<p style="text-align: justify">Additive Manufacturing (AM) is increasingly being considered for fabrication of components with complex geometries in various industries such as aerospace and healthcare. Control of surface roughness of components is thus a crucial aspect for more widespread adoption of AM techniques. However, estimating the internal (or ‘far-side’) surface roughness of components is a challenge, and often requires sophisticated techniques such as X-ray computed tomography, which are difficult to implement online. Although ultrasound could potentially offer a solution, grain noise and inspection surface conditions complicate the process.</p><p style="text-align: justify">This paper studies the feasibility of using Artificial Intelligence (AI) in conjunction with ultrasonic measurements for rapid estimation of internal surface roughness in AM components, using numerical simulations. In the first models reported here, a pulse-echo configuration is assumed, whereby a specimen sample with rough surfaces is insonified with bulk ultrasonic waves and the backscatter is used to generate line-scan images. Simulations are carried out for various combinations of the model parameters, yielding a large number of such line-scans. A neural network algorithm is then created and trained on a subset of the datasets so generated using simulations, and later used to predict the roughness from the rest. The results demonstrate the immense potential of this approach in inspection automation for rapid roughness assessments in AM components, based on ultrasonic measurements.</p>

Estimation of Internal Surface Roughness of Additively Manufactured Components Under Complex Conditions Using Artificial Intelligence and Measurements of Ultrasonic Backscatter

Paper Proceedings: https://asmedigitalcollection.asme.org/qnde/QNDE2021/volume/85529

ASME 2021 48th Annual Review of Progress in Quantitative Nondestructive Evaluation (QNDE2021)

Supporting the need among researchers worldwide for additional online and multimedia content, ASME now offers the ASME Conference Video Collection. This platform will collect and provide long-term access to conference presentation videos, encompassing all technology areas within ASME's technical conference and events program. About 30,000-40,000 engineers across all mechanical engineering and related disciplines participate in ASME conferences each year. This includes authors of approximately 6,000 to 8,000 technical papers published by ASME, the majority of whom also submit video presentations for our virtual conferences. The video collection will be available as an add-on to existing product packages or as a separate subscription.