Additive manufacturing (AM) provides a greater level of flexibility to build parts with complex structures than the traditional subtractive manufacturing. However, the more complex the engineering design is, the greater challenge is posed on the AM machine. To cope with such complexity, advanced imaging is increasingly invested to increase the information visibility. There is an urgent need to leverage the available imaging data to investigate the interrelationships between design complexity and quality characteristics of AM builds. This article presents a design of experiments on the laser powder bed fusion machine to investigate how design parameters (i.e., recoating orientation, hatching pattern, width, and height) influence edge roughness in thin-wall structures of the final builds. First, we perform the postbuild inspection of final builds and collect large amounts of X-ray computed tomography (XCT) images. Second, we integrate the computer-Aided designs with XCT images for image registration and then characterize the edge roughness of each layer in a thin wall of the AM build. Finally, we perform an analysis of variance with respect to design parameters and develop a regression model to predict how build design impacts the edge roughness in each layer of the thin-wall structures. Experimental results show that edge roughness is sensitive to recoating orientations, width, and hatching patterns. This article sheds insights on the optimization of engineering design to improve the quality of AM builds.
All Science Journal Classification (ASJC) codes
- Electrical and Electronic Engineering