Microstructural simulation of friction stir welding using a cellular automaton method: a microstructure prediction of AZ91 magnesium alloy

Asadi et al. International Journal of Mechanical and Materials Engineering (2015) 10:20 DOI 10.1186/s40712-015-0048-5 ORIGINAL ARTICLE Open Access Microstructural simulation of friction stir welding using a cellular automaton method: a microstructure prediction of AZ91 magnesium alloy Parviz Asadi1*, Mohammad Kazem Besharati Givi1 and Mostafa Akbari2 Abstract Background: Recently, some researchers have simulated FSW using FEM and studied the influence of process parameters and tool geometry on material flow, welding force, and temperature and strain distributions during friction stir processing. Additionally, in terms of microstructure modeling, various approaches such as the Cellular Automaton (CA) model have been developed to simulate microstructural evolution during plastic deformation processes. Method: In this work, a finite element model (FEM) is established to study the microstructure evolution during friction stir welding (FSW) of AZ91 magnesium alloy. To this aim, first, the hot compression tests at different temperatures and strain rates were carried out to achieve the flow stress curves. Then, the hardening parameter, the recovery parameter and the strain rate sensitivity were calculated according to flow stress results and using the Kocks−Mecking model. Next, a continuum based thermo-mechanically coupled rigid-viscoplastic FEM model was proposed in Deform-3D software to simulate the FSW of AZ91 magnesium alloy. To evaluate microstructure of the weld zone a model is proposed based on the combination of Cellular Automaton and Laasraoui-Jonas models. Results: Temperature history, strain distribution and welding force are achieved through thermomechanical model and microstructure and grain size distribution are achieved by microstructure evolution model. The effects of rotational and traverse speeds on the grain size and microstructure of weld zone are considered. Conclusion: There is a good agreement between results of numerical models and experiments in the aspects of welding forces, temperature history and grain size. Additionally, the proposed microstructure evolution model can simulate accurately the dynamic recrystallization (DRX) process during FSW and its resulted microstructure. Keywords: FSW simulation; Microstructural evolution; DRX; Cellular automaton; Laasraoui-Jonas model Background AZ91, a magnesium alloy, is one of the most commercially and commonly used magnesium alloys. This alloy, containing 9 wt% Al, 1 wt% Zn, and 0.2–0.3 wt% Mn as major alloying elements, contains a good combination of castability, mechanical strength, and ductility (Suresh et al. 2009). This has made AZ91 a popular light metal alloy especially among automotive industries whose aim is manufacturing lightweight vehicles (Srinivasan et al. * Correspondence: 1 School of Mechanical Engineering, College of Engineering, University of Tehran, Kargarshomali St, Po Box: 11155/4563, Tehran, Iran Full list of author information is available at the end of the article 2010). However, the use of AZ91 in different industries is not yet extended comparing to its competitors such as aluminum alloys and plastics, partially due to the difficulty in controlling its microstructure (Asadi et al. 2010a). Friction stir welding (FSW) as a relatively new welding technique has gained wide applications in different industries such as aerospace, automotive, and maritime. It has been utilized to weld and process different aluminum (Heidarzadeh et al. 2015), Mg (Asadi et al. 2012; 2010b; Motalleb-nejad et al. 2014; Faraji and Asadi 2011), and Cu (Farrokhi et al. 2013) alloys, some of © 2015 Asadi et al. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Asadi et al. International Journal of Mechanical and Materials Engineering (2015) 10:20 which are classified as practically unweldable alloys in use of conventional welding methods. Recently, some researchers have simulated FSW using a finite element model (FEM). Buffa et al. (2013; 2012; 2006) simulated friction stir welding using a 3D finite element method. Their model effectively determines the relationships between the tool forces and process parameters. Shojaeefard et al. (2013) studied the influence of pin profile and shoulder diameter on material flow, welding force, temperature, and strain distributions. Marzbanrad et al. (2014) investigated the effect of tool pin profile, and Asadi et al. (2011a;Tutunchilar et al. 2012a) studied the effect of the process parameters on material flow, temperature, and strain distributions during friction stir processing. It is clear that the grain size in the weld zone has a great influence on the mechanical properties of weld such as hardness, tensile strength, plasticity, and toughness properties, and therefore, fine-grain structure could enhance these properties (Asadi et al. 2010b; Farrokhi et al. 2013; Heidarzadeh et al. 2014). Since it is difficult and time consuming to investigate experimentally the microstructure of weld, numerical simulations could be very applicable in different manufacturing processes (Liu et al. 2013; Wang et al. 2010). In terms of microstructure modeling, various approaches such as the cellular automaton (CA), the Monte Carlo model, and the phase field model have been developed to simulate microstructural evolution during processes (Liu et al. 2013). Although all these models successfully simulate microstructural evolution, most of the CA model is employed because of its length scale calibrations and straightforward time. Discrete spatial and temporal evolution of complex systems via applying local or global deterministic or probabilistic transformation rules to the location of a lattice is the main algorithm of the CA method. Many researchers have shown that CA offers a computationally efficient framework for simulation of microstructural evolution (Liu et al. 2013). Timoshenkov et al. simulated the microstructure evolution in steel using CA for thermo-mechanical treatment. Tsai et al. (2010) predicted the morphologies in the solidification process for Cu-0.6Cr (mass fraction, %) alloy and Wang et al. (2010) simulated the dynamic recrystallization (DRX) characteristic in hot compression of steel using the CA method. They stated that the CA model can simulate the nucleation and growth kinetics of dynamically recrystallized grains in hot working process. Besides these advantages, this method could not consider solely the effects of the process parameters on DRX and the relationship between the nucleation sites and the distribution of dislocation density (Liu et al. 2013). In fact, dislocation density plays a crucial role in nuc (...truncated)