ORIGINAL ARTICLE Prediction of the deformation behavior of a selective laser-melted Ti-6Al-4V alloy as a function of process parameters Mostafa Mahdavi 1 & Elham Mirkoohi 2 & Eric Hoar 1 & Steven Liang 2 & Hamid Garmestani 1 Received: 29 January 2020 /Accepted: 13 April 2020 /Published online: 24 April 2020 # Springer-Verlag London Ltd., part of Springer Nature 2020 Abstract Recent studies have shown that the mechanical properties of Ti alloys produced by additive manufacturing (AM) methods are sensitive to AM process parameters. The mechanical threshold stress (MTS) model is capable of predicting the flow stress behavior of materials; however, the parameters needed in the MTS model are affected by the microstructure that originates from the AM process parameters. To find a relationship between the AM process parameters and the MTS parameters, the effect of process parameters on the mechanical properties of selective laser-melted (SLM) Ti-6Al-4V samples was studied. As the MTS model is sensitive to the microstructure, only near fully dense samples were considered. Keywords Ti-6Al-4V . Selective laser method . Additive manufacturing . Deformation behavior 1 Introduction Selective laser melting (SLM) is a common method used in additive manufacturing (AM) that is capable of fabricating near fully dense parts for a wide range of process parameters [1–3]. AM process parameters (e.g., laser power, scan speed, layer thickness, and hatch space) have a direct impact on the solidifi- cation process and consequently, microstructure and mechanical properties of the additively manufactured part [3–11]. There are many numerical and analytical models that can predict the me- chanical behavior of materials based on microstructural informa- tion [12–17]. However, these models can predict the mechanical behavior of materials based on microstructure. However, in the case of AM, the microstructure changes for having any change in the process parameters. Therefore, in AM, there is a need to define the resulting microstructure for any set of process param- eters and then use that microstructure in the numerical/analytical models which could be very time-consuming. On the other hand, there are some analytical models that can predict the mechanical behavior of materials in a short time with high accuracy using some empirical parameters for a specific material. In this regard, different experiments for different scenarios are needed to define the required parameters. For example, Kocks presented accurate formulations for the work-hardening behavior of a wide range of materials as a function of temperature and strain rate [18]. Furthermore, Mechnig and Kocks worked on the kinetics of flow stress and strain hardening that can be used for metal materials [19]. Then, Follansbee and Gray modified the Kocks/Mecking model to investigate the deformation behavior of Ti-6Al-4V at different temperatures and strain rates [20]. In these studies, the empirical parameters were extracted by running uniaxial tensile tests under different temperatures and strain rates. The big challenge for the implementation of these models into AM is the change in the built microstructure for different sets of AM process parameters that leads to changes in the empirical parameters. To overcome this problem, a model is needed that is able to take AM process parameters as inputs and the corresponding empirical parameters as outputs. In this regard, different additively manufactured parts must be pro- duced using different AM process parameters and mechanical tests must be performed on each part. This process helps to create a collection of AM process parameters and their asso- ciated deformation behavior. However, a model created in this way would be material-dependent. In order to expand this type of model to other material systems, there would need to be enough data to identify the relationship between the AM process parameters and material parameters for each new material system. * Mostafa Mahdavi mostafa.mahdavi@gatech.edu 1 School of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, GA, USA 2 Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA The International Journal of Advanced Manufacturing Technology (2020) 107:4069–4076 https://doi.org/10.1007/s00170-020-05330-w