An Intelligent Metrology Architecture With AVM for Metal Additive Manufacturing

The capability of measuring melt pool variation is the key evaluating metal additive manufacturing quality. To measure the variation, a metrology architecture with in situ melt pool measurement and an estimation module is required. However, it is a challenge to effectively extract significant featur...

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Bibliographic Details
Published inIEEE robotics and automation letters Vol. 4; no. 3; pp. 2886 - 2893
Main Authors Yang, Haw-Ching, Adnan, Muhammad, Huang, Chih-Hung, Cheng, Fan-Tien, Lo, Yu-Lung, Hsu, Chih-Hua
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 01.07.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Adaptive control
Additive manufacturing
Architecture
AVM
Cameras
Feature extraction
in-situ metrology
intelligent metrology architecture
melt pool
Metal additive manufacturing
Metals
Metrology
Modules
Process parameters
Signal processing
Surface roughness
Temperature measurement
Three-dimensional printing
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Summary:The capability of measuring melt pool variation is the key evaluating metal additive manufacturing quality. To measure the variation, a metrology architecture with in situ melt pool measurement and an estimation module is required. However, it is a challenge to effectively extract significant features from the huge data collected by the in situ metrology for quality estimation requirement. The purpose of this letter is to propose an intelligent metrology architecture with an in situ metrology (ISM) module and an enhanced automatic virtual metrology (AVM) system. The ISM module can extract the melt pool features with a coaxial camera and a pyrometer. On the other hand, the AVM system is improved with a feature selection method to solve the issue of limited samples in the component modeling quality. The examples with different metals are adopted to illustrate how the system works for estimating surface roughness and density of components, and, in the future, the system can even serve as the feedback signal for adaptive control of the process parameters by layering in an additive manufacturing system.
ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2019.2921927