04-07-2012, 12:05 PM
Investigation of cutting parameters of surface roughness for a non-ferrous material using artificial neural network in CNC turning
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INTRODUCTION
Now-a-days, due to the increasing demand of higher
precision components for its functional aspect, surface
roughness of a machined part plays an important role in
the modern manufacturing process. Turning is a
machining operation, which is carried out on lathe. The
quality of the surface plays a very important role in the
performance of turning as a good quality turned surface
significantly improves fatigue strength, corrosion
resistance, or creep life. Surface roughness also affects
several functional attributes of parts, such as, contact
causing surface friction, wearing, light reflection, heat
transmission, ability of distributing and holding a
lubricant, load bearing capacity, coating or resisting
fatigue.
Literature survey
Since turning is the primary operation in most of the
production processes in the industry, surface finish of
turned components has greater influence on the quality of
the product. Surface finish in turning had been found to
be influenced in varying amounts by a number of factors
such as feed rate, work material characteristics, work
hardness, unstable built-up edge, cutting speed, depth of
cut, cutting time, tool nose radius. According to these
parameters, a detailed literature survey is carried out as
follows. David et al. (2006) described an approach to
predict Surface roughness in a high speed end-milling
process and used artificial neural networks (ANN) and
statistical tools to develop different surface roughness
predictors.
PROBLEM DEFINITION
Most of the measurement techniques have limitations to
their in-process use. The purpose of the analysis is to
develop techniques to predict the surface roughness of a
part to be machined and to avoid “trial and error”
approaches to set-up turning conditions in order to
achieve the desired surface roughness. The goal of
which is to predict surface roughness (Ra) under multiple
cutting conditions determined by spindle speed, feed rate
and depth of cut. Surface roughness would be measured
directly by surface roughness measuring instruments.
Experimental results are expected to show that
parameters of spindle speed, feed rate and depth of cut
could predict surface roughness (Ra) under different
combinations of cutting parameters.
Processing units
Each unit performs a relatively simple job: Receive input
from neighbors or external sources and use this to
compute an output signal which is propagated to other
units. Apart from this processing, a second task is the
adjustment of the weights. The system is inherently
parallel in the sense that many units can carry out their
computations at the same time.
Within neural systems, it is useful to distinguish three
types of units: Input units (indicated by an index i) which
receive data from outside the neural network; output units
(indicated by an index o) which send data out of the
neural network, and hidden units (indicated by an index
h) whose input and output signals remain within the
neural network.
Network topologies
Here, the pattern of connections between the units and
the propagation of data was focused on. As for this
pattern of connections, the main distinction we can make
is between:
1. Feed-forward networks, where the data flow from input
to output units is strictly feed forward. The data
processing can extend over multiple (layers of) units, but
no feedback connections are present, that is, connections
extending from outputs of units to inputs of units in the
same layer or previous layers.