Nondestructive Evaluation with Laser Ultrasound of Powder Bed Fusion Printed Metal

Abstract

Many of the main advantages of 3D printing metal components, for example the possibility to manufacture parts of high geometric complexity in small series, typically make the nondestructive quality control difficult and resource intense. A number of published studies have proposed in-process nondestructive evaluation of the printed material, as it is built layer by layer, as a possible general approach solution to this difficulty. Previous studies have also indicated that the non-contact nondestructive testing method laser ultrasound might be an applicable method to conduct such an in-process nondestructive evaluation of 3D printed parts. Potential pros of such an ultrasonic based evaluation, as compared to more process monitoring like approaches (e.g. acoustic emission from the printing process) would for example be increased defect characterization capabilities.In this work laser ultrasonic nondestructive evaluation of both electron beam and laser beam powder bed fusion printed metal is demonstrated. Nickel-base and Stainless Steel samples are evaluated both from a machined surface and, in order to simulate the in-process setup, from the as-printed top surface.The laser ultrasonic evaluation results are then compared to results from other material characterization methods, such as light optical microscopy and X-ray inspection. Designed artificial defects as well as process material anomalies could be detected with the proposed laser ultrasonic evaluation. In some cases material defects could be detected also when the laser ultrasonic evaluation was performed from the as-printed top surface.Our results are similar to other studies that have been reported on the subject: laser ultrasound can be utilized to detect material anomalies of interest in powder bed fusion printed metal material. Further research is required in order to better understand and improve the capability and reliability of the nondestructive evaluation method.QC 20191001</p