| Abstract Scope |
Metal powder defects in additive manufacturing can affect product quality. We present a non-destructive evaluation method sensitive to thermal properties at adjustable depths. By modulating the energy source and analyzing temperature responses in the frequency domain, we assess thermal properties and detect defects in metal powders and printed materials. Our setup identifies distinct thermal responses tied to material features like core detection, age, oxygen content, and particle size distribution. This method works with powder bed fusion lasers across materials such as Cu, AlSi10Mg, In718, SS316L, Ti64 G23, and G5. Frequency-domain measurements offer reduced noise compared to traditional methods. Utilizing machine learning, we identify core, age, oxidation, thickness, and size distribution, enhancing quality control and process monitoring. |