Correlation among the Power Dissipation Efficiency, Flow Stress Course, and Activation Energy Evolution in Cr-Mo Low-Alloyed Steel.

Petr Opěla,Radek Jurča,Petr Kawulok,Ivo Schindler,Stanislav Rusz,Horymír Navrátil,Rostislav Kawulok

doi:10.3390/ma13163480

Petr Opěla, Radek Jurča + Show 5 more

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https://doi.org/10.3390/ma13163480

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Abstract

In the presented research, conventional hot processing maps superimposed over the flow stress maps or activation energy maps are utilized to study a correlation among the efficiency of power dissipation, flow stress, and activation energy evolution in the case of Cr-Mo low-alloyed steel. All maps have been assembled on the basis of two flow curve datasets. The experimental one is the result of series of uniaxial hot compression tests. The predicted one has been calculated on the basis of the subsequent approximation procedure via a well-adapted artificial neural network. It was found that both flow stress and activation energy evolution are capable of expressing changes in the studied steel caused by the hot compression deformation. A direct association with the course of power dissipation efficiency is then evident in the case of both. The connection of the presence of instability districts to the activation energy evolution, flow stress course, and power dissipation efficiency was discussed further. Based on the obtained findings it can be stated that the activation energy processing maps represent another tool for the finding of appropriate forming conditions and can be utilized as a support feature for the conventionally-used processing maps to extend their informative ability.

Highlights

Materials 2020, 13, 3480 medium carbon steel [7], medium carbon steel, microalloyed by B and Ti [8], medium-carbon bainitic steel [9], 43CrNi steel [10], high-carbon/low-carbon steel composite [11], 347H austenitic heat-resistant stainless steel [12], high-titanium Nb-micro-alloyed steel [2]; nickel alloys: Ni-based superalloy [13], P/M nickel-based superalloy [14], IN-718 superalloy [15], NiTiNb shape memory alloy [16]; aluminum alloys: as-extruded 7075 [17], 5052 alloy [1], B4 C/6061Al nanocomposites fabricated by spark plasma sintering [18]; titanium alloys: Ti-15-3 [19], Ti-6242 [20], ATI425 [21], TC21 [22]; zirconium alloys: reactor-grade alpha-zirconium [23], Zr-2.5Nb [24]; and some other alloys: AZ31-1Ca-1.5 vol% nano-alumina composite [25], Cu–Cr–Zr–Nd alloy [26], Pb-Mg-10Al-0.5B alloy [27], Fe3 Al intermetallic alloy [28], etc
Quan et al [17] have proposed to extend an experimental dataset by means of a prediction procedure since the limited number of experimental curves can lead to the inferior informative ability of compiled processing maps
The results have showed that the activation energy maps can be used as a support tool for the choice of appropriate forming conditions in cooperation with the conventional processing maps

Summary

Introduction

Since the end of the 2nd millennium, hot processing maps, introduced on the basis of the dynamic material model (DMM), have been being broadly used in the sense of the optimization of hot forming processes (forging, rolling, etc.) [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28]. It is well known that the processing map displays the distribution of power dissipation efficiency and metallurgical instability in the strain rate–temperature coordinates under the specific value of strain. The thermomechanical conditions linked with the higher efficiency of power dissipation and in the same time with the absence of metallurgical instability are usually considered as advantageous. In the case of a specific material, these maps are usually developed on the basis of an experimentally-achieved flow-curve dataset. Quan et al [17] have proposed to extend an experimental dataset by means of a prediction procedure since the limited number of experimental curves can lead to the inferior informative ability of compiled processing maps

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Conclusion