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Title: Data mining and statistical inference in selective laser melting

Abstract

Selective laser melting (SLM) is an additive manufacturing process that builds a complex three-dimensional part, layer-by-layer, using a laser beam to fuse fine metal powder together. The design freedom afforded by SLM comes associated with complexity. As the physical phenomena occur over a broad range of length and time scales, the computational cost of modeling the process is high. At the same time, the large number of parameters that control the quality of a part make experiments expensive. In this paper, we describe ways in which we can use data mining and statistical inference techniques to intelligently combine simulations and experiments to build parts with desired properties. We start with a brief summary of prior work in finding process parameters for high-density parts. We then expand on this work to show how we can improve the approach by using feature selection techniques to identify important variables, data-driven surrogate models to reduce computational costs, improved sampling techniques to cover the design space adequately, and uncertainty analysis for statistical inference. Here, our results indicate that techniques from data mining and statistics can complement those from physical modeling to provide greater insight into complex processes such as selective laser melting.

Authors:
 [1]
  1. Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
Publication Date:
Research Org.:
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1438728
Report Number(s):
LLNL-JRNL-680063
Journal ID: ISSN 0268-3768
Grant/Contract Number:  
AC52-07NA27344
Resource Type:
Accepted Manuscript
Journal Name:
International Journal of Advanced Manufacturing Technology
Additional Journal Information:
Journal Volume: 86; Journal Issue: 5-8; Journal ID: ISSN 0268-3768
Publisher:
Springer
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE; 97 MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; Additive manufacturing; Selective laser melting; Design of experiments; Sampling; Feature selection; Code surrogates; Uncertainty analysis

Citation Formats

Kamath, Chandrika. Data mining and statistical inference in selective laser melting. United States: N. p., 2016. Web. doi:10.1007/s00170-015-8289-2.
Kamath, Chandrika. Data mining and statistical inference in selective laser melting. United States. https://doi.org/10.1007/s00170-015-8289-2
Kamath, Chandrika. Mon . "Data mining and statistical inference in selective laser melting". United States. https://doi.org/10.1007/s00170-015-8289-2. https://www.osti.gov/servlets/purl/1438728.
@article{osti_1438728,
title = {Data mining and statistical inference in selective laser melting},
author = {Kamath, Chandrika},
abstractNote = {Selective laser melting (SLM) is an additive manufacturing process that builds a complex three-dimensional part, layer-by-layer, using a laser beam to fuse fine metal powder together. The design freedom afforded by SLM comes associated with complexity. As the physical phenomena occur over a broad range of length and time scales, the computational cost of modeling the process is high. At the same time, the large number of parameters that control the quality of a part make experiments expensive. In this paper, we describe ways in which we can use data mining and statistical inference techniques to intelligently combine simulations and experiments to build parts with desired properties. We start with a brief summary of prior work in finding process parameters for high-density parts. We then expand on this work to show how we can improve the approach by using feature selection techniques to identify important variables, data-driven surrogate models to reduce computational costs, improved sampling techniques to cover the design space adequately, and uncertainty analysis for statistical inference. Here, our results indicate that techniques from data mining and statistics can complement those from physical modeling to provide greater insight into complex processes such as selective laser melting.},
doi = {10.1007/s00170-015-8289-2},
journal = {International Journal of Advanced Manufacturing Technology},
number = 5-8,
volume = 86,
place = {United States},
year = {2016},
month = {1}
}

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Works referencing / citing this record:

Machine‐Learning‐Based Monitoring of Laser Powder Bed Fusion
journal, August 2018

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Optimized hatch space selection in double-scanning track selective laser melting process
journal, November 2019

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Systematic approach for determining optimal processing parameters to produce parts with high density in selective laser melting process
journal, November 2019

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Regression with small data sets: a case study using code surrogates in additive manufacturing
journal, March 2018


Process-Structure Linkages Using a Data Science Approach: Application to Simulated Additive Manufacturing Data
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Generating virtual process maps of SLM using powder-scale SPH simulations
journal, November 2019

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Data analytics approach for melt-pool geometries in metal additive manufacturing
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A processing diagram for high-density Ti-6Al-4V by selective laser melting
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A Review of Model Inaccuracy and Parameter Uncertainty in Laser Powder Bed Fusion Models and Simulations
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Uncertainty Quantification of Film Cooling Performance of an Industrial Gas Turbine Vane
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Data analytics approach for melt-pool geometries in metal additive manufacturing
text, January 2019


Data analytics approach for melt-pool geometries in metal additive manufacturing
text, January 2019