# Imaging Microstructure on Optically Rough Surfaces Using Spatially Resolved Acoustic Spectroscopy
###### Abstract  
The microstructure of a material defines many of its mechanical properties. Tracking the microstructure of parts during their manufacturing is needed to ensure the designed performance can be obtained, especially for additively manufactured parts. Measuring the microstructure non-destructively on real parts is challenging for optical techniques such as laser ultrasound, as the optically rough surface impacts the ability to generate and detect acoustic waves. Spatially resolved acoustic spectroscopy can be used to measure the microstructure, and this paper presents the capability on a range of surface finishes. We discuss how to describe 'roughness' and how this influences the measurements. We demonstrate that measurements can be made on surfaces with \(R_{a}\) up to 28 \(\upmu\)m for a selection of roughness comparators. Velocity images on a range of real surface finishes, including machined, etched, and additively manufactured finishes in an as-deposited state, are presented. We conclude that the \(R_{a}\) is a poor descriptor for the ability to perform measurements as the correlation length of the roughness has a large impact on the ability to detected the surface waves. Despite this issue, a wide range of real industrially relevant surface conditions can be measured. SRAS; rough surfaces; laser ultrasound; microstructure imaging; orientation imaging +
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(WAAM) uses wire feed-stock and an electrical arc to melt and weld the new material to the previously built layers [1]. Wire laser AM (WLAM) is similar to WAAM except that the wire feedstock is melted by a high-power laser beam [2]. They have relatively fast deposition rates, with rates in excess of 10 kg/h possible; however, this comes at the cost of resolution and part complexity. Laser powder bed fusion (L-PBF) uses a laser to melt metal powder a layer at a time to build the part. This has high spatial resolution, allowing for very complex geometries to be built, but with smaller build volumes and lower rates of deposition compared to \(\mathrm{WAAM}<200\,\mathrm{g/h}\)[3]. A multitude of defect mechanisms threaten the integrity of a build, which can include obvious geometrical errors to the more subtle internal porosity [4], micro-cracking [1], and heterogeneous microstructure formation [5]. The ability to determine the material state is a limiting factor in the application of these manufacturing methods to safety-critical parts despite the wealth of advantages they have over traditional manufacturing methods. It is useful to break the types of material non-conformity down into two categories, defects and microstructural. The first is concerned with physical defects both through the volume and on the surface, for example, cracking and porosity. For the detection of such issues, there is a mature non-destructive evaluation (NDE) industry, utilising tools including eddy current [6] and dye penetrant [7] for surface defects, whilst internal defects and porosity are inspected by X-ray [8] and conventional ultrasound. Processing steps for both traditional and additive manufacturing affect the microstructure of the final part and can cause deviations between the intended crystalline microstructure and that of the real part. Common microstructural non-conformities include grain size, texturing, and the presence of microtextured regions, all of which act to reduce the performance of the part. Traditional statistical qualification processes for metallic materials are well defined [9], requiring extensive testing, which can take several years to complete with costs running into the millions [10]. This is typically based around mechanical testing to establish the components yield and ultimate tensile strength [11] and is performed over a large number of specimens to obtain high confidence in performance across each build set.