Defect Analysis of 316 L Stainless Steel Prepared by LPBF Additive Manufacturing Processes

Abstract

The 316 L stainless-steel samples were prepared by laser powder bed fusion (LPBF). The effects of processing parameters on the density and defects of 316 L stainless steel were studied through an orthogonal experiment. The density of the samples was measured by the Archimedes method, optical microscopy (OM) and X-ray Computed Tomography (XCT). The microstructures and defects under different LPBF parameters were studied by OM and SEM. The results show that the energy density has a significant effect on the defect and density of the structure. When the energy density is lower than 35.19 J/mm3, the density increases significantly with the increase of energy density. However, when the energy density is larger than this value, the density remains relatively stable. The process parameter with the greatest influence on energy density is the hatch distance D, followed by laser power P, scanning speed V and rotation angle θ. In this paper, the optimum parameters consist of P = 260 W, V = 1700 mm, D = 0.05 mm and θ = 67°, in which the density is as high as 98.5%. In addition, the possibility and accuracy of the XCT method in detecting the discontinuity and porosity of 316 L stainless steel were discussed. The results show that XCT can provide the whole size and variation trend of pores in the different producing direction of LPBF.

Mitigation of Gas Porosity in Additive Manufacturing Using Experimental Data Analysis and Mechanistic Modeling

“…[17] SS 316 Adjustment of the process variables such as point distance, exposure time, and layer thickness during experiments lowered porosity. [18] Ti6Al4V Laser post-processing decreased gas pores which was confirmed by micro-CT examination. [19] Ti6Al4V Gas pores were eliminated by post-process hot isostatic pressing at different temperatures and pressures.…”

“…Several attempts have been made to mitigate gas porosity in LPBF parts (Table 1) using experimental techniques [18][19][20][21][22][23][24][25], mechanistic modeling [26][27][28][29][30][31][32][33][34], and machine learning [35][36][37][38][39][40][41]. However, experimental trial-and-error to adjust many process variables for reducing gas porosity is expensive and time-consuming.…”

Design and Characterization of Innovative Gas-Atomized Al-Si-Cu-Mg Alloys for Additive Manufacturing

“…Despite recent advancements, one of the main limitations of MAM technology is the limited availability of processable materials. Typically, stainless steel, Ti6Al4V, Inconel 625, Inconel 718, and AlSi10Mg [3,[10][11][12][13][14][15] are the most extensively studied materials. Among these alloys, aluminum alloys stand out as some of the most interesting materials due to their low specific weight, excellent strength-to-weight ratio, intrinsic corrosion resistance, good thermal and electrical conductivity, and optimal formability and machinability [16].…”

Multi-Task Learning of Scanning Electron Microscopy and Synthetic Thermal Tomography Images for Detection of Defects in Additively Manufactured Metals

“…The approaches to quality control of AM structures involve in situ monitoring of structure layers during printing [8][9][10][11], and ex situ evaluation of the final printed structures [12][13][14][15]. Regardless of in situ monitoring results, the detection and classification of material defects in the final structure prior to deployment in a nuclear reactor is necessary because of the stringent safety requirements of nuclear energy.…”