In-situ monitoring of laser powder bed fusion process anomalies via a comprehensive analysis of off-axis camera data

Chaitanya Krishna Prasad Vallabh, Yubo Xiong, Xiayun Zhao

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

1 Scopus citations

Abstract

In-situ monitoring of a Laser Powder-Bed Fusion (LPBF) additive manufacturing process is crucial in enhancing the process efficiency and ensuring the built part integrity. In this work, we present an in-situ monitoring method using an off-axis camera for monitoring layer-wise process anomalies. The in-situ monitoring is performed with a spatial resolution of 512 × 512 pixels, with each pixel representing 250 × 250 µm and a relatively high data acquisition rate of 500 Hz. An experimental study is conducted by using the developed in-situ off-axis method for monitoring the build process for a standard tensile bar. Real-time video data is acquired for each printed layer. Data analytics methods are developed to identify layer-wise anomalies, observe powder bed characteristics, reconstruct 3D part structure, and track the spatter dynamics. A deep neural network architecture is trained using the acquired layer-wise images and tested by images embedded with artificial anomalies. The real-time video data is also used to perform a preliminary spatter analysis along the laser scan path. The developed methodology is aimed to extract as much information as possible from a single set of camera video data. It will provide the AM community with an efficient and capable process monitoring tool for process control and quality assurance while using LPBF to produce high-standard components in industrial (such as, aerospace and biomedical industries) applications.

Original languageEnglish
Title of host publicationAdditive Manufacturing; Advanced Materials Manufacturing; Biomanufacturing; Life Cycle Engineering; Manufacturing Equipment and Automation
ISBN (Electronic)9780791884256
DOIs
StatePublished - 2020
EventASME 2020 15th International Manufacturing Science and Engineering Conference, MSEC 2020 - Virtual, Online
Duration: 3 Sep 2020 → …

Publication series

NameASME 2020 15th International Manufacturing Science and Engineering Conference, MSEC 2020
Volume1

Conference

ConferenceASME 2020 15th International Manufacturing Science and Engineering Conference, MSEC 2020
CityVirtual, Online
Period3/09/20 → …

Keywords

  • Anomaly detection
  • Deep learning
  • In-situ process monitoring
  • Spatter analysis

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