Research Update

Defect Detection in AM Process (LPBF) (3)

Feature-based characterization of signature topography in laser powder bed fusion of metals [1]

Figure 1. Identification of spatters from the surface data

Senin et al. proposed a feature-based algorithmic characterization method to isolate topographic formations and quantify their dimensional and topography measurement. This study uses an algorithmic approach to measure the topology information of the Ti6A14V sample fabricated with the LPBF process. After obtaining 3D images of the sub-millimeter level surface area using a coherence scanning interferometer (Zygo Newview 8300), the method of measuring the width and height of weld tracks and the area and height of spatters observed on the surface is introduced. Morphological operators (especially dilation and erosion) played a pivotal role in identifying spatters (i.e., balling defects) from the surface data. Here is the research question: **Can we use morphological operators as a spatter detector from surface normals (even though this approach has processed a binary edge map)? **

References:

[1] Senin, N., Thompson, A. and Leach, R., 2018. Feature-based characterisation of signature topography in laser powder bed fusion of metals. Measurement Science and Technology, 29(4), p.045009.