Prediction of microstructural evolution during hot of microstructural evolution during hot forging...

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  • Prediction of microstructural evolution during hot forging

    Fei Chen*, Zhenshan Cui, and Jun Chen

    Institute of Forming Technology and Equipment, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, PR China

    Received 31 March 2014 / Accepted 10 June 2014

    Abstract Microstructural evolution, which is governed by temperature, strain and strain rate during hot forging, is akey factor influencing mechanical properties. Understanding the microstructural evolution of metals and alloys in hotforging has a great importance for the designers of metal forming processes. The principal objective of this paper is toprovide an overview of the models for the prediction of microstructural evolution for metals and alloys during the hotforging process. In this review paper, the models are divided into four categories, including the phenomenological,physically-based, mesoscale and artificial-neural-network models, to introduce their developments, prediction capabil-ities and application scopes. Additionally, some limitations and objective suggestions for the further development of themodelling of microstructural evolution during hot forging are proposed.

    Key words: Forging, Microstructure, Recrystallization, Modelling

    1 Introduction

    Hot forging can serve to tailor the service properties of met-als through microstructure evolution, in addition to alteringtheir shape [1, 2]. On the one hand, an optimal design schemefor hot forging must ensure the required shape and size; on theother hand, the most important issue in forging is that the forg-ing scheme must meet the parts made in respect of their perfor-mance requirements such as strength and toughness as well asresistance to corrosion, etc., of high temperature parts, as wellas the high-temperature tensile properties of performance, creepresistance and thermal fatigue properties. Metallography hasindicated that grain size has a decisive effect on the abovemechanical properties of the forgings after the chemical compo-sition of the specific raw material has been determined, withregard to such as large power-station turbine rotors [3] andaeronautical blades [4]. In most metals and alloys, the grain sizeof forgings are mainly determined by forging and heat-treatingprocesses, where a fine-grained microstructure produced byforging plays a key role in obtaining a fine-grained microstruc-ture in the following heat-treatment process. Therefore, study ofmicrostructural evolution during the forging process, and fur-thermore, prediction of the grain-size distribution of forgings,have important practical significance. This has become a veryimportant consideration in the design and optimization of aforging scheme: it is also the cutting edge research in the field

    of thermal processing and significantly affects the future devel-opments in this field.

    Generally, hot forging is often classified according to theforging equipment and how the forging operation is performed,i.e. by die forging or free forging. Microstructural evolutionduring die forging is mainly dominated by the following twosteps: heating and single-hit deformation. In the heating pro-cess, the heating temperature, heating rate and holding time tre-mendously influence the grain evolution. For single-hitdeformation, dynamic recrystallization (DRX), which affectsthe grain-size distribution of the final forging for metals withmoderate-to-low stacking fault energy, plays a dominant rolein the microstructural evolution at elevated temperature. Overthe past half century, a number of research groups haveattempted to secure better understanding and controlling ofthe grain evolution for various metal and alloys during heatingand DRX, and a series of phenomenological models have alsobeen developed to predict grain evolution during heating andDRX [529]. In recent years, with the rapid development ofcomputer technology, mesosopic models have also been pro-posed to predict microstructural evolution during DRX. In dis-tinct comparison to die forging, multi-hitting and multi-heatingare the typical characteristics of free forging, such as in the pro-duction of heavy forgings, during which DRX, static recrystal-lization (SRX) and meta-dynamic recrystallization (MDRX) areimportant microstructural evolution mechanisms. The hot-deformed grains exhibit a quite different and more complexrecrystallization behavior than that for single-blow deformedgrains. In order to predict grain evolution during complex*e-mail:

    Manufacturing Rev. 2014, 1, 6 F. Chen et al., Published by EDP Sciences, 2014DOI: 10.1051/mfreview/2014006

    Available online at:

    This is an Open Access article distributed under the terms of the Creative Commons Attribution License (,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


  • recrystallization processes, many empirical, semi-theoreticaland mesocopic models for the grain evolution have been pro-posed by metallurgical and material researchers. Among thesemodels, the phenomenological model that describes the rela-tionship of austenite grain size with temperature, strain andstrain-rate have been used practically [5, 6]. At the same time,with the use of the finite-element (FE) method, simulations cou-pled with the developed phenomenological models assumed aprominent role in investigation of the processing parameterssuch as temperature, strain and strain-rate.

    The following mainly presents a critical review of the meth-ods for the prediction of microstructural evolution during thehot-forging process. Generally speaking, the methods aremainly divided into the following four categories:

    1. Phenomenological model. This expresses mathematicallythe results of observed phenomena without payingdetailed attention to their fundamental significance.In other words, the phenomenological model lacks aphysical background that accounts for experimentalobservations. Additionally, the coefficients in the modelare obtained by a regression method based on extensiveexperimental observations [542]. As a result, the modelcan only be used within a specified range, otherwise theuse of the model beyond the range of the sample datais likely to result in serious error. Therefore, it is impera-tive to update the model parameters based on the detailsof the hot-forging scheme employed.

    2. Physically-based on internal variable model. Thisaccounts for physical aspects of the material behaviour,such as the constitutive behaviour and the dynamicmicrostructural development in the hot working of metalsand alloys [4345]. According to different scale lengths(SL), grain size, volume fraction of different types ofgrain, volume fraction of phases, sub-grain size, sec-ond-phase particles and dislocation density can be usedas the mesoscale and microscale internal state variables(ISVs) [4659]. It is worth noting that the ISVs are inde-pendent of each other, and therefore the flow stress can-not serve as the ISV, because the flow stress mainlydepends on the evolution of the microstructure. Gener-ally, compared to the phenomenological descriptions,these models allow for an accurate definition of materialbehaviour under wide ranges of loading conditions bysome physical assumptions and a larger number of mate-rials constants. Most internal-variable-based constitutivemodels are completely capable of describing strain-hardening and dynamic recovery. However, one of themain disadvantages of this approach is that they are oftennot so well suited to the capture of minor alloying differ-ences, phase transformations, or recrystallization phe-nomena [46].

    3. Mesoscale modelling. According to their differences inphysical methodology and numerical algorithms, themesoscopic modelling tools are explicitly grouped intodifferent categories, e.g., the Monte Carlo (MC) model,the Cellular Automaton (CA) model, vertex models andphase-field (PF) models. These methods can not only suc-cessfully provide the general average microstructure

    properties, but can also model the evolution of the grains[60, 61]. The common characteristic of all these models isthat they try to use just one basic physical concept tosummarize the different microstructural evolution phe-nomena [62]. A representative example is the cellularautomate (CA) models, which are algorithms thatdescribe the discrete spatial and temporal evolution ofcomplex systems by applying local deterministic or prob-abilistic transformation rules to the cells of a regular (ornon-regular) lattice [63]. Mostly such models are notalways so much thermodynamically based but rather root-ing in corresponding simple local mobility and driving-force rules as well as local rules that systematically acton the neighbouring cells. The CA method of using eachcell as a little automaton makes this method relativelyefficient and fast. These features indicate that it is possi-ble to model the microstructure evolution within a unifiedframe [6466]. In contrast to the limited applicability ofthe macro-scale models, e.g., the phenomenologicalmodel and the statistical model, this characteristic ofmesoscopic models also shows the latent advantage ofthe numerical solution of the complexity of microstruc-tural evolution globally [60103], such as normal graingrowth [6876], recrystallization [8298] and phasetransformation [100103].

    4. Advanced statistical model. The artificial neural network(ANN) model is a typical statistical model [104]. Duringthe hot forging of metals and alloys, the microstructuralevolution involves complex, dynamic and often non-linear