Evaluation of Heat Transfer Coefficients during Quenching of Steels

Ph.D. thesis, by Hala Salman Hasan, University of Technology, Baghdad, Iraq, 2010


The control of steel quenching has been investigated in this study by developing a physically based mathematical model using the control volume method, to simulate the quenching process and to predict the time-temperature history, quench factor, and as-quenched hardness. Accurate prediction requires knowledge of the boundary conditions, and the heat transfer coefficient which is the key parameter for quenching simulations.

The heat transfer coefficients for steels as a function of temperature were obtained by developing a suitable measurement probe. Both lumped heat capacity (Biot number ≤ 0.1) and inverse heat conduction models were utilised in the design of the probe dimensions (2 mm diameter, 10 mm length). The time-temperature history was recorded using a 1 mm K-type thermocouple inserted in the geometric centre of the cylindrical probe; these data were used to calculate the heat transfer coefficient as a function of temperature. Six steel probes with different chemical composition were used to investigate the generality of the method.

A 60×20 mm steel sample was used to demonstrate the modelling technique and to assess the applicability of the calculated heat transfer coefficient to another sample with different dimensions. Good agreement was found between the results for cooling curves and hardness distribution obtained from the quenching process modelling program and the experimentally measured cooling curves and hardness.

The performance of a number of quenchants in varying conditions was also investigated, and the designed probe was used to illustrate the effect of quenching parameters (quenchant type and quenchant temperature) on cooling rate, heat-transfer coefficient, quench factor, and the estimated as-quenched hardness. The results show that the heat transfer properties are greatly affected by the quenchant parameters which influence the quench factor and the mechanical properties of the sample.

Thermal conductivity is also an important boundary condition for simulating the quenching process. A neural network model was formulated to estimate the thermal conductivity of steels as a function of temperature and chemical composition. With this model it is possible to simulate the quenching process for any steel rather than being limited to only those with available data. It is also a necessary tool to improve the design of steels and the heat treatment process.


Graduation photographs