Process window estimation in manufacturing through Entropy-Sigma active learning

Jaydeep Karandikar, Anirban Chaudhuri, Scott Smith, Tony Schmitz, Karen Willcox

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

In manufacturing, there exist boundary identification problems for defining parameter spaces that meet desired thresholds on outcomes. This paper presents an Entropy-Sigma acquisition function for active learning of the process window/map in manufacturing using a Gaussian Process surrogate. The method is applied to identify the stability boundary for the stability process map in machining using time-domain simulations with a periodic sampling stability metric. Results show that the proposed Entropy-Sigma method significantly outperforms Latin hypercube sampling or grid-based methods. The described method can be applied to identify the process window/map for any manufacturing application using a quantitative process outcome metric.

Original languageEnglish
Pages (from-to)87-92
Number of pages6
JournalManufacturing Letters
Volume34
DOIs
StatePublished - Oct 2022

Funding

This manuscript has been authored in part by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). This work has been supported in part by the DOE Office of Energy Efficiency and Renewable Energy (EERE), Manufacturing Science Division, and used resources at the Manufacturing Demonstration Facility, a DOE-EERE User Facility at Oak Ridge National Laboratory. The second and last authors acknowledge support from Department of Energy award number DE-SC0021239 and ARPA-E Differentiate award number DE-AR0001208.

Keywords

  • Additive manufacturing
  • Boundary identification
  • Contour location
  • Machining
  • Stability boundary

Fingerprint

Dive into the research topics of 'Process window estimation in manufacturing through Entropy-Sigma active learning'. Together they form a unique fingerprint.

Cite this