Numerical modeling and anvil design of high-speed forging process for railway axles

Abstract

Railway axles, which are an important component of railway vehicles, are generally manufactured using high-speed forging processes. To investigate the microstructure evolution and flow behavior during forging processes, a series of isothermal hot compression and heating tests were conducted in the temperature range of 900–1200 °C and strain rate range of 0.01–20 s⁻¹. A strain-compensated Arrhenius constitutive relationship was identified for 25CrMo4 steel, and microstructure evolution kinetics, comprising dynamic recrystallization, metadynamic recrystallization, static recrystallization, and grain growth, were determined. A coupled thermomechanical–metallurgical numerical model was established for the high-speed forging of 25CrMo4 steel axles by using the TRANSVALOR Forge software package. The grain size evolution during the multipass high-speed forging process was predicted, and a full-scale axle was fabricated through high-speed forging to verify the predicted results. The predicted and experimentally observed grain sizes had good agreement. Finally, a series of geometric constraints are proposed for the design of round anvils. The reliability and applicability of the proposed constraints were validated by considering the surface quality and microstructure requirements as well as the forming force during chamfering. The results indicated that the proposed constraints can suitably guide the design of anvils for high-speed forging processes.

Appendix

According to Eq. (1), the following equation can be obtained

$$ \dot{\varepsilon}=A\cdotp {\left[\sinh \left(\alpha \sigma \right)\right]}^n\cdotp \exp \left(-\frac{Q}{R_UT}\right) $$

(31)

From Eq. (31), the following equation can be obtained:

$$ {\left[\frac{\dot{\varepsilon}\cdotp \exp \left(Q/{R}_UT\right)}{A}\right]}^{\frac{1}{n}}=\sinh \left(\alpha \sigma \right) $$

(32)

Moreover, sinh(ασ) can be expressed as follows:

$$ \sinh \left(\alpha \sigma \right)=\frac{\exp \left(\alpha \sigma \right)-\exp \left(-\alpha \sigma \right)}{2} $$

(33)

From Eq. (33) and the inverse function of Eq. (33), the following equation is obtained:

$$ \sigma =\frac{1}{\alpha}\ln \left\{\sinh \left(\alpha \sigma \right)+{\left[{\left(\sinh \left(\alpha \sigma \right)\right)}^2+1\right]}^{\frac{1}{2}}\right\} $$

(34)

By combining Eqs. (32), (34) and (2), the following equation is obtained:

$$ \sigma =\frac{1}{\alpha}\ln \left\{{\left(\frac{\dot{\varepsilon}\cdotp \exp \left(Q/{R}_UT\right)}{A}\right)}^{\frac{1}{n}}+{\left[{\left(\frac{\dot{\varepsilon}\cdotp \exp \left(Q/{R}_UT\right)}{A}\right)}^{\frac{2}{n}}+1\right]}^{0.5}\right\}=\frac{1}{\alpha}\ln \left\{{\left(\frac{Z}{A}\right)}^{\frac{1}{n}}+{\left[{\left(\frac{Z}{A}\right)}^{\frac{2}{n}}+1\right]}^{0.5}\right\} $$

(35)