Abstract: Equal channel angular pressing (ECAP) of
commercial Al-Mg-Si alloy was conducted using two strain rates.
The ECAP processing was conducted at room temperature and at
250°C. Route A was adopted up to a total number of four passes in
the present work. Structural evolution of the aluminum alloy discs
was investigated before and after ECAP processing using optical
microscopy (OM). Following ECAP, simple compression tests and
Vicker’s hardness were performed. OM micrographs showed that, the
average grain size of the as-received Al-Mg-Si disc tends to be larger
than the size of the ECAP processed discs. Moreover, significant
difference in the grain morphologies of the as-received and processed
discs was observed. Intensity of deformation was observed via the
alignment of the Al-Mg-Si consolidated particles (grains) in the
direction of shear, which increased with increasing the number of
passes via ECAP. Increasing the number of passes up to 4 resulted in
increasing the grains aspect ratio up to ~5. It was found that the
pressing temperature has a significant influence on the
microstructure, Hv-values, and compressive strength of the processed
discs. Hardness measurements demonstrated that 1-pass resulted in
increase of Hv-value by 42% compared to that of the as-received
alloy. 4-passes of ECAP processing resulted in additional increase in
the Hv-value. A similar trend was observed for the yield and
compressive strength. Experimental data of the Hv-values
demonstrated that there is a lack of any significant dependence on the
processing strain rate.
Abstract: Design and modeling of nonlinear systems require the
knowledge of all inside acting parameters and effects. An empirical
alternative is to identify the system-s transfer function from input and
output data as a black box model. This paper presents a procedure
using least squares algorithm for the identification of a feed drive
system coefficients in time domain using a reduced model based on
windowed input and output data. The command and response of the
axis are first measured in the first 4 ms, and then least squares are
applied to predict the transfer function coefficients for this
displacement segment. From the identified coefficients, the next
command response segments are estimated. The obtained results
reveal a considerable potential of least squares method to identify the
system-s time-based coefficients and predict accurately the command
response as compared to measurements.