Abstract
Metal 3D printing techniques have become the primary additive manufacturing method for intricate and detailed designs, surpassing traditional techniques. These innovative methods allow for the use of novel fabrication techniques that enable greater intricacy, resolution, and detail compared to previous methods. In the aerospace industry, this printing technique is being developed to manufacture lightweight parts with complex designs. Similarly, in the biomedical engineering industry, metal 3D printing is used to manufacture implants and prosthetics that require precise designs and high-quality finishes. While research in conventional manufacturing has made advancements in the understanding of crack and defect propagation, there has been relatively less attention given to the dynamic attributes of metal 3D printed parts. If these parts are to be used in engineering applications, it is essential to have strict quality control and inspection in place to detect defects. To address this issue, this investigation proposes the use of the relative frequency shift (RFS) method, a non-destructive and efficient method for detecting and localizing internal voids in metal 3D printed parts. This study examines the vibration of an additively manufactured metal cantilever beam with a purposely placed square void, using analytical and Finite Element Methods (FEM). By considering a segmented and continuous beam, the relative frequency shifts were determined analytically. The location of voids with respect to modes and antinodes was observed to impact the magnitude of shift in each mode. To gather an array of frequency responses, void location along the beam was varied. Using an analytical transfer matrix method, a parametric map was created to display the frequency response of higher modes with respect to void location. This map is utilized to detect and locate internal voids along the length of the beam by comparing the observed shifts to those in the map and drawing inferences. This parametric map was validated using FEM. Through this investigation, it was discovered that every void location in 3D printed metal parts has a distinct dynamic “fingerprint” that can be utilized to precisely identify and localize defects. The effectiveness of the RFS method in detecting and localizing such defects is successfully demonstrated by this study. These findings have significant implications for the quality control and design optimization of metal 3D printed parts in various industries, including aerospace and biomedical engineering. By providing a non-destructive and efficient method for detecting defects, the RFS method can help to ensure the safety and reliability of 3D printed metal parts used in critical applications.
