An Intelligent Metrology Architecture With AVM for Metal Additive Manufacturing
Haw Ching Yang, Muhammad Adnan, Chih-Hung Huang, Fan-Tien Cheng, Yu-Lung Lo, Chih-Hua Hsu
Abstract
The capability of measuring melt pool variation is the key evaluating metal additive manufacturing quality. To measure the variation, a metrology architecture with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> 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 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> metrology for quality estimation requirement. The purpose of this letter is to propose an intelligent metrology architecture with an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> 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.
BibTeX
@inproceedings{ral2019_anintelligentmet,
title = {An Intelligent Metrology Architecture With AVM for Metal Additive Manufacturing},
author = {Haw Ching Yang and Muhammad Adnan and Chih-Hung Huang and Fan-Tien Cheng and Yu-Lung Lo and Chih-Hua Hsu},
booktitle = {RA-L 2019},
year = {2019}
}