting (SLM) Process
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Optical In-Process Temperature Monitoring of Selective Laser Melting
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Data mining and statistical inference in selective laser melting
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Review of selective laser melting: Materials and applications
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Integration of physically-based and data-driven approaches for thermal field prediction in additive manufacturing
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A fuzzy pattern recognition based system for monitoring laser weld quality
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Determination of True Temperature in Selective Laser Melting of Metal Powder Using Infrared Camera
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Metal Additive Manufacturing: A Review
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Review of in-situ process monitoring and in-situ metrology for metal additive manufacturing
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Design and validation of a ten nanosecond resolved resistive thermometer for Gaussian laser beam heating
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