Imaging material texture of as-deposited selective laser melted parts using spatially resolved acoustic spectroscopyTools Patel, Rikesh, Hirsch, Matthias, Dryburgh, Paul, Pieris, Don, Achamfuo-Yeboah, Samuel, Smith, Richard J., Light, Roger A., Sharples, Steve D., Clare, Adam T. and Clark, Matt (2018) Imaging material texture of as-deposited selective laser melted parts using spatially resolved acoustic spectroscopy. Applied Sciences, 8 (10). 1991/1-1991/15. ISSN 2076-3417
Official URL: https://www.mdpi.com/2076-3417/8/10/1991
AbstractAdditive manufacturing (AM) is a production technology where material is accumulated to create a structure, often through added shaped layers. The major advantage of additive manufacturing is in creating unique and complex parts for use in areas where conventional manufacturing reaches its limitations. However, the current class of AM systems produce parts that contain structural defects (e.g., cracks and pores) which is not compatible with certification in high value industries. The probable complexity of an AM design increases the difficulty of using many non-destructive evaluation (NDE) techniques to inspect AM parts—however, a unique opportunity exists to interrogate a part during production using a rapid surface based technique. Spatially resolved acoustic spectroscopy (SRAS) is a laser ultrasound inspection technique used to image material microstructure of metals and alloys. SRAS generates and detects ‘controlled’ surface acoustic waves (SAWs) using lasers, which makes it a non-contact and non-destructive technique. The technique is also sensitive to surface and subsurface voids. Work until now has been on imaging the texture information of selective laser melted (SLM) parts once prepared (i.e., polished with Ra < 0.1 um)—the challenge for performing laser ultrasonics in-process is measuring waves on the rough surfaces present on as-deposited parts. This paper presents the results of a prototype SRAS system, developed using the rough surface ultrasound detector known as speckle knife edge detector (SKED)—texture images using this setup of an as-deposited Ti64 SLM sample, with a surface roughness of Sa ~6um, were obtained.
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