Abstract: Experimental & numeral study of temperature
distribution during milling process, is important in milling quality
and tools life aspects. In the present study the milling cross-section
temperature is determined by using Artificial Neural Networks
(ANN) according to the temperature of certain points of the work
piece and the point specifications and the milling rotational speed of
the blade. In the present work, at first three-dimensional model of the
work piece is provided and then by using the Computational Heat
Transfer (CHT) simulations, temperature in different nods of the
work piece are specified in steady-state conditions. Results obtained
from CHT are used for training and testing the ANN approach. Using
reverse engineering and setting the desired x, y, z and the milling
rotational speed of the blade as input data to the network, the milling
surface temperature determined by neural network is presented as
output data. The desired points temperature for different milling
blade rotational speed are obtained experimentally and by
extrapolation method for the milling surface temperature is obtained
and a comparison is performed among the soft programming ANN,
CHT results and experimental data and it is observed that ANN soft
programming code can be used more efficiently to determine the
temperature in a milling process.
Abstract: In this paper the effect of wall waviness of side walls
in a two-dimensional wavy enclosure is numerically investigated.
Two vertical wavy walls and straight top wall are kept isothermal and
the bottom wall temperature is higher and spatially varying with
cosinusoidal temperature distribution. A computational code based on
Finite-volume approach is used to solve governing equations and
SIMPLE method is used for pressure velocity coupling. Test is
performed for several different numbers of undulations. The Prandtl
number was kept constant and the Ra number denotes that the flow is
laminar. Temperature and velocity fields are determined. Therefore,
according to the obtained results a correlation is proposed for average
Nusselt number as a function of number of side wall waves. The
results indicate that the Nusselt number is highly affected by number
of waves and increasing it decreases the wavy walls Nusselt number;
although the Nusselt number is not highly affected by surface
waviness when the number of undulations is below one.