Structural Integrity of Additively-Manufactured Parts
State-of-the-art and qualification challenges
Embedded Files
Friday, November 1, 2019
About the Presentation
About the Presentation
This presentation focuses on the ongoing challenges in qualification and certification of metallic parts fabricated via additive manufacturing (also known as 3D printing) techniques. Additive manufacturing (AM) is a common term used to describe any technology that manufactures physical 3D objects in a layer‐wise manner and through the addition of material, as opposed to the traditional subtractive methods.
Several AM processes, which vary in their method of layer manufacturing (e.g., blown powder or powder bed), feedstock material (e.g., metal or polymer), and form (e.g., powder or wire), have been developed in the past decades. Among various AM systems, more attention has been paid towards powder‐based metal AM in recent years by commercial and academic sectors.
Many industries, including aerospace and biomedical, are increasingly turning to the metal AM to fabricate customized parts with complex geometries, which traditional manufacturing techniques are unable to produce. However, there are several challenges against the full adoption of AM by these industries, fatigue and durability of the parts being one of the most important ones.
This presentation discusses the effects of part’s size, building orientation, post-manufacturing heat treatment, and surface finish on the fatigue and failure mechanisms of different AM alloys —including Ti-6Al-4V, Inconel 718, and stainless steels.
Successful fatigue-life prediction of AM materials using the crack-growth and multistage fatigue modeling approaches is also presented. The major barrier towards component-level nondestructive evaluation (NDE) of fatigue performance is the lack of tools for characterizing the AM process-induced defects (e.g., micro-cracks, porosity, lack-of-fusion).
This presentation covers a nondestructive qualification technique for accurately estimating the characteristics of process-induced defects (e.g., size, location, and distribution) and thus the fatigue performance, based on in-situ process signatures (i.e. real-time thermal history during the fabrication process). An NDE capability for predicting the fatigue resistance from in-process thermal signals, unique to each part, enables new opportunities for qualification and certification of AM parts for critical applications and dramatically reduces the time needed for the development of new products.
About the Presenter
About the Presenter
Aref Yadollahi is currently a research associate at the Center for Advanced Vehicular Systems (CAVS) at Mississippi State University (MSU). He received his Ph.D. in Mechanical Engineering from MSU in 2017.
Dr. Yadollahi’s current research interests include advanced/additive manufacturing (AM), Solid Mechanics, fatigue and fracture mechanics. His main research focus during the past few years has been on investigating the fatigue and fracture properties of AM metallic materials, quantifying their reliability, and developing tools for predicting their performance under monotonic and cyclic loading. His work in this area has been involved with extensive experimental testing as well as numerical and computational works.
Yadollahi has authored/co-authored more than 30 articles, which have been published in top-tier journals and peer-reviewed conference proceedings. He is also the recipient of several prestigious awards, such as the ASTM International Graduate Fellowship and the Best Graduate Student Research Award from MSU.
Report abuse