Abstract: The objective of this paper is to estimate realistic
principal extrusion process parameters by means of artificial neural
network. Conventionally, finite element analysis is used to derive
process parameters. However, the finite element analysis of the
extrusion model does not consider the manufacturing process
constraints in its modeling. Therefore, the process parameters
obtained through such an analysis remains highly theoretical.
Alternatively, process development in industrial extrusion is to a
great extent based on trial and error and often involves full-size
experiments, which are both expensive and time-consuming. The
artificial neural network-based estimation of the extrusion process
parameters prior to plant execution helps to make the actual extrusion
operation more efficient because more realistic parameters may be
obtained. And so, it bridges the gap between simulation and real
manufacturing execution system. In this work, a suitable neural
network is designed which is trained using an appropriate learning
algorithm. The network so trained is used to predict the
manufacturing process parameters.
Abstract: The purpose of this study is mainly to predict collision
frequency on the horizontal tangents combined with vertical curves
using artificial neural network methods. The proposed ANN models
are compared with existing regression models. First, the variables
that affect collision frequency were investigated. It was found that
only the annual average daily traffic, section length, access density,
the rate of vertical curvature, smaller curve radius before and after
the tangent were statistically significant according to related
combinations. Second, three statistical models (negative binomial,
zero inflated Poisson and zero inflated negative binomial) were
developed using the significant variables for three alignment
combinations. Third, ANN models are developed by applying the
same variables for each combination. The results clearly show that
the ANN models have the lowest mean square error value than those
of the statistical models. Similarly, the AIC values of the ANN
models are smaller to those of the regression models for all the
combinations. Consequently, the ANN models have better statistical
performances than statistical models for estimating collision
frequency. The ANN models presented in this paper are
recommended for evaluating the safety impacts 3D alignment
elements on horizontal tangents.