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Published on: April 7, 2021
Size-Dependent Mechanical Properties of Additively Manufactured Ti-6Al-4V Thin Walls
Tenglong Xie1, Chao Ding1, Peng Wang1
1Zhejiang Key Laboratory of Aerospace Metallic Materials, Hangzhou City University, Hangzhou 310015, China.
Abstract:
Additively manufactured lightweight lattice structures typically consist of thin walls with thicknesses ranging from hundreds of micrometers to millimeters. Within this range, such thin walls exhibit a pronounced size effect. Despite extensive research on the topic, a clear mapping between key influencing factors and mechanical properties remains lacking. This gap makes it challenging to accurately predict mechanical performance across different wall thicknesses, especially for those below 500 μm. In this work, Ti-6Al-4V thin-walled tensile specimens with thicknesses ranging from 0.2 mm to 1.0 mm were fabricated via laser powder bed fusion (LPBF). The variations in mechanical properties, microstructure, surface defects, and internal defects were investigated. The results indicate that yield strength (YS) and ultimate tensile strength (UTS) decreased significantly as thickness decreased, dropping from 794.1 MPa to 471.7 MPa and from 910.7 MPa to 485.2 MPa, respectively. Printing defects were identified as the dominant factors governing the size effect: strength was jointly affected by surface and internal defects, whereas failure mode and ductility were primarily governed by internal defects. By introducing an effective thickness ratio parameter, a semi-empirical predictive model was developed to characterize the strength-thickness relationship and to quantify the individual contributions of surface defects and other coupled interior factors. Subsequently, ultra-thin specimens were subjected to surface grinding and polishing to alleviate surface defects, leading to improvements in YS and UTS of approximately 27-39% and 22-45%, respectively. The model-predicted strengths of the surface-treated specimens were in good agreement with the measured values, further validating the effectiveness of the proposed model.
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