Relative Density Measurement of PBF-Manufactured 316L and AlSi10Mg Samples via Eddy Current Testing

Abstract

Powder bed fusion (PBF) is the most commonly used additive manufacturing process for fabricating complex metal parts via the layer-wise melting of powder. Despite the tremendous recent technological development of PBF, manufactured parts still lack consistent quality in terms of part properties such as dimensional accuracy, surface roughness, or relative density. In addition to process-inherent variability, this is mainly owing to a knowledge gap in the understanding of process influences and the inability to adequately control them during part production. Eddy current testing (ECT) is a well-established nondestructive testing technique primarily used to detect near-surface defects and measure material properties such as electrical conductivity in metal parts. Hence, it is an appropriate technology for the layer-wise measuring of the material properties of the fused material in PBF. This study evaluates ECT’s potential as a novel in situ monitoring technology for relative part density in PBF. Parts made from SS316L and AlSi10Mg with different densities are manufactured on a PBF machine. These parts are subsequently measured using ECT, as well as the resulting signals correlated with the relative part density. The results indicate a statistically significant and strong correlation (316L: r(8) = 0.998, p < 0.001, AlSi10Mg: r(8) = 0.992, p < 0.001) between relative part density and the ECT signal component, which is mainly affected by the electrical conductivity of the part. The results indicate that ECT has the potential to evolve into an effective technology for the layer-wise measuring of relative part density during the PBF process.

Laser powder bed fusion of AlSi10Mg-based composites with graphene and nanodiamond additions

“…All samples were manufactured in an N 2 atmosphere. The development of a suitable processing window for the nanodiamond and graphene modified composites is based on a known processing window for standard AlSi10Mg powder material as used by Spurek et al [25]. The optimization aims to maximize the relative material density considering the specific volumetric energy input E V into the material…”

Application of machine learning in polymer additive manufacturing: A review

“…In order to assess the quality of SLS printed parts, numerous non‐destructive characterization techniques have been shown to be useful. Such characterization techniques include XCT, 236 micro computed tomography (micro‐CT), 237 eddy current testing, 238 and acoustic emission 239 . Multiple techniques are necessary to confidently determine the quality of printed parts, which requires time and expertise.…”

Advanced Characterization and On-Line Process Monitoring of Additively Manufactured Materials and Components

“…Finally, the discussion is extended to component level, whereby defects [13] and residual stress [14] are determined in relevant industrial cases.…”