In-situ porosity recognition for laser additive manufacturing of 7075-Al alloy using plasma emission spectroscopy

Wenjing Ren,Jyoti Mazumder

doi:10.1038/s41598-020-75131-4

Wenjing Ren, Jyoti Mazumder

Open Access

https://doi.org/10.1038/s41598-020-75131-4

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Abstract

Poor quality and low repeatability of additively manufactured parts are key technological obstacles for the widespread adoption of additive manufacturing (AM). In-situ monitoring and control of the AM process is vital to overcome this problem. This paper describes the combined artificial intelligence and plasma emission spectroscopy to identify the porosity of AM parts during the process. The time- and position-synchronized spectra were collected during the directed energy deposition (DED) manufacturing process of a 7075-Al alloy part. Eighteen features extracted from spectra were coupled with the deposition qualities which were characterized by the 3D X-ray Computed Tomography (CT) scan and used to train a Random Forest (RF) classifier. The well-trained RF classifier achieved up to 83% precision for the porosity recognition of depositions. The feature importance recorded by the RF classifier indicates that the intensities of spectra at the wavelength of 414.234 (Fe I) nm and 396.054 (Al I) nm, and the kurtosis of spectra at wavelength ranges of 484–490 nm and 508–518 nm, are the most effective features for porosity recognition. The physical correlations between spectra, porosity formation, and thermal accumulation during the AM process were analyzed. This study demonstrates the great potentials, as well as challenges of plasma emission spectroscopy for in-situ quality monitoring of laser AM which allows the enhancement of AM technique.

Highlights

Poor quality and low repeatability of additively manufactured parts are key technological obstacles for the widespread adoption of additive manufacturing (AM)
Some observations can be found: (1) The classifier trained by the data of the 2nd layer and the 3rd layer can recognize the porosity of other examples of these two layers with relatively high recognition precisions (83% for porous examples, 82% for fully dense examples and 82% for overall examples)
This work investigated the application of combined artificial intelligence and plasma emission spectroscopy for in-situ porosity recognition of additive manufacturing

Summary

Introduction

Poor quality and low repeatability of additively manufactured parts are key technological obstacles for the widespread adoption of additive manufacturing (AM). This study demonstrates the great potentials, as well as challenges of plasma emission spectroscopy for in-situ quality monitoring of laser AM which allows the enhancement of AM technique. Similar to Coeck’s work, the present investigations of in-situ porosity recognition mainly focus on the non-fusion type porosity caused by abnormal process parameters The principle in these studies is that non-fusion type porosity mainly occurs along with changes of geometry or temperature in the melt pool which can be detected by optical cameras or thermal s ensors[12]. They have demonstrated that emission spectroscopy is effective for in-situ monitoring of feedstock composition[15], phase transformation[16], and residual s tress[17] of parts In their recent patents, spectral features including spectral line intensity, line-to-line ratio, and plasma temperature are used for the closed-loop control in a Direct Energy Deposition (DED) system. The main limitation of this study is that it recognizes the porosity layer-by-layer which makes it not applicable for real-time defects correction

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