In situ monitoring of thin-wall build quality in laser powder bed fusion using deep learning

Aniruddha Gaikwad, Farhad Imani, Hui Yang, Edward Reutzel, Prahalada Rao

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

The goal of this work is to mitigate flaws in metal parts produced from the laser powder bed fusion (LPBF) additive manufacturing (AM) process. As a step toward this goal, the objective of this work is to predict the build quality of a part as it is being printed via deep learning of in situ layer-wise images acquired using an optical camera instrumented in the LPBF machine. To realize this objective, we designed a set of thin-wall features (fins) from titanium alloy (Ti-6Al-4V) material with a varying length-to-thickness ratio. These thin-wall test parts were printed under three different build orientations, and in situ images of their top surface were acquired during the process. The parts were examined offline using X-ray computed tomography (XCT), and their build quality was quantified in terms of statistical features, such as the thickness and consistency of its edges. Subsequently, a deep learning convolutional neural network (CNN) was trained to predict the XCT-derived statistical quality features using the layer-wise optical images of the thin-wall part as inputs. The statistical correlation between CNN-based predictions and XCT-observed quality measurements exceeds 85 %. This work has two outcomes consequential to the sustainability of AM: (1) it provides practitioners with a guideline for building thin-wall features with minimal defects, and (2) the high correlation between the offline XCT measurements and in situ sensor-based quality metrics substantiates the potential for applying deep learning approaches for the real-time prediction of build flaws in LPBF.

Original languageEnglish (US)
Pages (from-to)98-121
Number of pages24
JournalSmart and Sustainable Manufacturing Systems
Volume3
Issue number1
DOIs
StatePublished - 2019

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering

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