SUPPORT VECTOR MACHINES FOR CLASSIFICATION OF DIRECT ENERGY DEPOSITION STANDOFF DISTANCE FOR IMPROVED PROCESS CONTROL

Zoe Alexander, Nathaniel DeVol, Molly Emig, Kyle Saleeby, Thomas Feldhausen, Thomas Kurfess, Katherine Fu, Christopher Saldana

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

A critical factor in the implementation of direct energy deposition is the ability to maintain the standoff distance between the nozzle and the build surface, as this influences powder capture efficiency and overall part quality. Due to process-related variations, layer height may vary, causing unintended variation in standoff distance and poor build quality. While prior work has utilized contact probing to qualify standoff distance during processing, in situ methods for qualification of standoff distance are of major interest. The present work seeks to understand efficacy of image-based methods for classifying standoff distance variation in real-time using support vector machines (SVMs). It was hypothesized that the size of the melt pool and the amount of spatter will have significant correlations with deviations in the standoff distance; thus, SVMs were used on a dataset that is comprised of morphological features of melt pool size and image entropy. The SVM model was used to classify melt pool images into categories according to standoff distance variation from nominal. K-folds cross validation was used to find the optimal hyperparameters for the SVM model. To understand the impact of the selected features on the classification performance and inference speed, multiple models were trained with differing numbers of included features. Results for classification score, inference time, and image preprocessing/feature extraction from these data are reported. The present results show that the SVM model was able to predict the standoff distance classification with an accuracy of 97 percent and a speed of 0.122 s per image, making it a viable solution for real-time control of standoff distance.

Original languageEnglish
Title of host publicationAdditive Manufacturing; Biomanufacturing; Life Cycle Engineering; Manufacturing Equipment and Automation; Nano/Micro/Meso Manufacturing
PublisherAmerican Society of Mechanical Engineers
ISBN (Electronic)9780791885802
DOIs
StatePublished - 2022
EventASME 2022 17th International Manufacturing Science and Engineering Conference, MSEC 2022 - West Lafayette, United States
Duration: Jun 27 2022Jul 1 2022

Publication series

NameProceedings of ASME 2022 17th International Manufacturing Science and Engineering Conference, MSEC 2022
Volume1

Conference

ConferenceASME 2022 17th International Manufacturing Science and Engineering Conference, MSEC 2022
Country/TerritoryUnited States
CityWest Lafayette
Period06/27/2207/1/22

Funding

This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Advanced Manufacturing Office under contract number DE-AC05-00OR22725 and the Department of Energy award DE-EE0008303. The authors would also like to thank the Okuma America Corporation for their support of this project.

Keywords

  • additive manufacturing
  • direct energy deposition
  • feature importance
  • machine learning
  • melt pool
  • process control
  • process monitoring
  • random forest classifier
  • standoff distance
  • support vector machines

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