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Research Article
Aug 27, 2021

Data-Driven Sensitivity Analysis for Static Mechanical Properties of Additively Manufactured Ti–6Al–4V

Publication: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering
Volume 8, Issue 1

Abstract

Additive manufacturing (AM) has been extensively investigated in recent years to explore its application in a wide range of engineering functionalities, such as mechanical, acoustic, thermal, and electrical properties. A data-driven approach is proposed to investigate the influence of major fabrication parameters in the laser-based additively manufactured Ti–6Al–4V. Two separate laser-based powder bed fusion techniques, i.e., selective laser melting (SLM) and direct metal laser sintering (DMLS), have been investigated and several data regarding the tensile properties of Ti–6Al–4V alloy with their corresponding fabrication parameters are collected from open literature. Statistical data analysis is performed for four fabrication parameters (scanning speed, laser power, hatch spacing, and powder layer thickness) and three postfabrication parameters (heating temperature, heating time, and hot isostatically pressed or not) which are major influencing factors and have been investigated by several researchers to identify their behavior on the static mechanical properties (i.e., yielding strength, ultimate tensile strength, and elongation). To identify the behavior of the relationship between the input and output parameters, both linear regression analysis and artificial neural network (ANN) models are developed using 53 and 100 datasets for SLM and DMLS processes, respectively. The linear regression model resulted in an average R squared value of 0.351 and 0.507 compared to 0.908 and 0.833 in the case of nonlinear ANN modeling for SLM and DMLS based modeling, respectively. Both local and global sensitivity analyses are carried out to identify the important factors for future optimal design. Based on the current study, local sensitivity analysis (SA) suggests that SLM is most sensitive to laser power, scanning speed, and heat treatment temperature while DMLS is most sensitive to heat treatment temperature, hatch spacing, and laser power. In the case of DMLS fabricated Ti–6Al–4V alloy, laser power, and scan speed are found to be the most impactful input parameters for tensile properties of the alloy while heating time turned out to be the least affecting parameter. The global sensitivity analysis results can be used to tailor the alloy's static properties as per the requirement while results from local sensitivity analysis could be useful to optimize the already tailored design properties. Sobol's global sensitivity analysis implicates laser power, heating temperature, and hatch spacing to be the most influential parameters for alloy strength while powder layer thickness followed by scanning speed to be the prominent parameters for elongation for SLM fabricated Ti–6Al–4V alloy. Future work would still be needed to eradicate some of the limitations of this study related to limited dataset availability. This article is available in the ASME Digital Collection at https://doi.org/10.1115/1.4051799.

Information & Authors

Information

Published In

Go to ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering
Volume 8Issue 1March 2022

History

Received: Nov 9, 2020
Revision received: Jul 13, 2021
Published online: Aug 27, 2021
Published in print: Mar 1, 2022

Authors

Affiliations

Antriksh Sharma
School for Engineering of Matter, Transport, and Energy, Arizona State University, Tempe, AZ 85287
Jie Chen
School for Engineering of Matter, Transport, and Energy, Arizona State University, Tempe, AZ 85287
Evan Diewald
Mechanical Engineering Department, Carnegie Mellon University, Pittsburgh, PA 15213
Anahita Imanian
Technical Data Analysis, Inc., Falls Church, VA 22042
Jack Beuth
Mechanical Engineering Department, Carnegie Mellon University, Pittsburgh, PA 15213
Yongming Liu [email protected]
School for Engineering of Matter, Transport, and Energy Arizona State University, Tempe, AZ 85287 e-mail: [email protected]

Funding Information

Naval Air Systems Command10.13039/100010464: N68335-20-C-0477

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