Additive manufacturing is known for producing complex metal components, particularly with materials like 316L stainless steel. However, ensuring the quality and microstructural consistency of such components remains a challenge, as traditional testing methods are often destructive and time-intensive. Data driven models that are used for non-destructive evaluation are often difficult to interpret. This study explores the use ultrasound measurements combined with a multivariate statistical technique (partial least squares), to estimate the material properties of steel samples and examining the relationships between ultrasound signals at various frequencies and material properties such as porosity, grain size, and hardness. This aims to enhance the interpretability of ultrasound testing for additive manufacturing. Our findings indicate that ultrasound backscatter can be effectively linked to key material properties.