Wire edges that pose difficulties to be machined by

Wire Electrical Discharge Machining (WEDM) is a
specialized thermal machining process capable of accurately machining parts of
hard materials with complex shapes. Parts having sharp edges that pose
difficulties to be machined by the main stream machining processes can be easily
machined by WEDM process. However, selection of process parameters for
obtaining higher cutting efficiency or accuracy in wire EDM is still not fully
solved, even with the most up-to-date CNC wire-EDM machine. This is mainly due
to the complicated stochastic process mechanisms in wire-EDM. One of the main
research fields in WEDM is related to the improvement of the process
productivity by avoiding wire breakage. Different factors can lead to wire
breakage such as a decrease in flushing pressure, inefficient removal of
erosion debris, as well as other types of stochastic phenomena that appear
during the cutting process. In such a case, the cutting process is stopped and
the wire has to be threaded again, involving an undesired waste of time.
Therefore, it would be desirable to diagnose in advance low quality cutting
regimes and consequently predict wire breakage, in order to perform an on-line
readjust of the machine parameter before it happens.

Recently,
vision systems are being exploited for such application mainly due to their
high resolution, reliability and ease of automatic processing of data. Machine
vision (MV) is the technology and method used to provide imaging-based
automatic inspection and analysis for such applications as automatic inspection,
process control and robot guidance in industry. The first step in the MV
sequence of operation is acquisition of an image, typically using cameras,
lenses, and lighting that has been designed to provide the differentiation
required by subsequent processing. MV software packages then employ various
digital image processing techniques to extract the required information and
often make decisions (such as pass/fail) based on the extracted information.

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In
spite of the large amount of work worldwide on the application of machine
vision for tool status monitoring, such system have not been implemented on
WEDM. Machine vision system can be used to park and align the electrode
precisely under the field of view of the camera for electrode status
measurement. By monitoring the electrode status, helps in avoiding unnecessary
overhauls of machines in good working orders, to detect the problem in time for
planned replacements and repairs, avoiding the breakdowns in production. The
electrode is the only element in a wire-EDM that requires frequent changes due
to failure, either by wear or breakage. Hence, there is an imperative need to
keep a watch on the condition of the electrode during the machining process, so
that the electrode can be replaced in time.

Worn
electrode state dramatically affects the texture of the machined surface.
Analyzing the texture of machined surfaces has been shown to be promising for
electrode status monitoring. However, most methods have their limitations when
applied to real environments, where the geometric features of machined surface
depend on the machining operation and where image quality is affected by
illumination and other factors. Problems of non-uniform illumination and image
noise can be reduced by applying image segmentation and image enhancement
techniques.

In
the past, many researchers have investigated the effect of the WEDM process
parameters on surface roughness and electrode wear. Cabanes, et al., 1
investigates on avoiding wire breakage and unstable situations in WEDM, as both
phenomena reduce process performance and can cause low quality components. This
work proposes a methodology that guarantees an early detection of instability
that can be used to avoid the detrimental effects associated to both unstable
machining and wire breakage. The proposed methodology establishes the
procedures to follow in order to understand the causes of wire breakage and
instability. In order to quantify the trend to instability of a given machining
situation, a set of indicators related to discharge energy, ignition delay
time, and peak current has been defined. Wire breakage risk associated to each
situation is evaluated comparing the evolution of those indicators with some
previously defined threshold values. Srinagalakshmi Nammi and Ramamoorthy, 2
have discussed on effect of surface lay in the surface roughness evaluation
using machine vision. This work explores the influence of orientation of
surface lay pattern of the machined components, while quantifying the surface
roughness using machine vision approach. The surface images are captured from
milled low carbon steel specimens with different roughness values using a
vision system with coaxial lighting arrangement at different angular
orientations of the work pieces. The captured images are subjected to
preprocessing in order to retain the frequency components that attribute to
roughness using a Gaussian filter by adapting the filtering procedures
specified in ISO 4288. Numerous image based parameters are computed from the
surface images captured at different angular positions of the work piece. The
computed vision based parameters are compared and correlated with the roughness
average (Ra) obtained using a stylus instrument and the results are analyzed.
The results clearly indicated that it is important to consider the orientation
of the work piece when the machine vision approach is used to quantify the
surface texture parameters.

Ossama
B. Abouelatta 3 has discussed on a new approach to measure surface roughness
in three dimensions by combining a light sectioning microscope and a computer
vision system. This approach has the advantages of being non-contact, fast and
cheep. A prototype version of a user interface program, currently named
SR3DVision, has been developed to manage three dimensional surface roughness
measurements. A light sectioning microscope is used to view roughness profiles
of the specimens to be measured and the vision system is used to capture images
for successive profiles. This program has been totally developed in-house using
Matlab™ software to analyze the captured images through four main modules:
(Measurement controller, Profile or surface extraction, 2D roughness parameters
calculation and 3D roughness parameters calculation). The system has been
calibrated for metric units and verified using standard specimens. In addition,
the system was used to measure various samples machined by different operations
and the results were compared with commercial software and a web-based surface
metrology algorithm testing system. The accuracy of the system was verified and
proved to be within ±4.8% compared with these systems. Ghassan A., et al, 4
have discussed about a methodology for using machine vision data to acquire
reliable surface roughness parameter measurement. Stylus-based measurements
were acquired and compared to vision-based measurements using standard and
non-standard roughness parameters. Two light reflection models namely
Intensity-Topography Compatible (ITC) model and Light-Diffuse model were
adopted and applied to interpret acquired vision data and to enable suitable
computation of roughness parameters. P.M.
George et al 5 EDM machining of carbon–carbon composite. Experiments have
been carried out to determine the optimal setting of the process parameters on
the electro-discharge machining (EDM) machine while machining carbon–carbon
composites. The parameters considered are pulse current, gap voltage and
pulse-on-time; whereas the responses are electrode wear rate (EWR) and material
removal rate (MRR). The optimal setting of the parameters are determined
through experiments planned, conducted and analysed using the Taguchi method.
It is found that the electrode wear rate reduces substantially, within the
region of experimentation, if the parameters are set at their lowest values,
while the parameters set at their highest values increase the MRR drastically.

This paper
discuss research work that analyzes images of work piece surface roughness and
electrode that have been subjected to WEDM operations and investigates the
correlation between electrode status and quality characterizing machined
surfaces. Results clearly indicate that tool status monitoring can be
successfully accomplished by analyzing surface image data.