Abstract
The hybrid manufacturing (HM) process combines the precision of computer numerical control (CNC) and the freeform capability of additive manufacturing to expand the versatility of advanced manufacturing. The intent of this paper is to explore the relationship between HM processing parameters and mechanical properties of the final parts manufactured by one type of HM process that combines laser metal deposition (LMD) and CNC milling. The design of experiment (DOE) is implemented to explore the Ti-6Al-4V thin-wall structure fabrication process with different HM build strategies. Vickers hardness, tensile test, and microstructure analyses are conducted to evaluate the mechanical property variance within the final parts fabricated according to the DOE matrix. Finally, a prediction model of yield strength at 0.2% offset for Ti-6Al-4V parts built through the aforementioned HM process was obtained by an analysis of variance (ANOVA) test, which revealed the significant factors are build height within each LMD process, laser energy input, and the interaction of build height within each LMD process to the preheating condition.
Recommended Citation
L. Yan et al., "Mechanical Properties Evaluation of Ti-6Al-4V Thin-Wall Structure Produced by a Hybrid Manufacturing Process," Proceedings of the 29th Annual International Solid Freeform Fabrication Symposium (2018, Austin, TX), pp. 291 - 301, University of Texas at Austin, Aug 2018.
Meeting Name
29th Annual International Solid Freeform Fabrication Symposium -- An Additive Manufacturing Conference, SFF 2018 (2018: Aug. 13-15, Austin, TX)
Department(s)
Materials Science and Engineering
Second Department
Mechanical and Aerospace Engineering
Research Center/Lab(s)
Intelligent Systems Center
Document Type
Article - Conference proceedings
Document Version
Final Version
File Type
text
Language(s)
English
Publication Date
15 Aug 2018
Comments
This project was supported by The Boeing Company through the Center for Aerospace Manufacturing Technologies (CAMT), National Science Foundation Grants #CMMI-1547042 and CMMI-1625736, and the Intelligent Systems Center (ISC) at Missouri S&T. Their financial support is greatly appreciated.