Monitoring of functionally graded material during laser directed energy deposition by acoustic emission and optical emission spectroscopy using artificial intelligence

Abstract

Laser directed energy deposition (L-DED) allows the production of multi-materials and functionally graded material (FGM) parts. But for commercialisation, process and quality monitoring of parts is required. For the first time, a novel monitoring method for chemical composition and process regimes of FGMs is proposed using a cost-effective acoustic emission (AE) (microphone) and optical emission spectroscopy (OES) sensors. Four chemical compositions (100%Ti, 58%Ti42%Nb, 37%Ti63%Nb, and 100% Nb) and two process parameters (475 W – 1'400 mm/min and 175 W – 2'000 mm/min) were selected, leading to four regimes/quality (conduction mode, partial, minor, medium, and severe lack of fusion pores). The signals were classified using seven mainstream artificial intelligence algorithms. The main conclusions are twofold. First, microphones are unsuitable candidates for monitoring the laser-material interaction during L-DED. The acoustic waves generated by the laser-material interaction are shielded by high gas flow surrounding it and so are either highly disturbed or does not reach the microphone. Conversely, OES are suitable candidates as the classification accuracies are higher than 90% for most category and machine learning algorithms, even after drastic feature reduction. Considering the wide range of chemical composition and quality, our proposed methods using OES have high industrialised potentials for them during L-DED FGM.

Keywords

• functionally graded material
• laser directed energy deposition
• acoustic emission
• optical emission spectroscopy
• artificial intelligence