Layerwise in-process quality monitoring in laser powder bed fusion

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

18 Scopus citations

Abstract

The goal of this work is to understand the effect of process conditions on part porosity in laser powder bed fusion (LPBF) Additive Manufacturing (AM) process, and subsequently, detect the onset of process conditions that lead to porosity from in-process sensor data. In pursuit of this goal, the objectives of this work are two-fold: (1) Quantify the count (number), size and location of pores as a function of three LPBF process parameters, namely, the hatch spacing (H), laser velocity (V), and laser power (P). (2) Monitor and identify process conditions that are liable to cause porosity through analysis of in-process layer-bylayer optical images of the build invoking multifractal and spectral graph theoretic features. This is important because porosity has a significant impact on the functional integrity of LPBF parts, such as fatigue life. Furthermore, linking process conditions to sensor signatures and defects is the first-step towards in-process quality assurance in LPBF. To achieve the first objective, titanium alloy (Ti-6Al- 4V) test cylinders of 10 mm diameter × 25 mm height were built under differing H, V, and P settings on a commercial LPBF machine (EOS M280). The effect of these parameters on count, size and location of pores was quantified based on X-ray computed tomography (XCT) images. To achieve the second objective, layerwise optical images of the powder bed were acquired as the parts were being built. Spectral graph theoretic and multifractal features were extracted from the layer-by-layer images for each test part. Subsequently, these features were linked to the process parameters using machine learning approaches. Through these image-based features, process conditions under which the parts were built was identified with the statistical fidelity over 80% (F-score).

Original languageEnglish (US)
Title of host publicationAdditive Manufacturing; Bio and Sustainable Manufacturing
PublisherAmerican Society of Mechanical Engineers (ASME)
ISBN (Print)9780791851357
DOIs
StatePublished - 2018
EventASME 2018 13th International Manufacturing Science and Engineering Conference, MSEC 2018 - College Station, United States
Duration: Jun 18 2018Jun 22 2018

Publication series

NameASME 2018 13th International Manufacturing Science and Engineering Conference, MSEC 2018
Volume1

Other

OtherASME 2018 13th International Manufacturing Science and Engineering Conference, MSEC 2018
CountryUnited States
CityCollege Station
Period6/18/186/22/18

All Science Journal Classification (ASJC) codes

  • Industrial and Manufacturing Engineering

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